Butt J, Jenab M, Pawlita M, Tjønneland A, Kyrø C, Boutron-Ruault Mc

Schulze Mb

La Vecchia

Semi Zouiouich
email: zouiouichs@students.iarc.fr

Inge Huybrechts
email: huybrechtsi@iarc.fr

Panayiotis Louca
email: panayiotis.louca@kcl.ac.uk

Heinz Herzig
email: karl-heinz.herzig@oulu.fi

Cristina Menni
email: cristina.menni@kcl.ac.uk

Marjo-Riitta Jarvelin
email: m.jarvelin@imperial.ac.uk

Marc J Gunter
email: gunterm@iarc.fr

metabolic health and gut microbiome diversity: findings from two population-based cohort studies. Diabetologia. (Provisionally accepted -Under revision)

Keywords: colorectal cancer, screening programs, microbiome, fecal sample collection

opportunistic collection of fecal samples in fecal immunochemical test tubes after colorectal cancer screening is likely a viable method for establishing cohorts with prediagnostic fecal specimens.

Overall, this thesis presents the state-of-the-art on epidemiological evidence for the role of the microbiome in tumorigenesis, provides novel insights on the association of metabolic risk factors for colorectal cancer with the gut microbiome in population-based studies, and finally has generated important methodological data on the impact of fecal sample collection tools on microbial measurements that is needed for future epidemiological research on the microbiome and colorectal cancer, as well as other chronic diseases.

1

Abstract

The gut microbiome is the ensemble of microorganisms that inhabit the gastrointestinal tract. A dynamic host-microbiome symbiosis exists in the intestine and specific interactions between the microbiota and host metabolism and immune system are critical for shaping host physiology. Disturbance of this symbiosis has been hypothesized to play a role in the development of various chronic diseases, including colorectal cancer. Indeed, there is growing experimental evidence that the gut microbiome potentially influences tumor development through dysregulation of host metabolism and immune function. However, epidemiological data linking the gut microbiome with colorectal carcinogenesis remains limited as very few existing cohorts have collected fecal samples. Colorectal cancer screening programs, in which millions of stool samples are collected each year, might provide rich opportunities to establish population-based cohorts with repeated, prospectively collected samples.

The first part of the thesis systematically summarizes the current epidemiological literature that has been published in the past decade on the association of the human microbiome with cancer. Our findings emphasised that for most microbiome indicators, the evidence was still too weak to draw firm conclusions in relation to their role in cancer.

The second part of the thesis investigates the association between insulin resistance and inflammation -recognised colorectal cancer risk factors -and the gut microbiome in two population-based cohorts -the Northern Finland Birth Cohort and TwinsUK. Our study indicated that higher levels of insulin resistance and other markers of metabolic dysfunction were associated with lower microbiome diversity in both cohorts, even after control for obesity and other factors.

Finally, the third part of the thesis evaluates microbiome stability and accuracy in fecal samples collected using different methods employed in ongoing colorectal cancer screening programs. Our findings suggest that commonly used fecal sample collections such as fecal immunotests and paper-based collection cards are, in general, suitable media for microbiome measurements though storage factors such as ambient temperature can impact on stability for some methods. In addition, the

Résumé

Le microbiome intestinal désigne l'ensemble des micro-organismes présents dans le tractus gastro-intestinal. Une symbiose dynamique entre l'hôte et le microbiome existe dans l'intestin et des interactions spécifiques entre le microbiome et le métabolisme de l'hôte, et son système immunitaire, sont essentielles pour façonner la physiologie de l'hôte. L'hypothèse que la perturbation de cette symbiose joue un rôle dans le développement de diverses maladies chroniques, y compris du cancer colorectal, a été émise. En effet, il existe de plus en plus de preuves expérimentales selon lesquelles le microbiome intestinal influence potentiellement le développement de tumeurs par le biais d'une dérégulation du métabolisme de l'hôte et de sa fonction immunitaire. Cependant, les données épidémiologiques reliant le microbiome intestinal à la carcinogenèse colorectale restent limitées car très peu de cohortes existantes ont collecté des échantillons fécaux. Les programmes de dépistage du cancer colorectal, dans lesquels des millions d'échantillons de selles sont prélevés chaque année, pourraient fournir de riches opportunités pour établir des cohortes basées sur la population avec des échantillons répétés et prospectivement collectés.

La première partie de la thèse résume de manière systématique la littérature épidémiologique actuelle qui a été publiée au cours de la dernière décennie sur l'association entre le microbiome et le cancer. Nos résultats ont souligné que pour la plupart des indicateurs du microbiome, les preuves étaient encore trop faibles pour tirer des conclusions définitives concernant leur rôle dans le cancer.

La deuxième partie de la thèse étudie l'association entre la résistance à l'insuline et l'inflammation -des facteurs de risque reconnus du cancer colorectal -et le microbiome intestinal, dans deux cohortes populationnelles : The Northern Finland Birth Cohort et TwinsUK. Notre étude a indiqué que des niveaux plus élevés de résistance à l'insuline et d'autres marqueurs de dysfonctionnement métabolique étaient associés à une diversité amoindrie du microbiome dans les deux cohortes, même après contrôle de l'obésité et d'autres facteurs.

Enfin, la troisième partie de la thèse évalue la stabilité et la concordance du microbiome dans les échantillons fécaux prélevés à l'aide de différentes méthodes utilisées dans les programmes de dépistage du cancer colorectal en cours. Nos résultats suggèrent que les collections d'échantillons fécaux couramment utilisées, telles que les tests immunochimiques fécaux et les cartes de prélèvement sur papier, sont en général des supports appropriés pour mesurer la diversité microbiome, bien que des facteurs de stockage, tels que la température ambiante, puissent avoir un impact sur la stabilité de certaines méthodes. En outre, la collecte opportuniste d'échantillons fécaux dans des tubes à tests immunochimiques fécaux après le dépistage du cancer colorectal est probablement une méthode viable pour établir des cohortes avec des échantillons fécaux pré-diagnostics.

Dans l'ensemble, cette thèse présente l'état de l'art sur les preuves épidémiologiques du rôle du microbiome dans la tumorigenèse. Elle fournit de nouvelles informations sur l'association des facteurs de risque métaboliques du cancer colorectal avec le microbiome intestinal dans les études populationnelles. Enfin, elle a généré d'importantes données méthodologiques sur l'impact des outils de collecte d'échantillons fécaux sur les mesures microbiennes nécessaires pour les futures recherches épidémiologiques sur le microbiome et le cancer colorectal, ainsi que sur d'autres maladies chroniques.

Résumé substantiel

Le microbiome intestinal désigne l'ensemble des micro-organismes, incluant les bactéries commensales et pathogènes, les virus, les archées et les mycètes, ainsi que leur matériel génétique présents dans le tractus gastro-intestinal. Le microbiome intestinal possède de nombreuses fonctions métaboliques, telles que la production d'acides gras à chaîne courte via la fermentation de polysaccharides complexes, la déconjugaison d'acides biliaires, la synthèse de lipopolysaccharides spécifiques et la biosynthèse de certains acides aminés essentiels et vitamines. Une symbiose dynamique entre l'hôte et le microbiome existe dans l'intestin et des interactions spécifiques, telles que la digestion d'aliments et l'extraction de nutriments ou le métabolisme de certains médicaments, entre le microbiome et le métabolisme de l'hôte, et son système immunitaire, sont essentielles pour façonner la physiologie de l'hôte. Cependant, cette symbiose repose sur un équilibre fragile, et des perturbations des interactions entre l'hôte et le microbiome intestinal ont été associées à plusieurs maladies chroniques telles que l'obésité, le diabète et potentiellement différents types de cancer.

Le cancer colorectal est le troisième cancer le plus fréquent chez les hommes et le deuxième chez les femmes, et est également la deuxième cause de décès par cancer dans le monde chez les deux sexes combinés. Bien que les facteurs génétiques, tel que les antécédents familiaux de cancer colorectal, ainsi que l'inflammation chronique induite par les maladies inflammatoires de l'intestin, contribuent au développement du cancer colorectal, il existe des preuves indiquant que de nombreux facteurs modifiables liés à l'alimentation et au mode de vie affectent le risque de cancer colorectal. De plus en plus d'indices suggèrent que le microbiome intestinal influence potentiellement l'initiation et la progression du développement tumoral par le biais d'une dérégulation du métabolisme de l'hôte et de sa fonction immunitaire, ainsi que par des perturbations des processus homéostatiques favorisant l'inflammation chronique et l'instabilité du génome induisant des mutations.

Le lien potentiel entre le microbiome intestinal et la néoplasie colorectale représente une hypothèse prometteuse pour étudier les caractéristiques inexpliquées du cancer colorectal. Cependant, les données épidémiologiques reliant le microbiome intestinal à la carcinogenèse colorectale restent limitées car très peu de cohortes existantes ont collecté des échantillons fécaux. Les données basées sur des études cas-témoins ne permettent pas de déterminer si le processus de cancérogenèse modifie l'environnement local et crée une nouvelle niche pour les micro-organismes, ou si les altérations de la population microbienne contribuent à la cancérogenèse. Des échantillons répétés, collectés prospectivement à partir d'études de cohortes populationnelles, sont donc nécessaires pour une meilleure compréhension de la temporalité des associations microbiennes avec le cancer colorectal. Les programmes de dépistage du cancer colorectal, dans lesquels des millions d'échantillons de selles sont prélevés chaque année, pourraient fournir de riches opportunités pour établir des cohortes basées sur la population avec des échantillons répétés et collectés prospectivement.

L'objectif de cette thèse est de contribuer à déchiffrer les associations supposées entre le microbiome intestinal et le développement du cancer colorectal en renforçant les preuves épidémiologiques sur la relation entre le microbiome intestinal et des facteurs de risque établis de cancer colorectal, notamment la résistance à l'insuline et l'inflammation chronique, ainsi qu'en évaluant des méthodes de collection d'échantillons de matières fécales utilisées dans les programmes de dépistage.

La première partie de la thèse résume de manière systématique la littérature épidémiologique actuelle qui a été publiée au cours de la dernière décennie sur l'association entre le microbiome et le cancer. En effet, si certaines revues et métaanalyses portant sur les profils de microbiome en lien avec des cancers spécifiques ont déjà été publiées, aucun aperçu systématique des associations les plus significatives entre la composition du microbiome (par exemple les taxons et la diversité) et les différents sites de cancer n'a été publié à ce jour. En raison de la diversité des données publiées, un aperçu des preuves épidémiologiques disponibles liant le microbiome au cancer est nécessaire.

Nos résultats indiquent que bien que des preuves soient disponibles pour le lien entre certains taxons du microbiome intestinal et le risque de cancer colorectal, et pour le microbiome buccal et le risque de cancer buccal, pour la plupart des indicateurs du microbiome, les preuves sont encore trop faibles pour tirer des conclusions définitives concernant leur rôle dans le cancer. La plupart des preuves nécessitent une validation dans des études prospectives basées sur la population.

La deuxième partie de la thèse étudie l'association entre la résistance à l'insuline et l'inflammation -des facteurs de risque reconnus du cancer colorectal -et le microbiome intestinal, dans deux cohortes populationnelles : The Northern Finland Birth Cohort et TwinsUK. Alors que l'hypothèse selon laquelle le microbiome intestinal influence le développement de la résistance à l'insuline a été émise, les données humaines sur la résistance à l'insuline et d'autres paramètres métaboliques et le microbiome intestinal sont encore limitées, car très peu de cohortes ont collecté des échantillons sanguins et fécaux. La résistance à l'insuline est une cause potentielle du diabète et d'autres maladies métaboliques, et constitue un facteur de risque établi de cancer colorectal. Par conséquent, déchiffrer la physiopathologie de la résistance à l'insuline à l'aide de données populationnelles pourrait aider à comprendre les mécanismes de développement du cancer colorectal et à renforcer les preuves de stratégies préventives potentielles.

Notre étude a indiqué que des niveaux plus élevés de résistance à l'insuline et d'autres marqueurs de dysfonctionnement métabolique étaient associés à une diversité amoindrie du microbiome dans les deux cohortes, même après contrôle de l'obésité et d'autres facteurs. Cependant, en raison de la nature transversale de cette étude, il est impossible de conclure si ces différences dans la composition du microbiome et les associations de taxons sont une cause ou une conséquence d'un dysfonctionnement métabolique.

Enfin, la troisième partie de la thèse évalue la stabilité et la concordance du microbiome dans les échantillons fécaux prélevés à l'aide de différentes méthodes utilisées dans les programmes de dépistage du cancer colorectal en cours dans différents pays. En effet, pour potentiellement conseiller l'établissement d'études internationales ayant des échantillons fécaux répétés et collectés prospectivement basés sur des programmes de dépistage du cancer colorectal, un travail méthodologique est nécessaire pour tester les tubes de test immunochimique fécal et les cartes de prélèvement d'échantillons utilisées dans les pays où différents dispositifs de dépistage sont utilisés.

Nos résultats suggèrent que les collections d'échantillons fécaux couramment utilisées, telles que les tests immunochimiques fécaux et les cartes de prélèvement sur papier, sont en général des supports appropriés pour mesurer la diversité microbiome, bien que des facteurs de stockage, tels que la température ambiante, puissent avoir un impact sur la stabilité de certaines méthodes. En outre, la collecte opportuniste d'échantillons fécaux dans des tubes à tests immunochimiques fécaux après le dépistage du cancer colorectal est probablement une méthode viable pour établir des cohortes avec des échantillons fécaux pré-diagnostics.

Dans l'ensemble, cette thèse présente l'état de l'art sur les preuves épidémiologiques du rôle du microbiome dans la tumorigenèse. Elle fournit de nouvelles informations sur l'association des facteurs de risque métaboliques du cancer colorectal avec le microbiome intestinal dans les études populationnelles. Enfin, elle a généré d'importantes données méthodologiques sur l'impact des outils de collecte d'échantillons fécaux sur les mesures microbiennes nécessaires pour les futures recherches épidémiologiques sur le microbiome et le cancer colorectal, ainsi que sur d'autres maladies chroniques. Gunter MJ, Leclerc M (2020) Comparison of fecal sample collection methods for microbial analysis embedded within colorectal cancer screening programs. mSphere. The rapid evolution of the microbiomics field has been accompanied by the introduction of a wide vocabulary used to describe different aspects of these communities and their environments. In this chapter of the thesis, key terms, such as microbiota, microbiome, taxonomy, 16S ribosomal RNA and diversity will be defined.

Table of contents

Human microbiota

The human body, including mucosal and skin environments, is not only composed of somatic cells but also of a multitude of microbial cells derived from commensal, symbiotic and pathogenic bacteria, viruses, archaea and fungi [START_REF] Sender | Are We Really Vastly Outnumbered? Revisiting the Ratio of Bacterial to Host Cells in Humans[END_REF]. Together, these microorganisms form a complex bacterial ecosystem termed the human microbiota.

Gut microbiota

The human gut microbiota is the microbial ecosystem of the gastrointestinal digestive tract and is among the most complex of the body sites, including 500 to 1,000 different species [START_REF] Turnbaugh | The Human Microbiome Project[END_REF][START_REF] Locey | Scaling laws predict global microbial diversity[END_REF]. In most scientific publications, the gut microbiota refers to the bacterial population present in the digestive tract, without taking into account other microorganisms (i.e. viruses, archaea and fungi).

Gut microbiome

These trillions of microorganisms interact with their host through the expression of several millions of genes, which together comprise the human microbiome [START_REF] Hooper | Commensal host-bacterial relationships in the gut[END_REF]. Each bacterial strain has a genome containing thousands of genes, offering more genetic diversity than the human genome [START_REF] Turnbaugh | The Human Microbiome Project[END_REF]. A rough approximation of 1,000 bacterial species in the gut with 2,000 genes per species yields an estimate of 2,000,000 genes, 100 times the number of approximately 20,000 genes composing the human genome [START_REF] Qin | A human gut microbial gene catalogue established by metagenomic sequencing[END_REF][START_REF]The Integrative Human Microbiome Project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease[END_REF].

In many studies, the terms "microbiome" and "microbiota" are used interchangeably and are often synonymous. Even if these definitions are overlapping, small but relevant differences exist [START_REF] Marchesi | The vocabulary of microbiome research: a proposal[END_REF] (Table 1).

Table 1. Differences between three major terms of the vocabulary of microbiome research

Microbiome

Microbiota Metagenome The microorganisms, their genomes and the surrounding environmental conditions

The microorganisms present in a defined environment The collection of genomes and genes from the members of a microbiota Refers to the bacteria (primary) and their genes (secondary)

Refers to the taxonomy of microorganisms

Refers to the collective functions of microbial genes

Bacterial taxonomy

Bacterial taxonomy is the rank-based classification of bacteria. A taxon groups together different organisms to form a unit. The current bacterial classification includes domain, phyla, class, order, family, genus and species. The species constitutes the basic unit in the classification of living organisms [START_REF] Brenner | Classification of Procaryotic Organisms and the Concept of Bacterial Speciation. In: Bergey's Manual of Systematics of Archaea and Bacteria[END_REF]. Nowadays, the most generally applied method for determining phylogenetic relationships between microorganisms is based on metagenomics, as high-throughput sequencing technologies have seen a rapid development in recent years. The purpose of metagenomics is to quantify the DNA of a multitude of species in a particular ecosystem at once, allowing the identification of non-cultivable bacterial species by standard methods in laboratories [START_REF] Amann | Phylogenetic identification and in situ detection of individual microbial cells without cultivation[END_REF][START_REF] Hugenholtz | Impact of Culture-Independent Studies on the Emerging Phylogenetic View of Bacterial Diversity[END_REF]. Two sequencing approaches are used today: targeted metagenomics, consisting in amplifying and sequencing a single gene present in several species [START_REF] Suenaga | Targeted metagenomics: a high-resolution metagenomics approach for specific gene clusters in complex microbial communities[END_REF], and shotgun metagenomics, consisting in sequencing all the DNA from all organisms in a sample [START_REF] Quince | Shotgun metagenomics, from sampling to analysis[END_REF]. Comparative analysis of the 16S ribosomal RNA (or 16S rRNA) gene sequences is particularly common for the study of the gut microbiome [START_REF] Weisburg | 16S ribosomal DNA amplification for phylogenetic study[END_REF].

16S ribosomal RNA

16S rRNA is part of the 30S small subunit of the ribosome present in every prokaryotic cell. The gene encoding for the subunit,16S rRNA gene, and its sequence is approximately 1,550 base pairs long [START_REF] Clarridge | Impact of 16S rRNA Gene Sequence Analysis for Identification of Bacteria on Clinical Microbiology and Infectious Diseases[END_REF]. The 16S rRNA gene is used as a phylogenetic marker as it is highly conserved between different species of bacteria and archaea [START_REF] Coenye | Intragenomic heterogeneity between multiple 16S ribosomal RNA operons in sequenced bacterial genomes[END_REF]. In addition to highly conserved primer binding sites, 16S rRNA gene sequences contain hypervariable regions that can provide species-specific signature sequences useful for identification of bacteria ( 16) (Figure 1).

Figure 1. Schematic representation of ribosome complex and 16S rRNA gene

From Fukuda et al, 2016 (17) Different DNA-based techniques, such as quantitative PCR (qPCR) or 16S rRNA sequencing, are widely used in microbiology studies to identify diversity in bacteria.

When analysing 16S rRNA using qPCR, a universal primer is applied to the sample.

The partial 16S gene amplicons that result from this primer contain the hypervariable regions, which is compared to other known sequences through sequence alignment.

The result helps deduce phylogenetic relationships between bacteria. qPCR is a quantitative technique with the advantage of being fast, specific and precise for the detection of minor populations of bacteria [START_REF] Postollec | Recent advances in quantitative PCR (qPCR) applications in food microbiology[END_REF]. However, the main disadvantage of qPCR is that the primers are designed to only quantify a selection of specific bacterial strains.

16S rRNA sequencing has been widely used for identification and taxonomic classification of bacterial species. 16s rRNA sequencing refers to sequencing amplicons of the 16s rRNA gene. While sequencing the entire 16s rRNA gene is difficult due to the read length restrictions of many next generation sequencing platforms, sequencing one or more hypervariable regions is relatively rapid and cheap [START_REF] Janda | 16S rRNA Gene Sequencing for Bacterial Identification in the Diagnostic Laboratory: Pluses, Perils, and Pitfalls[END_REF]. In contrast to qPCR, 16S rRNA sequencing does not only focus on specific bacterial strains but rather on the complex bacterial community. Using 16S rRNA gene sequences, numerous bacterial genera and species have been reclassified and renamed, classification of uncultivable bacteria has been made possible, phylogenetic relationships have been determined, and the discovery and classification of novel bacterial strains has been facilitated [START_REF] Woo | Then and now: use of 16S rDNA gene sequencing for bacterial identification and discovery of novel bacteria in clinical microbiology laboratories[END_REF]. However, although 16S rRNA gene sequencing is highly useful in regard to bacterial classification, it has low phylogenetic power at the species level and poor discriminatory power for some genera [START_REF] Mignard | 16S rRNA sequencing in routine bacterial identification: a 30month experiment[END_REF].

Operational taxonomic units (OTUs) are clusters of similar sequence variants of the 16S rRNA gene sequences [START_REF] Blaxter | Defining operational taxonomic units using DNA barcode data[END_REF]. Each of these clusters are intended to represent a taxonomic unit of a bacteria species depending on the sequence similarity threshold.

In past years, using a 97% identity cut-off was a standard approach to distinguish bacteria at the genus level. Recent approaches are now focused towards amplicon sequence variants (ASVs), focusing on minor differences (in many cases single nucleotide variations) to identify unique bacterial species [START_REF] Callahan | DADA2: High-resolution sample inference from Illumina amplicon data[END_REF][START_REF] Callahan | Exact sequence variants should replace operational taxonomic units in marker-gene data analysis[END_REF].

Microbial diversity

Changes in the microbiome composition and population diversity, is typically measured with taxonomic diversity. The taxonomic diversity is defined by two components: the richness which describes how many different types of bacteria are observed in a sample, in terms of their taxonomic ranking and the evenness, which designates how evenly these bacteria are distributed [START_REF] Stirling | Empirical Relationships between Species Richness, Evenness, and Proportional Diversity[END_REF].

To measure biodiversity, two concepts are generally used in microbiome studies: alpha and beta diversity [START_REF] Whittaker | Evolution and Measurement of Species Diversity[END_REF]. The alpha diversity refers to the diversity within a single sample and is usually expressed by the number of species in that ecosystem. The beta diversity shows the difference between microbial communities and generally focuses on taxonomic abundance profiles from different samples.

Even though there is no general agreement on which diversity index is the best to use, several diversity indices have been commonly used to study microbial diversity (Table 2). For alpha diversity, these metrics are the Shannon diversity index, inverse Simpson index, and the number of observed OTUs/ASVs. For beta diversity, these metrics include Bray-Curtis dissimilarity, Jaccard index and UniFrac.

Table 2. Ecological diversity measures commonly used in microbiome studies

Diversity indices

Description References

Shannon diversity index

Based on species richness and species evenness: more weight on species richness ( 27)

Inverse Simpson index

Based on species richness and species evenness: more weight on species evenness [START_REF] Lemos | Rethinking microbial diversity analysis in the high throughput sequencing era[END_REF][START_REF] Simpson | Measurement of Diversity[END_REF] Observed

OTUs/ASVs

Based on species richness: number of species present in a community [START_REF] Hughes | Counting the Uncountable: Statistical Approaches to Estimating Microbial Diversity[END_REF][START_REF] Gotelli | Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness[END_REF])

Bray-Curtis dissimilarity

Based on abundance: differences in microbial abundances between two samples (31)

Jaccard index

Based on presence or absence of species: differences in microbial composition between two samples (32)

UniFrac

Based on sequence distances: fraction of branch length shared between two samples or unique to one or the other sample ( 33)

Composition of the human gut microbiota

Composition at high taxonomic levels shows the overall stability of the gut microbiota between individuals. In healthy individuals, intestinal microbiota are typically dominated, at the phyla level, by Gram-negative Bacteroidetes and Gram-positive

Firmicutes, with a smaller abundance of Actinobacteria [START_REF] Irrazábal | The multifaceted role of the intestinal microbiota in colon cancer[END_REF][START_REF] Bevins | Paneth cells, antimicrobial peptides and maintenance of intestinal homeostasis[END_REF]. However, the proportion of these phyla is not static, and different bacterial strains may compete to fulfil distinct ecological niches, inducing considerable inter-individual variation between phenotypically similar hosts. Therefore, each individual harbours radically different complexity and diversity of bacterial community in the digestive tract, making each gut microbiota unique [START_REF] Grice | Topographical and Temporal Diversity of the Human Skin Microbiome[END_REF][START_REF] Lloyd-Price | Strains, functions and dynamics in the expanded Human Microbiome Project[END_REF].

Our understanding of what leads to inter-individual variation in diversity remains limited, even if several studies tend to show that a number of host (e.g. genetics, age) and lifestyle factors (e.g. diet, obesity, medications, mode of delivery at birth) could influence the composition of the gut microbiota through the life-course, potentially to the benefit or detriment of the host [START_REF] Yatsunenko | Human gut microbiome viewed across age and geography[END_REF][START_REF] O'keefe | Fat, fibre and cancer risk in African Americans and rural Africans[END_REF][START_REF] Rothschild | Environment dominates over host genetics in shaping human gut microbiota[END_REF][START_REF] Vila | Impact of commonly used drugs on the composition and metabolic function of the gut microbiota[END_REF][START_REF] Collado | Factors influencing gastrointestinal tract and microbiota immune interaction in preterm infants[END_REF] (Figure 2).

Figure 2. Evolution of the gut microbiome over the human life-course

Adapted from Dong & Gupta, 2019 (43), SCFA: short-chain fatty acid, IBS:

inflammatory bowel syndrome Typically-consumed diets, such as the Western diet and the Mediterranean diet, can have a significant impact on the gut microbiome [START_REF] Cresci | Gut Microbiome: What We Do and Don't Know[END_REF]. The Western diet, characterised by high-fat, high-sugar, high level of red and processed meat, high levels of refined grains and a lower level of fibre, was associated to a decrease in overall bacterial richness/diversity, and beneficial Bifidobacteria and Eubacteria [START_REF] Dong | Influence of Early Life, Diet, and the Environment on the Microbiome[END_REF][START_REF] Singh | Influence of diet on the gut microbiome and implications for human health[END_REF]. This alteration of the gut microbiome was associated with gut barrier degradation, reduced levels of short-chain fatty acids (SCFAs), higher levels of lipopolysaccharides (LPS) and inflammation [START_REF] Desai | A Dietary Fiber-Deprived Gut Microbiota Degrades the Colonic Mucus Barrier and Enhances Pathogen Susceptibility[END_REF][START_REF] Turnbaugh | The Effect of Diet on the Human Gut Microbiome: A Metagenomic Analysis in Humanized Gnotobiotic Mice[END_REF]. In comparison, the Mediterranean diet, characterised by a beneficial fatty acid profile, higher intake of fibre, vegetables, and fruits, and with lower intake of sugar and red meat, was associated to increased bacterial richness/diversity, higher levels of SCFAs and decreased inflammation [START_REF] Dong | Influence of Early Life, Diet, and the Environment on the Microbiome[END_REF][START_REF] Makki | The Impact of Dietary Fiber on Gut Microbiota in Host Health and Disease[END_REF]. Consumption of dietary fibre increases bacterial richness and diversity with a relative abundance in such genera as Prevotella and Treponema, and enhance the production of SCFAs [START_REF] O'keefe | Fat, fibre and cancer risk in African Americans and rural Africans[END_REF][START_REF] Sonnenburg | Starving our microbial self: the deleterious consequences of a diet deficient in microbiota-accessible carbohydrates[END_REF].

In addition to diet and other early life events [START_REF] Tamburini | The microbiome in early life: implications for health outcomes[END_REF], such as the mode of delivery at birth and breast-feeding, there are many other lifestyle exposures throughout adulthood that can lead to changes in microbial composition. Certain medications, such as antibiotics and proton pump inhibitors, can trigger gut microbiome imbalances which can persist over time and increase the host's susceptibility to infection and metabolic syndrome [START_REF] Dethlefsen | Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation[END_REF][START_REF] Francino | Antibiotics and the Human Gut Microbiome: Dysbioses and Accumulation of Resistances[END_REF]. Smoking has also been associated to changes in gut microbiota composition, as shown by strong microbial alterations before and after smoking cessation [START_REF] Biedermann | Smoking cessation alters intestinal microbiota: insights from quantitative investigations on human fecal samples using FISH[END_REF]. More recently, in addition to the previously mentioned factors that are relatively well described in the literature, it has been shown that alcohol consumption and bowel movement quality might have a very strong impact on the gut microbiome [START_REF] Falony | Population-level analysis of gut microbiome variation[END_REF][START_REF] Roy | Red Wine Consumption Associated With Increased Gut Microbiota α-Diversity in 3 Independent Cohorts[END_REF][START_REF] Vujkovic-Cvijin | Host variables confound gut microbiota studies of human disease[END_REF].

Most importantly, in contrast to other dietary factors influencing the gut microbiome, these two factors have also been shown to differ when comparing diseased subjects to healthy individuals and to influence disease associations by confounding the effects of disease [START_REF] Vujkovic-Cvijin | Host variables confound gut microbiota studies of human disease[END_REF].

Gut microbiome metabolism

The gut microbiome is highly metabolically active and helps define essential physiological functions such as digestion of food and extraction of nutrients [START_REF] Rowland | Gut microbiota functions: metabolism of nutrients and other food components[END_REF], drug metabolism [START_REF] Zimmermann | Mapping human microbiome drug metabolism by gut bacteria and their genes[END_REF], and modification of the host immune response and metabolism [START_REF] Brestoff | Commensal bacteria at the interface of host metabolism and the immune system[END_REF].

Interestingly, microbial diversity between individuals does not appear to critically influence core functions in microbial metabolism, including the production of SCFAs via fermentation of complex polysaccharides, deconjugation of bile acids, synthesis of specific LPS and the biosynthesis of some essential amino acids and vitamins [START_REF] Qin | A human gut microbial gene catalogue established by metagenomic sequencing[END_REF][START_REF] Huttenhower | Structure, function and diversity of the healthy human microbiome[END_REF][START_REF] Flint | The role of the gut microbiota in nutrition and health[END_REF][START_REF] Shafquat | Functional and phylogenetic assembly of microbial communities in the human microbiome[END_REF].

Short-chain fatty acids

Since the human body lacks the digestive enzymes required to digest many dietary plant polysaccharides and oligosaccharides, these dietary fibres are metabolically processed by the gut microbiota [START_REF] Hinsberger | Digestion and absorption[END_REF][START_REF] Bäckhed | Host-Bacterial Mutualism in the Human Intestine[END_REF]. Bacterial fermentation of dietary nondigestible polysaccharides in the human colon produces SCFAs, largely composed of acetate, propionate and butyrate [START_REF] Koh | From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites[END_REF][START_REF] Ríos-Covián | Intestinal Short Chain Fatty Acids and their Link with Diet and Human Health[END_REF]. These end products of bacterial fermentation are produced by different bacteria in the colon. For instance, butyrate is produced by several bacteria from the phyla Firmicutes such as Faecalibacterium prausnitzii, or Roseburia spp [START_REF]A Sense of Community[END_REF].

SCFAs play a major role in the maintenance of normal physiology in the host including the stimulation of water absorption, the production of energy for intestinal epithelial cell growth, and can either be absorbed from the colon or bind to free fatty acid receptors which are expressed in the intestinal epithelium [START_REF] Cummings | Role of intestinal bacteria in nutrient metabolism[END_REF][START_REF] Ulven | Short-chain free fatty acid receptors FFA2/GPR43 and FFA3/GPR41 as new potential therapeutic targets[END_REF][START_REF] Kimura | The SCFA Receptor GPR43 and Energy Metabolism[END_REF]. More specifically, butyrate is an important source of energy for colonic epithelial cells, propionate is utilized in the liver as a substrate for the production of glucose via gluconeogenesis, and acetate, once transformed into acetyl-CoA, is an important biochemical intermediate in human metabolism [START_REF] Cummings | Role of intestinal bacteria in nutrient metabolism[END_REF][START_REF] Roberfroid | Prebiotic effects: metabolic and health benefits[END_REF].

Bile acids

Bile acids represent the primary pathway for cholesterol catabolism in the liver and they primarily promote absorption of lipids, including fat-soluble vitamins in the intestine [START_REF] Staels | Bile Acids and Metabolic Regulation[END_REF]. Deconjugation of primary bile acids by the gut microbiota results in the formation of secondary bile acids, including deoxycholic and lithocholic acids, derived from cholic acid and chenodeoxycholic acid, respectively [START_REF] Lefebvre | Role of bile acids and bile acid receptors in metabolic regulation[END_REF].

These bioconversions modulate the signalling properties of bile acids through the nuclear farnesoid X receptor and the G protein-coupled membrane receptor 5, which regulate diverse metabolic pathways in relation to glucose and lipid metabolism in the host [START_REF] Swann | Systemic gut microbial modulation of bile acid metabolism in host tissue compartments[END_REF]. More precisely, secondary bile acids regulate the hepatic triglyceride pool via nuclear farnesoid X receptor-dependent inhibition of sterol regulatory element binding protein-1, therefore inhibiting lipogenesis [START_REF] Watanabe | Bile acids lower triglyceride levels via a pathway involving FXR, SHP, and SREBP-1c[END_REF]. Secondary bile acids also stimulate the secretion of the incretin glucagon like peptide-1 through binding to the G protein-coupled membrane receptor 5 on enteroendocrine cells, thus improving insulin sensitivity, attenuating weight gain and increasing energy expenses [START_REF] Thomas | TGR5-mediated bile acid sensing controls glucose homeostasis[END_REF].

Lipopolysaccharide

LPS is the major component of the outer layer of the membrane of Gram-negative bacteria and is, in non-capsulated strains, exposed on the cell surface [START_REF] Zhang | On the essentiality of lipopolysaccharide to Gramnegative bacteria[END_REF]. Distinct structures of LPS can be found on the surface of many pathogenic bacteria (e.g.

Escherichia coli, Salmonella, Yersinia pestis) and therefore, have been termed endotoxin in reference to the cell-associated toxicity. LPS performs several functions in Gram-negative bacteria such as serving as a major structural component of the outer membrane, transforming the outer membrane into an effective permeability barrier against small, hydrophobic molecules and most importantly [START_REF] Nikaido | Molecular basis of bacterial outer membrane permeability revisited[END_REF], playing a crucial role in bacteria-host interactions by modulating responses by the host immune system. Gut microbial LPS is thought to be one of the most important mediators of the microbiome's influence on host physiology, notably because chronic immune cell activation is, in part, caused by the LPS-mediated stimulation of toll-like receptor 4 [START_REF] Vatanen | Variation in Microbiome LPS Immunogenicity Contributes to Autoimmunity in Humans[END_REF]. However, our understanding of the mechanisms that support host-microbe interactions through LPS is still limited. While many assume that proinflammatory signalling by LPS is a central feature of the gut microbiome activity, others consider that proinflammatory pathways commonly triggered by bacterial pathogens upon interaction with the host are, in fact, actively repressed by beneficial bacteria of the gut microbiome to maintain homeostasis in the gut [START_REF] Hennezel | Total Lipopolysaccharide from the Human Gut Microbiome Silences Toll-Like Receptor Signaling[END_REF][START_REF] Yoshida | A possible beneficial effect of Bacteroides on faecal lipopolysaccharide activity and cardiovascular diseases[END_REF].

Amino acids

The gut microbiota is also involved in the digestion and metabolism of endogenous and exogenously-derived amino acids. Amino acids are among the major nutrients in the diet and regulate energy and protein homeostasis in organisms [START_REF] Bergen | Intestinal nitrogen recycling and utilization in health and disease[END_REF][START_REF] Metges | Contribution of microbial amino acids to amino acid homeostasis of the host[END_REF]. Some functional amino acids, such as tryptophan, glutamine, methionine, and branched chain amino acids, also have beneficial effects on the immune system through modulation of key metabolic signalling pathways [START_REF] Ma | Dietary Amino Acids and the Gut-Microbiome-Immune Axis: Physiological Metabolism and Therapeutic Prospects[END_REF]. Along the gastrointestinal tract, alimentary and endogenous proteins are hydrolysed into peptides and amino acids by host-and bacteria-derived proteases and peptidases [START_REF] Macfarlane | Contribution of the microflora to proteolysis in the human large intestine[END_REF]. A number of microorganisms, such as bacteria belonging to Clostridium and Streptococcus, are particularly involved in amino acid fermentation in the large intestine [START_REF] Dai | Amino acid metabolism in intestinal bacteria: links between gut ecology and host health[END_REF].

Amino acid-fermenting bacteria are not only important for protein digestion and subsequent amino acid absorption, but they also secrete various metabolites, characterised by nitrogen and sulfur containing material. The products are, sometimes, detrimental to the health of the host [START_REF] Blachier | Effects of amino acid-derived luminal metabolites on the colonic epithelium and physiopathological consequences[END_REF] but may also influence cellular signalling pathways and exert immune and barrier effects and thus, maintain homeostasis of the host [START_REF] Metges | Contribution of microbial amino acids to amino acid homeostasis of the host[END_REF]. After being metabolised by microorganisms, amino acids can also be used to synthesise constituents, such as SCFAs or other bacterial proteins [START_REF] Libao-Mercado | Dietary and endogenous amino acids are the main contributors to microbial protein in the upper gut of normally nourished pigs[END_REF]. Additionally, the gut bacteria can be involved in de novo biosynthesis of certain amino acids, suggesting that the exchange of amino acids between the host and the gut microbiota is bidirectional [START_REF] Gill | Metagenomic analysis of the human distal gut microbiome[END_REF].

Gut microbiome in insulin resistance, diabetes and cancer

Elaborate mechanisms relying on host-driven tolerance are required to maintain the host-microbe symbiosis in the intestine, including physical barriers of the oriented gut tissues or secretion of antimicrobial peptides and secreted antibodies [START_REF] Rossi | Host-recognition of pathogens and commensals in the mammalian intestine[END_REF]. However, this symbiosis is based on a dynamic and fragile equilibrium, and disturbances of the interactions between the host and the gut microbiome have been associated with several chronic diseases such as obesity [START_REF] Turnbaugh | An obesityassociated gut microbiome with increased capacity for energy harvest[END_REF][START_REF] Turnbaugh | A core gut microbiome in obese and lean twins[END_REF], diabetes [START_REF] Qin | A metagenome-wide association study of gut microbiota in type 2 diabetes[END_REF][START_REF] Larsen | Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults[END_REF] and potentially different types of cancer [START_REF] Ahn | Human gut microbiome and risk for colorectal cancer[END_REF][START_REF] Fernández | Breast Cancer and Its Relationship with the Microbiota[END_REF].

Gut microbiome in insulin resistance and metabolic syndrome

Type 2 diabetes (T2D) has reached global epidemic proportions and is recognised as a major cause of morbidity and mortality [START_REF] Zheng | Global aetiology and epidemiology of type 2 diabetes mellitus and its complications[END_REF]. Insulin resistance, characterised as the reduced sensitivity of glucose storage tissues to insulin-mediated biologic activity, is a condition that precedes the development of T2D [START_REF] Taylor | Insulin Resistance and Type 2 Diabetes[END_REF], and often accompanies obesity, though not exclusively. Indeed, individuals with insulin resistance are not systematically overweight or obese [START_REF] Kahn | Mechanisms linking obesity to insulin resistance and type 2 diabetes[END_REF] and the existence of metabolically-healthy obese and metabolically unhealthy normal-weight individuals has been described in previous studies (100). Insulin resistance is also related to poor glucose control and high levels of chronic inflammation, which together, comprise elements of the metabolic syndrome (101). The incidence of metabolic syndrome often parallels the incidence of obesity and incidence of T2D, and it has been estimated that more than 20% of adults worldwide are known to have metabolic syndrome (102). However, the prevalence of metabolic syndrome varies depending on environmental factors, the age and gender composition of the population, genetic differences, and lifestyle factors including physical activity level and eating habits (103,104). Despite the wellestablished epidemiological associations between metabolic syndrome and its related metabolic parameters (e.g. insulin resistance) and chronic diseases, the biological mechanisms underlying its development remain to be deciphered.

Insulin and insulin-like growth factor-1 mediate their biological effects via the insulin and insulin-like growth factor-1 receptors. Cellular actions of insulin and insulin-like growth factor-1 involve a wide variety of effects on post-receptor signalling pathways within target cells (105). These signalling pathways contain several points of regulation, signal divergence, and cross talk with other signalling cascades that are essential to mediate the variety of insulin and insulin-like growth factor-1 biological responses (106). Mechanisms of insulin resistance are therefore multifactorial. In most cases, insulin resistance is caused by cellular perturbations, such as lipotoxicity, inflammation, glucotoxicity, mitochondrial dysfunction, and endoplasmic reticulum stress (105). These potential complex mechanisms lead to deregulation of genes and inhibitory protein modifications and result in impaired insulin and insulin-like growth factor-1 action (Table 3).

Previous studies have suggested that the gut microbiome might influence host metabolic health through several pathways that are directly or indirectly related to insulin resistance development [START_REF] Qin | A metagenome-wide association study of gut microbiota in type 2 diabetes[END_REF][START_REF] Larsen | Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults[END_REF]107,108). These pathways included energy extraction, intestinal barrier integrity, metabolism of bile acids and host metabolic and signalling pathways (109). Alterations of microbial composition and functionality in T2D patients compared to healthy participants have been described [START_REF] Qin | A metagenome-wide association study of gut microbiota in type 2 diabetes[END_REF][START_REF] Larsen | Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults[END_REF]110). For example, circulating levels of branched-chain amino acids have been positively associated with insulin resistance (111) and correlate with specific changes in gut microbiome composition (e.g. P. copri and B. vulgatus) and functions of individuals with insulin resistance (112). In addition, low bacterial diversity, characterised by the reduction in butyrate-producing bacteria (e.g. Roseburia and Faecalibacterium prausnitzii) and the increase in mucin-degrading bacteria, was associated with impaired gut integrity causing low-grade inflammation of visceral adipose tissue through endotoxemia (107,113,114) and may provide a link between obesity and insulin resistance (115). If animal and human studies have identified relevant differences in intestinal microbiota composition in subjects with insulin resistance related chronic diseases, the magnitude of effect of the gut microbiome on metabolic functions remains to be proven (116).

Decreased expression of insulinsignalling molecules

Hyperglycaemia

Glycation of insulin-signalling molecules

Reduced affinity for insulin receptor Decreased DNA-binding capacities of transcription factors Hyperactivation of protein phosphatase protein phosphatase 2A

Reduced phosphorylation of insulin receptor and insulinsignalling molecules

Hyperinsulinemia

Hyperactivation of leucine-rich repeat protein phosphatases-1 and Grb14

Decreased Akt Ser 473 phosphorylation Competition for insulin receptor substrate binding to insulin receptor Insulin resistance and subsequent hyperinsulinemia and T2D are conditions which are associated with increased risk of cancer at a number of anatomical sites (117). For example, metabolic changes such as hyperinsulinemia and insulin resistance are associated with colorectal cancer (118)(119)(120). The mechanisms linking insulin resistance and metabolic dysfunction with colorectal cancer are not fully understood but may reflect enhanced signalling through insulin receptors and insulin-like growth factors (i.e. insulin-like growth factor-1 and 2) and signal transduction to pro-mitogenic and anti-apoptotic pathways that enable cell proliferation and survival of transformed cells (121). Insulin stimulates cell proliferation by binding with low affinity to the insulinlike growth factor-1 receptor, inducing high serum levels of insulin-like growth factor-1 and increasing risk of cancer, including colorectal cancer (122,123). Furthermore, some cancer cells promote tumor proliferation by locally producing insulin-like growth factor-2 which binds with high affinity to foetal isoforms of the insulin receptor in tumor cells (124). Since insulin resistance, T2D and obesity are known cancer-promoting conditions, understanding the relationship between the gut microbiome and metabolic syndrome might help to understand the mechanisms of cancer development and open new possibilities for preventive strategies as well as screening, diagnosis, and treatment (125).

Role of the gut microbiome in cancer

There is growing evidence that the gut microbiota potentially influence the initiation and progression of tumor development (126,127). Since disturbances of the hostmicrobiome homeostasis have been associated with many established hallmarks of cancer (128), the gut microbiome likely influences cancer risk at different levels, including cancer initiation, promotion, dissemination, and response to therapy (129).

Broadly, these hallmarks comprise dysregulations of host metabolism [START_REF] Qin | A metagenome-wide association study of gut microbiota in type 2 diabetes[END_REF]107,108) and immune function [START_REF] Brestoff | Commensal bacteria at the interface of host metabolism and the immune system[END_REF][START_REF] Vatanen | Variation in Microbiome LPS Immunogenicity Contributes to Autoimmunity in Humans[END_REF], perturbations of homeostatic processes promoting chronic inflammation (130-132) and promotion of genome instability and mutations (133,134) (Figure 3).

Colorectal cancer

Although recent studies have implicated the gut microbiota in the development of cancers at distal sites (135,136), the earliest observations linking the microbiota with the hallmarks of cancer have primarily focused on colorectal cancer as the most complex and dense populations of endogenous microorganisms are found within the gastrointestinal tract.

Colorectal cancer: pathophysiology, epidemiology and etiology

Pathophysiology

Colorectal cancer develops through four main stages: initiation, promotion progression and metastasis (137). Briefly, initiation involves irreversible genetic damage to target cells that allow neoplastic transformations occur. The initiated cells proliferate during the promotion stage, inducing abnormal growth. During the progression phase, through the gradual accumulation of genetic and epigenetic alterations, benign tumour cells transform into malignant cancer cells and acquire aggressive characteristics and metastatic potential. Finally, metastasis is marked by the spread of cancer cells from the primary organ to other organs or tissues through the bloodstream or the lymphatic system. The duration of each phase is difficult to estimate but these mechanisms generally take a long time and decades may be required for all stages to be completed in colorectal cancer (138).

Colorectal cancer occurs through at least three distinct carcinogenic pathways (139).

The adenoma-carcinoma sequence pathway accounts for 85 to 90% of sporadic colorectal cancer. Normal cells progress to small adenoma, to large adenoma and, finally, to cancer. This pathway is predominantly associated with chromosomal instability (140). In addition, the serrated pathway accounts for 10 to 15% of sporadic colorectal cancer. This model is characterised by the progression from normal cells, to hyperplastic polyp, to sessile serrated adenomas and, finally, to cancer (141). This pathway is predominantly associated with CpG island methylator phenotype (142).

Finally, a twofold increased risk of colorectal cancer was previously observed in patients with inflammatory bowel disease (143). Due to the low incidence of inflammatory bowel disease, the inflammatory pathway explains less than 2% of all colorectal cancers. Driven by chronic inflammation, normal cells progress to indefinite dysplasia, to low-grade dysplasia, to high-grade dysplasia and, finally, to cancer (144).

Epidemiology

Worldwide, colorectal cancer is the third most common cancer in men and the second most common cancer in women, and is also the second leading cause of cancerrelated death in the world among both sexes combined (145). Over the next 15 years, the number of cases of colorectal cancer is expected to increase by 72% to more than 3 million (146). While the incidence of colorectal cancer in many countries with a high human development index has decreased among those older than 50 years, a recent study established that rates of colorectal cancer in individuals younger than 50 years increased by up to 4% per year (147). In addition, in many countries that underwent major development transitions, particularly in Eastern Europe, Asia and South America, increases in both colorectal cancer incidence and mortality have been observed (146,148). The rise in incidence in countries in transition and the generational changes may reflect the role of secular changes in colorectal cancer risk factors that have influenced incidence rates.

Etiology

Although genetic factors, including family history of colorectal cancer and hereditary cancer syndromes (143,149), as well as chronic inflammation induced by inflammatory bowel diseases, contribute to colorectal cancer development, there is strong evidence that many modifiable dietary and lifestyle factors affect colorectal cancer risk (Table 4) (150,151). These factors include obesity, low physical activity and sedentary lifestyle, poor diets (such as higher consumption of red and processed meat, high fat, low fibre, low whole grain and low calcium), smoking tobacco and excessive alcohol consumption (152-154).

Gut microbiome and colorectal cancer

Advances in characterising the gut microbiome have offered potential insights into the etiology of colorectal cancer. In fecal samples collected within a case-control study, colorectal cancer risk was positively associated with decreased bacterial diversity with enrichment of Bacteroidetes and depletion of Firmicutes in colorectal cancer cases compared to non-cancer controls [START_REF] Ahn | Human gut microbiome and risk for colorectal cancer[END_REF]. In another case-control study, the composition of the gut microbiota was investigated in colorectal cancer precursors and revealed differences in the relative distribution of eight phyla between adenomatous and control colorectal tissues (164). Accumulating evidence supports the likelihood that the collective activity of the gut microbiota might influence colorectal cancer development and progression (165,166). However, over the past decade, several species have garnered specific interest for the potential roles in colorectal carcinogenesis.

Data from in vitro and murine models have demonstrated that Fusobacterium nucleatum might promote colorectal cancer cell proliferation and increase tumor growth rates (167). Several mechanisms have been proposed to explain these findings (Figure 4). For all the aforementioned examples, it is apparent that variation in the gut microbiota and potential link with colorectal neoplasia might represent a promising hypothesis that could offer insights to unexplained features of the epidemiology of colorectal cancer.

However, it remains unclear whether bacterial features of the gut microbiota have a causative role in colorectal carcinogenesis and due to several limitations, evidence on the potential mechanisms involved are still too weak to draw definitive conclusions.

Colorectal cancer screening and gut microbiome

As described above, colorectal cancers typically develop from normal epithelium through sequential phases of adenomatous dysplasia over a period that can span Indeed, epidemiological studies of the association between the gut microbiome and colorectal cancer development have so far been very limited, usually with small sample sizes and case-control and other cross-sectional designs without repeated measurements. Thus, due to potential reverse causality, caution is required in the interpretation of colorectal cancer-microbiome associations (187). In data based on case-control studies, it is not possible to determine whether the carcinogenic process changes the local environment and creates a new niche for microorganisms or whether alterations in the microbial population contribute to carcinogenesis. Repeated, prospectively collected samples from population-based cohort studies are necessary for better understanding of the temporal nature of microbial associations with colorectal cancer.

Thesis aim and objectives

The main aim of this thesis was to contribute to unravelling the putative associations of the gut microbiome with colorectal cancer development by strengthening epidemiologic evidence on the relationship between the gut microbiome and established colorectal cancer risk factors, including insulin resistance and chronic inflammation, and by evaluating fecal sample collection methods used in ongoing screening programs with a view to providing the necessary evidence for establishing future cohorts with prediagnostic fecal samples. To address this over-arching aim, this thesis has been divided into three objectives.

The human microbiome in relation to cancer risk

The first part of the thesis aimed to summarize the current epidemiological literature that has been published in the past decade on the association of the microbiome with cancer. Indeed, while some reviews and meta-analyses focusing on microbiome profiles for single cancer sites have already been published, no systematic overview of the most significant associations between microbiome composition (e.g. taxa and diversity) and different cancer sites was published to date. Due to the diversity of published data, an overview of available epidemiologic evidence linking the microbiome with cancer was needed.

Objective I:

Evaluate the strengths and weaknesses of the current evidence on the relationship between the human microbiome and cancer in epidemiologic studies.

Markers of metabolic health and gut microbiome diversity

The second part of the thesis aimed to investigate the association between insulin

Objective II:

Investigate the association of gut bacteria 16S rRNA gene sequence data with serologic levels of homeostatic model for insulin resistance, glycated haemoglobin and C-reactive protein in two distinct population-based cohorts, the Northern Finland Birth Cohort 1966 and TwinsUK.

Comparison of fecal sample collection methods for microbial analysis

The third part of the thesis evaluated microbiome stability and accuracy in fecal samples collected using different methods employed in ongoing colorectal cancer screening programs. Indeed, to potentially inform the establishment of international studies with repeated, prospectively collected fecal specimens based in colorectal cancer screening programs, methodologic work was needed to test FIT and specimen collection cards used in countries where different screening devices are employed.

Objective III:

Compare the impact of different fecal collection methods currently used in ongoing colorectal cancer screening programs on microbiome technical reproducibility, accuracy and stability.

Chapter II: The human microbiome in relation to cancer risk: a systematic review of epidemiological studies

Background

The gut microbiome has potentially been associated with cancer development (128) but due to the diversity of published data, the exact mechanisms of this relationship are not clear (187). A large number of individual observational studies that have examined the association of the microbiome with single cancer types have been published in the past decade, however, no systematic overview of the most significant associations between microbiome composition and diversity, and different cancer sites was published to date.

Objective

The objective of this study was to evaluate the strengths and weaknesses of the current evidence on the relationship between the human microbiome and cancer in epidemiologic studies.

Methods

Inclusion and exclusion criteria

A systematic review was conducted to identify peer-reviewed publications related to the human microbiome and cancer. Observational studies (i.e. case-control, including case-crossover design or cohort studies) and randomized controlled trials were considered for inclusion. Reviews, case reports, and other studies without a comparison group, as well as in vitro and animal studies were excluded. Adult humans with or without any type of cancer were included while underage persons (<18 years)

were excluded. Studies investigating the prevalence or incidence of cancer and its association with the human microbiome were considered for inclusion. Studies investigating only precancerous lesions/conditions (no malignant conditions) were excluded. Studies focusing on single bacteria instead of communities of microorganisms were excluded.

Data collection and analysis

Tailored search strings (see Supplementary Material Table S1 in annex) were used to search for relevant articles from database conception until December 2019 via the PubMed and EMBASE databases. Studies were reviewed and selected independently by four reviewers. Significant differences in relative abundance of taxa and/or diversity measures between cancer cases and controls were extracted and reported. If multiple analyses were run with different levels of adjustment, then the one with maximal adjustment for confounders was chosen. The methodologic quality of the included articles was assessed independently by two reviewers using the Newcastle-Ottawa quality assessment scale ( 188). An association was considered as "strong" when three or more publications reported a statistically significant association in the same direction and none were in the opposite direction. The association was deemed "suggestive"

when two publications reported a statistically significant association in the same direction and none in the opposite direction.

Results

A total of 124 articles (including 15,764 subjects in total; 7,652 cancer cases vs. 8,112 controls) were included and used for this review. Among all the anatomical sites evaluated, the gut microbiome followed by the oral microbiome were most frequently studied. The majority of articles reported specific differences in microbiome diversity and/or composition between cases and control groups (including not only non-cancer patients but sometimes tissues). Fifty studies reported differences in gut microbiome composition between colorectal cancer patients and various controls. Overall, consistent findings showed that abundance of pro-inflammatory opportunistic taxa, such as Fusobacterium, Parvimonas and Porphyromonas, was increased in colorectal cancer patients while a depletion of butyrate-producing bacteria was observed. Sixteen studies described differences in oral microbiome composition between oral cancer patients and controls. Enrichment of Fusobacterium and depletion of Streptococcus were reported in patients with oral cancer compared with controls. The quality assessment demonstrated rather low quality for the "ascertainment of the exposure of interest" because studies often used different methods of ascertainment for cases and controls, for example, resection for cases while biopsy for controls. The large diversity of parameters used to describe the microbial composition made it impossible to harmonize the different studies in a way that would allow meta-analysis.

Conclusion

Although strong evidence was available for certain taxa of the gut microbiome and colorectal cancer risk, and for the oral microbiome and oral cancer risk, for most of the microbiome taxa/indicators the evidence was still too weak to draw firm conclusions in relation to their role in cancer. Most findings now require validation in population-based prospective studies in which standardised methods are applied.

Scientific article

Objective

The aim of this study was to investigate the association of gut bacteria 16S rRNA gene sequence data with serologic levels of homeostatic model for insulin resistance (HOMA-IR), glycated haemoglobin (HbA1c) and C-reactive protein (CRP) in two distinct population-based cohorts, the Northern Finland Birth Cohort 1966 (NFBC) and TwinsUK.

Methods

Study design and samples

This study was carried out among 506 participants from the NFBC and 1,140 participants from TwinsUK. In the NFBC, at age 46 years, anthropometric measures, including body mass index (BMI), and lifestyle information were collected using selfadministered questionnaires and clinical examinations. All the metabolic markers were measured during clinical examination after an overnight fasting period of 12 hours.

Additionally, at 46 years of age, the participants were asked to collect a fecal sample at home. In TwinsUK, height and weight were measured during each participant's annual clinic visit, allowing BMI to be calculated. Smoking status was obtained by questionnaires. Glucose and insulin levels were measured for all individuals after a 10 hours overnight fast. TwinsUK participants were also asked to collect a fecal sample at home and bring it to the clinical research facility. In both cohorts, HOMA-IR was calculated based on fasting insulin and fasting glucose levels. The gut microbiome was characterized using the same procedure in both cohorts. DNA was extracted using MO-BIO PowerSoil® kit, and the V4 region of the 16S rRNA gene was amplified and sequenced using the MiSeq. The sequence quality control was performed with QIIME2 and DADA2, and diversity metrics calculated using R.

Statistical analysis

We estimated associations between gut microbiome diversity metrics and serologic levels of HOMA-IR, glycated haemoglobin HbA1c and C-reactive protein CRP using multivariable linear regression models adjusted for gender, smoking status and BMI.

We used general linear models with a quasi-Poisson distribution and Microbiome

Regression-based Kernel Association Tests (MiRKAT) to estimate associations of metabolic parameters with alpha-and beta-diversity metrics, respectively, and generalized additive models for location scale and shape (GAMLSS) fitted with the zero-inflated beta distribution to identify taxa associated with the metabolic markers.

Results

Overall, increased levels of HOMA-IR and CRP were associated with decreasing richness or diversity of microbiome composition in the two cohorts. Most importantly, the relationship between high levels of metabolic parameters and lower microbiome diversity was still significant, even after adjustment for BMI. In addition, in the NFBC, the association of BMI and microbiome diversity was attenuated when adjusting the model for insulin resistance. Finally, the beta component of the zero-inflated beta regression model indicated associations between HOMA-IR, CRP and HbA1c with several bacteria at the genus level.

Conclusion

Our findings indicate, in two distinct population-based cohorts, that individuals with worse metabolic control have lower gut microbial diversity and may provide insight into the pathophysiological mechanisms that underlie poor metabolic health. However, due to the cross-sectional nature of this study, it is impossible to conclude if these differences in microbial composition and taxa associations are a cause or a consequence of metabolic dysfunction. fitted with the zero-inflated beta distribution to identify taxa associated with the metabolic markers. Associations between gut microbiome diversity and HOMA-IR and CRP were also investigated in 1,140 adult participants from TwinsUK.

Results: In the NFBC1966, alpha diversity was significantly lower in individuals with higher HOMA-IR with an average of 76.5 (standard deviation [SD] 18.9) sequence variants (ASVs) for the first quartile of HOMA-IR and 65.9 (SD 18.4) for the fourth quartile of HOMA-IR. Alpha-diversity was also lower with higher HbA1c (number of ASVs and Shannon's diversity, P-values <0.001 and =0.003 respectively) and higher CRP (number of ASVs, P-values=0.025), even after control for BMI and other potential confounders. In TwinsUK, alpha-diversity measures were also significantly lower among participants with higher measures of HOMA-IR and CRP. When considering beta-diversity, we found that microbial community profiles were associated with HOMA-IR in the NFBC1966 and TwinsUK, using multivariate MiRKAT models, with binomial deviance dissimilarity P values of <0.001.

Conclusions/interpretation: Overall, higher levels of HOMA-IR, CRP and HbA1c

were associated with statistically significantly lower microbiome diversity in both the NFBC1966 and TwinsUK, even after control for BMI and other parameters. These results from two distinct population-based cohorts provide evidence that individuals with worse metabolic control have lower gut microbial diversity and may provide insight into the pathophysiological mechanisms that underlie poor metabolic health.

Key words: Insulin resistance, metabolic health, HOMA-IR, fecal microbiome

Research in context

What is already known about this subject?

Insulin resistance lies on the causal pathways of diabetes and other metabolic diseases, however, the pathophysiological origins of insulin resistance are not fully understood.

The gut microbiome has been hypothesized to influence insulin resistance development. However, human data on insulin resistance and the gut microbiome are limited as very few population-based cohorts have collected both blood and fecal specimens.

What is the key question?

What is the relationship between the gut microbiome and serologic measures of metabolic health in population-based studies?

What are the new findings?

Higher levels of insulin resistance and other markers of metabolic dysfunction were associated with lower richness and/or evenness of microbiome composition in two population-based cohort studies, even after control for obesity.

How might this impact on clinical practice in the foreseeable future?

Modification of the gut microbiota among individuals with worsening insulin senstivity and metabolic control may offer a potential preventive strategy for Type 2 diabetes.

Introduction

Obesity and Type 2 diabetes (T2D) have reached global epidemic proportions and are recognised as major causes of morbidity and mortality [START_REF] Zheng | Global aetiology and epidemiology of type 2 diabetes mellitus and its complications[END_REF]189). Insulin resistance is a pathophysiological condition that precedes the development of T2D [START_REF] Taylor | Insulin Resistance and Type 2 Diabetes[END_REF], however its aetiology is not fully understood. Obesity is a recognised risk factor for insulin resistance but not all insulin resistant individuals are overweight or obese [START_REF] Kahn | Mechanisms linking obesity to insulin resistance and type 2 diabetes[END_REF]; indeed, the existence of metabolically-healthy obese and metabolically unhealthy normalweight individuals has been described in previous studies (100). Despite the wellestablished epidemiological links between insulin resistance, its related parameters (i.e. poor glucose control and high levels of inflammation), and chronic diseases including obesity and T2D, the potential role of the gut microbiome in the development of insulin resistance and T2D is not fully understood.

Previous studies have suggested a link between the gut microbiome and metabolic health [START_REF] Larsen | Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults[END_REF]107) and in particular, have described differences in microbial composition and functionality in T2D patients compared to healthy participants, suggesting that the microbiome may play a key role in the pathogenesis of the disease [START_REF] Qin | A metagenome-wide association study of gut microbiota in type 2 diabetes[END_REF][START_REF] Larsen | Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults[END_REF] Bioinformatics: Bioinformatics processing was performed using QIIME 2 2017.8 (197).

Sequences were demultiplexed, and quality control on forward reads was performed with DADA2 [START_REF] Callahan | DADA2: High-resolution sample inference from Illumina amplicon data[END_REF]. Paired end reads were not joined, because shorter 16S rRNA gene sequences would be dropped because they cannot be joined with 150 base reads, resulting in systematic bias in community composition. Taxonomy was assigned to amplicon sequence variants (ASVs) using q2 feature classifier (198) and the Greengenes 13_8 reference database (199). A phylogenetic tree was built by aligning ASVs with MAFFT (200), filtering highly variable positions via q2 alignment, and applying FastTree (201) to construct an unrooted tree, followed by midpoint rooting using q2 phylogeny midpoint-root. Diversity metrics (i.e Shannon index, observed sequence variants, binomial deviance dissimilarity, Jaccard, weighted and unweighted UniFrac) were computed using the "vegan" (202) and "microbiome" packages in R

(

Results

As presented in Table 1, the majority of NFBC1966 participants were female, and 29.2%, 45.9% and 24.9% were normal weight, overweight and obese, respectively.

The TwinsUK cohort was entirely female and older compared to the NFBC1966 (mean age 62.9 and 46.6, respectively). In the NFBC1966, 31.9% of currents smokers were obese against 16.5% in TwinsUK. In the NFBC1966, BMI was strongly correlated with HOMA-IR (R=0. [START_REF] Bäckhed | Host-Bacterial Mutualism in the Human Intestine[END_REF]95%CI [0.58;0.68]) and more moderately correlated with CRP (R=0. [START_REF] Lloyd-Price | Strains, functions and dynamics in the expanded Human Microbiome Project[END_REF]95%CI [0.29;0.44]) and HbA1c (R=0. [START_REF] Callahan | DADA2: High-resolution sample inference from Illumina amplicon data[END_REF]95%CI [0.14;0.31]) (Figure S1).

Compared to NFBC1966, in TwinsUK, BMI was moderately correlated with HOMA-IR (R=0. [START_REF] Dong | Influence of Early Life, Diet, and the Environment on the Microbiome[END_REF]95%CI [0.38;0.47]) and CRP (R=0. [START_REF] Lloyd-Price | Strains, functions and dynamics in the expanded Human Microbiome Project[END_REF]95%CI [0.31;0.41]).

Prior to conducting multivariable analyses, variance inflation factors (VIF) were calculated to evaluate potential multicollinearity between alpha diversity metrics, BMI and HOMA; moderate VIF were found with these two covariates (VIF BMI 1.37, VIF HOMA-IR 1.43), suggesting that these predictors are not correlated with other variables. In NFBC1966 multivariable models that adjusted for BMI, gender and smoking status, alpha diversity was significantly reduced with increasing quartiles of HOMA-IR (Table 2). For example, Shannon's diversity was lower in the 4 th quartile of HOMA-IR (2.59,95%CI [2.50;2.68]) compared to the 1 st quartile (2.80,95%CI [2.71;2.89]). Similar results were found in the TwinsUK cohort where Shannon's diversity was significantly lower in the 4 th quartile of HOMA-IR (3.72,95%CI [3.65;3.79]) compared to the 1 st quartile (3.91,95%CI [3.84;3.98]). Quasi-Poisson models in NFBC1966 showed significant inverse associations of alpha diversity with numerical HOMA-IR, CRP and HbA1c levels (Table 2). In the TwinsUK cohort, Shannon's diversity was significantly reduced with increasing HOMA-IR (Estimate=-0.062, P-value<0.001) and CRP (Estimate=-0.018, P-value= 0.031) as well as the number of observed ASVs. In the NFBC1966, in multivariable models including numerical HOMA-IR or CRP and BMI, the association between BMI and the number of observed ASVs was no longer significant after adjustment (P-value without HOMA-IR: 0.001, P-value in model including HOMA-IR: 0.489, P-value in model including CRP: 0.082) (Table S1). In multivariable models including HbA1c and BMI, the association between BMI and the number of observed ASVs remained significant. In the TwinsUK cohort, BMI remained strongly inversely associated with alpha diversity, regardless of the presence of HOMA-IR or CRP in the model (Table S1).

In the NFBC1966, using the first three PCoA vectors from four beta diversity matrices, no visual clustering was observed by measures of metabolic health (results not shown). In the NFBC1966, when considering community composition using measures of beta-diversity, significant differences were detected by numerical HOMA-IR, CRP and HbA1c for three measures (e.g. binomial, Jaccard, Unweighted UniFrac) in adjusted MiRKAT models (Table 3); however, no association was seen for weighted UniFrac. In the TwinsUK cohort, similar results were found with significant differences detected by numerical HOMA-IR for three measures (e.g. binomial, Jaccard, Unweighted UniFrac) but not for weighted UniFrac. However, by numerical CRP, a significant difference was observed only for Jaccard. In MiRKAT models including BMI with adjustment only for gender and smoking status in the NFBC1966 and for age and smoking status in TwinsUK, the associations with BMI were significant for binomial, Jaccard and unweighted UniFrac in the NFBC1966 and for the four measures in TwinsUK (Table S2). In the NFBC1966, after inclusion of HOMA-IR in the models, the associations between BMI and microbial composition for binomial and unweighted UniFrac metrics were were no longer statistically significant. In the TwinsUK cohort, BMI remained strongly associated with four beta diversity matrices, regardless of the inclusion of HOMA-IR or CRP in the model (Table S2).

In GAMLSS models adjusted for BMI, gender and smoking status, sixteen individual genera met FDR-adjusted statistical significance with HOMA-IR, ten with CRP and nine with HbA1c in the NFBC1966. Higher values of HOMA-IR were strongly associated with higher average relative abundances of members from the Peptococcaceae (Estimate=11. S3a). Increased CRP was associated with lower average relative abundances of members from the Paraprevotellaceae, Peptococcaceae, Veillonellaceae and Peptostreptococcaceae families and higher average relative abundance of one ASVs, Peptococcus (Table S3b). Higher levels of HbA1c were associated with lower average relative abundances of members from the Bifidobacteriaceae, Peptococcaceae, Veillonellaceae and Peptostreptococcaceae families and higher average relative abundance of one ASVs, Oxalobacter (Table S3c).

For the presence/absence of taxa, the zero models accounting for excess zeros showed that the metabolic biomarkers-taxa associations were never significant except for the genus Gardneralla and HbA1c (Estimate=3.87, P-value=0.013).

Although the NFBC1966 results presented in this paper include both men and women, sensitivity analyses were run including women only (n=319) with similar results (data not shown).

Discussion

In this analysis of gut microbiome profiles and metabolic parameters in two populationbased cohorts, as levels of HOMA-IR, CRP and HbA1c increased, we observed decreasing richness and/or evenness of the gut microbial taxa, even after adjustment for BMI. In addition, HOMA-IR, CRP and HbA1c were associated with microbial community as indicated by significant associations with three beta-diversity measures, but were not associated with weighted UniFrac, suggesting that there were differences in community composition when ignoring phylogenetic similarity, but when this was accounted for, the communities were quite similar. Further, HOMA-IR, CRP and HbA1c

were significantly associated with the relative abundance of specific taxa, including members of the Peptococcaceae, Bifidobacteriaceae and Veillonellaceae families, but not with their presence or absence. In the NFBC1966, we observed that when including HOMA-IR or CRP in the models, the association between BMI and some measures of alpha-and beta-diversity lost statistical significance. These findings support a potential relationship between insulin resistance and markers of metabolic health and gut microbial diversity that may be independent of BMI.

Our data support the hypothesis that greater gut microbiome diversity is associated with better insulin sensitivity (107). A clinical trial studying the effect of fecal transplant from lean donors to men diagnosed with metabolic syndrome found that insulin sensitivity had improved and gut bacterial diversity had increased 6 weeks post transplantation ( 209). These results corroborate other studies identifying gut microbial alterations and attenuation of metabolic syndrome after various weight-loss interventions (210,211). Perturbation of homeostatic interactions between the gut microbiome and the host might promote metabolic disturbances but the mechanisms underlying this relationship are not well understood (212) (Figure 1). As described previously, the gut microbiome can trigger inflammatory processes associated with obesity and insulin resistance by stimulating immune cells through lipopolysaccharides derived from bacterial membranes (213,214). Furthermore, microbial-derived shortchain fatty acids, including butyrate (215), can enhance insulin sensitivity (216) and suppress insulin-mediated fat accumulation (217). CRP is a marker of chronic lowgrade systemic inflammation associated with obesity and insulin resistance (218).

Previous studies have found a relationship between systematic inflammation, through high-sensitivity CRP plasma levels, and microbial composition, supporting our findings (219). Our results suggest that higher levels of HbA1c were associated with a decreased bacterial diversity. Results from the literature seem less clear regarding the association between HbA1c level and gut microbiome composition. Some articles reported evidence of an association between HbA1c level and bacterial group counts (220) while others did not find any significant relationship (221). These findings could be explained by the fact that elevated insulin levels or HOMA-IR appeared to identify certain traits of metabolic syndrome changes, especially abdominal obesity, earlier than both HbA1c and measures of glucose (222).

Consistent with other studies, BMI was inversely associated with gut microbiome diversity (193). However, following adjustment for HOMA-IR and CRP, this association lost statistical significance with some measures of alpha-and beta-diversity, suggesting that the observed relationship between BMI and gut microbiome diversity might be indirect and influenced by measures of insulin resistance and inflammation.

This hypothesis needs to be further explored in other cohorts as results from TwinsUK suggested that associations between BMI and gut microbial diversity were attenuated but remained strongly significant.

Sixteen individual taxa met FDR-adjusted statistical significance with HOMA-IR, ten with CRP and nine with HbA1c. In particular, higher HOMA-IR was associated with higher average relative abundances of members from the Peptococcaceae, Bifidobacteriaceae (genus Gardnerella) and Veillonellaceae families while higher CRP levels were associated with lower relative abundances of Paraprevotellaceae, Peptococcaceae and Peptostreptococcaceae. Higher levels of HbA1c were associated with higher presence of the family Bifidobacteriaceae (genus Gardnerella). In a previous study, Schott et al. found that the relative abundance of certain proinflammatory bacteria, including members from the Peptococcaceae (rc4-4 sp genus)

and Peptostreptococcaceae, was higher in obese mice (223). Our findings support an association between HOMA-IR and Peptococcaceae rc4-4 sp but also show differences in CRP-taxa associations. Insulin-resistant individuals had a significantly higher abundance of Veillonellaceae, which support our findings (224). Relative abundance of succinate-producing Veillonellaceae was also found to be higher in obese individuals (225). No previous studies were found reporting the positive association between HOMA-IR and Gardnerella or the inverse association between HbA1c and Gardnerella which were found in our study (Tables S1a andS1c). Overall, the GAMLSS models showed that metabolic biomarkers were more associated with the relative abundance of the taxa rather than their presence or absence.

A limitation of our study was the cross-sectional nature of the analyses. Repeated, prospectively collected samples would be needed to study the causal relationships between gut microbiome and markers of metabolic health and to assess their respective association with risk of diseases such as T2D or cancer. Other limitations of the current study were that TwinsUK was only females, even if sensitivity analyses including females only in the NFBC1966 showed similar results, and that data on HbA1c were not available for the replication. Differences between NFBC1966 and TwinsUK populations have provided interesting insight to explore the association between microbiome and metabolic health. Indeed, a previous study has shown that associations between the microbiome and various risk factors varied by geographic location, it is a potential limitation that some associations may differ in other populations ( 226). Therefore, being able to see associations between gut microbiome composition and measures of metabolic health in two different populations showed consistency, independently of other potential factors. Finally, we recognize the limited taxonomic resolution and accuracy of the 16S rRNA gene sequencing methodology, preventing us to adequately perform species-level associative analysis and to unambiguously discriminate low-abundance taxa from noise and further replication and extension (e.g., metagenomics) of results in other population-based studies is warranted.

Our study suggests that general population cohorts are valuable in identifying potential associations between microbial features and measures of metabolic health, particularly with low contrasts in exposure and disease. However, the use of cross-sectional data does not allow us to causally interpret these associations, since interactions between metabolic biomarkers and the gut microbiome are complex and dynamic, and can be strongly affected by behavioural changes. Therefore, it is now critical for future studies to collect longitudinal data of both lifestyle exposures and the microbiome to help understand the dynamic relationship between the gut microbiome and host metabolism. Integration of microbiome data paired with fecal metabolomics data will provide a more complete picture of the metabolomics-microbial mechanisms that contribute to metabolic balance between the host and the gut microbiome.

In conclusion, insulin resistance, poor control of blood glucose levels and systemic inflammation were associated with statistically significantly lower gut microbiome diversity and distinct microbial community structures in both the NFBC1966 and TwinsUK, even after control for BMI and other parameters. These results from two distinct population-based cohorts provide evidence that individuals with worse metabolic control have lower gut microbial diversity and may provide insight into the pathophysiological mechanisms that underlie poor metabolic health. It is, however, impossible to conclude if these differences in microbial composition and taxa associations are a cause or a consequence of metabolic dysfunction. Thus, largescale, prospective studies with collection of fecal samples and longitudinal data on lifestyle and metabolic biomarkers at several time-points are now needed to validate and extend these observations.

Tables Table 1 -

Objective

The aim of this study was to compare the impact of different fecal collection methods currently used in ongoing colorectal cancer screening programs on microbiome technical reproducibility, accuracy and stability.

Methods

Fecal sample processing

Fecal samples from nineteen healthy volunteers were aliquoted across seven different collection methods, including two cryotubes with no solution (considered to be the or 30°C, or mailed to undergo colorectal cancer screening procedures before being frozen. Bacterial loads were calculated based on qPCR and microbiome composition were analysed for a total of 304 samples using 16S rRNA gene sequencing.

Statistical analysis

Intraclass correlation coefficients (ICCs) were calculated using the variance components from a one-way ANOVA to evaluate the stability and accuracy of the different fecal collection methods. The ICCs were computed based on (i) the square root of the relative abundances of the three most dominant phyla (Actinobacteria, Bacteroidetes, and Firmicutes) ant the most abundant genera (Faecalibacterium, Bacteroides, UCG-002, Subdoligranulum, Roseburia, Eubacterium eligens group, Blautia, Christensenellaceae R-7 group, Ruminococcus), which were present in at least 50% of fecal samples with relative abundance of ≥0.1%, (ii) our three alpha diversity metrics, and (iii) the first multidimensional scaling axis, also called first principal coordinate (PC1), of our four beta diversity metrics.To calculate accuracy

ICCs, we compared one replicate of samples without solution frozen immediately (considered as the gold standard), selected randomly, to one sample from each of the other collection methods for each participant. To calculate stability ICCs at different temperatures and procedures for each fecal collection method, we compared one sample frozen immediately to one stored at different conditions for each participant.

Results

Variability in diversity was largely explained by differences between participants and less by the collection method. Accuracy and stability ICCs were generally very high for PC1 of beta diversity matrices, except for OC-Auto Sampling tubes stored at 30°C and for Hemotrust tubes stored at room temperatures. Accuracy measures were very inconsistent for the relative abundance of the three phyla and alpha diversity, and in particular, very low for the inverse Simpson. At the genus level, we did not observe any differences in accuracy compared to the putative gold standard between Gram positive of Gram negative bacteria. Genera that represent a phylogenetically narrow group of species, UGC-002 and Christenellaceae RT7 group (≥0.75). Microbial profile stability was very high for specimen collection cards and seemed generally acceptable for FIT tubes (≥0.75), except for Hemotrust tubes (range, 0.06-0.94). Colorectal cancer screening tests did not impact microbiome stability in FIT tubes (≥0.75), however, exposure to summer temperatures (i.e. >30°C) (range, 0.41-0.90) did influence stability.

Conclusion

Our study supports previous findings indicating that microbial data obtained from FIT tubes and specimen collection cards are relatively stable and accurate and may be appropriate methods to collect fecal samples for gut microbiome analysis in populationbased cohort studies. Furthermore, our findings suggest that opportunistic collection of fecal samples in FIT tubes after colorectal cancer screening is feasible, thereby permitting the potential establishment of cohorts within such screening programs. The impact of fecal sample collection methods on gut microbiome parameters has been recently investigated (194,(230)(231)(232)(233)(234). Microbial populations in fecal samples collected using fecal occult blood tests (FOBT) and FIT have been found to be stable at room temperatures for up to 4 to 7 days, with similar microbial communities compared to samples collected without an additive and frozen immediately. For example, in one study conducted among 52 healthy volunteers in the United States, all fecal sample collection methods (i.e. no additive, 95% ethanol, RNAlater, postdevelopment FOBT cards, and FIT tubes) yielded microbial data that appeared relatively reproducible, stable, and accurate, when compared to the putative gold standard, and provided evidence that these collection methods can be employed for microbiome analyses in population-based studies (194).

Scientific article

However, to potentially inform on the establishment of international studies based in colorectal cancer screening programs, additional methodologic work is needed to test FIT and specimen collection cards used in other countries where different FIT methods and screening procedures are employed. Therefore, we designed a study to evaluate microbial stability in fecal specimens stored at room temperatures and accuracy of microbiome metrics from two different specimen collection cards and four different FIT tubes used in ongoing international colorectal cancer screening programs (in France and most European countries, Morocco, Turkey and Iran). Additionally, we investigated the impact of colorectal cancer screening procedures and alternative shipping temperatures (e.g., summer and winter temperatures) on microbiome accuracy and stability ascertained from FIT samples.

Results

Microbial alpha diversity by collection method -The Shannon index and inverse

Simpson index values were, on average, highest for the immediately frozen samples without solution, (i.e. the putative gold standard) and the number of observed OTUs appeared highest in the specimen collection cards (Figure 1). However, the different storage conditions did not seem to have a statistically significant impact on these alpha diversity metrics. From the linear mixed-effects model, we found that compared to the immediately frozen samples without solution (296, 95% CI [257; 335]), the observed number of OTUs was significantly higher in GenSaver cards at room temperatures (347,95% CI [308;387], P = 0.01) and in OC-Auto Sampling FIT tubes at -80°C (344, 95% CI [305; 383], P = 0.03). Compared to the defined gold standard (4.03, 95% CI [3.81; 4.24]), the Shannon index was significantly lower in OC-Auto Sampling FIT tubes at -80°C (3.73,95% CI [3.51;3.94], P = 0.005), One-Step FOB tubes at -80°C and at room temperatures (3.76,95% CI [3.54;3.98], P=0.03 and 3.49, 95% CI [3.27; 3.72], P < 0.001, respectively), and in Specimen Collection Container A tubes at -80°C and at room temperatures (3.60,95% CI [3.38,3.82], P < 0.001 and 3.70, 95% CI [3.48; 3.91], P < 0.001, respectively).

Percent variability explained by participant and collection method -All multidimensional scaling ordinations suggested that the between participant variability was higher than the technical variability (Figure S2 in supplementary material). In multidimensional scaling (MDS) plots, each point represents one microbiome sample for 19 participants and seven collection methods; all samples from a given participant tended to group together (as shown by ellipses, one per participant). Additionally, based on four distance matrices (i.e. Jaccard, Bray-Curtis, unweighted UniFrac and weighted UniFrac), the overall variability in diversity was largely explained by between participants (55% to 79%) and only marginally by collection methods (4.8% to 14.8%, Figure S3). For example, based on the Bray-Curtis distance matrix, the protocol variability accounted for roughly 6.9% of the overall variability whereas the biological variability accounted for 79.0%.

Relative abundance comparisons -At the phylum level, the distributions of relative abundances of each phylum were consistent for all the collection methods; samples were mainly represented by Firmicutes followed by Bacteroidetes (Figure 2). At the genus level, when compared to the putative gold standard, the relative abundance of Faecalibacterium was greater in the other collection methods, especially in the OC-Auto Sampling FIT tubes. Consequently, when compared to gold standard, the relative abundance of other genera, such as Bacteroidetes or Blautia, was lower in other collections methods. However, there was also substantial inter-individual variability at the genus level (Figure S4 in supplementary material).

Accuracy compared with putative gold standard -Samples collected without solution and immediately frozen at -80°C were considered as the putative gold standard and compared to samples collected using other methods in different storage conditions using ICCs (Figure 3, and Bray-Curtis and unweighted UniFrac distances for all collection methods. Additionally, technical variability was also quantified based on beta diversity distances between the two replicates of the putative gold standard and used as baseline to assess the accuracy of each method. For each participant, the distance between the putative gold standard and each collection method was computed (Figure S5 in supplementary material). All those distances were slightly higher than the baseline, suggesting that the effect the collection methods exceeds the technical variability.

Stability -In each collection method, samples frozen immediately were compared to the samples stored in different conditions using ICCs (Figure 5, and 2B and Figure 6) In addition, UGC-002, but also Faecalibacterium, Roseburia and Ruminococcus were also subjected variations when stored at 30°C.

Discussion

In this study we compared microbiome stability and accuracy across different fecal sample collection methods (i.e. two different specimen collection cards and four different FIT tubes) used in ongoing colorectal cancer screening programs. We found that the overall variability in diversity was largely explained by differences between participants and less by the collection method. In addition, accuracy and stability ICCs were generally very high for PC1 of beta diversity matrices, except for OC-Auto

Sampling tubes stored at 30°C and for Hemotrust tubes stored at room temperatures.

Accuracy measures were very inconsistent for the relative abundance of the three phyla and alpha diversity, and in particular, very low for the inverse Simpson. This highlights the importance of using one consistent method for study comparisons.

Overall, microbial profile stability was very high for specimen collection cards and seemed generally acceptable for FIT tubes, except for Hemotrust tubes. Colorectal cancer screening tests did not impact microbiome stability in FIT tubes, however, exposure to summer temperatures (i.e. >30°C) did influence stability. These results are informative for the development of future population-based cohorts with fecal sample collection within colorectal cancer screening programs.

As shown in prior studies that have assessed microbiome diversity in human or animal samples, microbial composition and diversity were largely explained by betweenparticipants differences and only marginally by the collection methods (194,233). Fecal specimen collection cards have been previously tested for microbial analysis in other studies and have shown moderate to excellent accuracy compared to the putative gold standard and stability at room temperatures (230,233,(235)(236)(237)(238)(239)(240). In this study, the specimen collection cards (i.e. GenCollect and GenSaver) stored at room temperatures for 10 weeks showed excellent stability when compared to the immediately frozen cards, although other studies detected lower amounts of DNA among fecal samples from humans and animals collected on FTA cards (Whatman)

after several weeks (238,241). Also consistent with previous findings, we found that specimen collection cards tended to differ in bacterial taxa composition and have observed OTUs than when using the putative gold standard (194,235,241), but we did not observed higher Shannon and inverse Simpson. One hypothesis to explain this observation is that the card matrix may increase chemical cell lysis (235); as in our study, Firmicutes, often spore formers, were the most represented phylum in sample collected with specimen collection cards, supporting the hypothesis that cell lysis might be one explanation for these differences. Furthermore, Blautia genus, including the homoacetogen Blautia hydrogenotrophica, is also adequately represented in the collection cards, despite being highly sensitive to oxygen.

In previous studies, fecal samples stored in FIT tubes have also shown moderate to excellent accuracy compared to the gold standard and stability at room temperatures (234,242,243). However, the different types of FIT tubes in our study did not seem to perform equally. For the specimen cards and some of the FIT tubes, especially OC-Auto Sampling and Hemotrust tubes stored at -80°C, we detected significant differences in the relative abundance of phyla and genera with higher levels of Firmicutes and Faecalibacterium genus, including the anti-inflammatory butyrate producing Faecalibacterium prausnitzii, when compared to the gold standard, which support observations from a previous study (243). As described previously (237,242), when compared to the gold-standard, FIT tubes showed good to excellent accuracy for beta diversity metrics, however, some FIT tubes, including OC-Auto Sampling, Hemotrust and Specimen Collection Container A tubes, revealed lower accuracy for alpha diversity metrics, especially those stored for 7 days at room temperatures or at 30°C. Previous studies have found that FIT tubes showed good stability at room temperatures (194,234,243).

Regarding stability at room temperature, we found variable results for the different types of FIT tubes. When compared to those directly frozen, Hemotrust tubes stored at room temperature for 7 days showed poor stability for alpha diversity metrics and for One-Step FOB tubes the confidence intervals were wide, indicating high variability in stability at room temperature. In this study, we detected different stabilities at room temperature for Eubacterium eligens and Roseburia, other butyrate producers from the human gut microbiota. As OC-Auto Sampling tubes seemed to be less stable at 30°C, the collection and shipping of samples during high temperatures might have an impact on gut microbiome composition. These variations in accuracy and mostly in stability between the types of FIT tubes might be due to differences in DNA-stabilizing and antimicrobial properties of the solution (the composition of which remains proprietary for most methods) inside the tubes, impacting the stabilization of DNA, prevention of bacterial growth, and preservation of microbial profiles.

This study has several limitations. First, we included principally female, healthy participants, which might limit the inference of our results to general populations.

However, previous studies have found that stability and accuracy for comparison of samples that were frozen immediately without solution to other fecal sample collection methods were similar between different populations (194,232,234) Specifically, the FIT probes were dipped into the homogenised fecal specimen, and returned to the FIT tubes and were then shaken to mix as instructed. All of these steps were performed by the same laboratory technician.

Fecal sample storage -The study samples are outlined in On arrival at the CPO Piemonte laboratory, the FIT tubes were processed using standard colorectal cancer screening procedures (248). After testing for occult blood, the FIT tubes were immediately closed with parafilm and shipped to the Micalis Institute (INRAE/AgroParisTech) in Jouy-en-Josas, France for 16S rRNA gene profiling. Upon receipt of all the specimens at the same time, the samples were removed from the tube using pliers and a sterile pipette, transferred to a 1.5 mL Eppendorf tube, shaken, and then two 250 μL aliquots were transferred to a sterile tube and frozen at -80°C.

DNA extraction, real-time quantitative PCR and 16SrRNA sequencing -

The samples remaining at IARC were shipped on dry ice, except for the specimen collection cards stored at room temperatures, to the Micalis Institute (INRAE/AgroParisTech) in Jouy-en-Josas, France. For each collection method, sampling volumes were collected with different approaches, for optimal recovery, sterility and safety, as described below.

For the specimen collection cards, a rectangular section of the filter was cut in the centre of the filter using a sterile scalpel (average concentration per slice 41.5 ng/μL).

The FIT tubes were thawed for 20 minutes on ice, then manually shaken for 15 seconds and finally, 500 μL of fecal solution were collected as follows: Hemotrust tubes were manually opened, using the bottom red plastic screw, and gently pressed to transfer all of the liquid into a sterile 2 mL screw cap tube; One-Step FOB tubes were manually opened using the top stopper and the liquid poured into a 2 mL sterile screw cap tube; OC-Auto Sampling tubes and Specimen Collection Container A tubes were opened from the top and a long pipette tip was used to collect 4x125 μL while shaking in between each sampling.

Total DNA was extracted from 500 μL aliquots or one sliced section of the cards using Quality control -A 384-well plate was used for sequencing all the samples in one run.

The plate contained quality control samples including negative controls after V3-V4

PCR and positive controls from human gut microbiota that had already been sequenced and analyzed. Most samples from each collection method presented sufficient bacterial biomass for the V3-V4 PCR preceding 16S rRNA sequencing.

However, differences in bacterial amounts were observed between the collection methods, with lower quantity for Specimen Collection Container A tubes and One-Step FOB tubes (Table S2c in supplementary material). In detail, one subject had a lower amount of material for two of the One-Step FOB tubes, and two subjects for one of the One-Step FOB tubes, preventing to continue with processing for these samples. Three

GenCollect and one GenSaver specimen collection cards were first negative for PCR amplification and then positive after a second PCR with 50ng of DNA instead of 25ng.

Conversely, for some fit kits samples (stored at room temperature), PCR which were first negative, were positive when 10ng of DNA extract was used, suggesting the presence of PCR inhibitors. Additionally, technical variability was also quantified based on the distance between the two replicate samples without solution frozen immediately.

Technical reproducibility was high, with ICCs ≥89% between replicates of the gold standards for all seven tested metrics. Simpson index) and the differences between samples (beta diversities: Jaccard, Bray-Curtis, weighted and unweighted UniFrac distances).

Bioinformatics

Statistical analysis -Statistical analyses were conducted using R, version 3.6.2 and the packages DESeq2, icc, phyloseq and vegan (202,(256)(257)(258). Descriptive characteristics of study participants were based on the questionnaire provided by the participants. We performed visualization using multidimensional scaling (MDS) plots and all samples from a given participant tended to group together. To assess the impact of the protocol on the alpha diversity, comparison of measures of alpha diversity between each fecal collection method was performed and a linear mixed-effects models with the collection method as fixed effect and the participant as random effect was fitted to the data and used to calculate least-squares means of the alpha diversity metrics. Mean diversities for the collection methods were then compared using Tukey's HSD tests.

Chapter V: Discussion and perspectives

In this chapter, the main findings reported in the three result chapters (II to IV) are summarised with regard to the objectives and compared with the broader evidence. In addition, research perspectives are discussed and future recommendations formulated.

Discussion

The first part of the thesis aimed to evaluate the strengths and weaknesses of the current evidence on the relationship between the human microbiome and cancer in epidemiological studies (objective I). Among all the anatomical sites evaluated, the gut microbiome followed by the oral microbiome were most frequently studied in human observational studies. The majority of articles reported specific differences in microbiome diversity and/or composition between cases and control groups (including not only non-cancer patients but also tissues). Fifty studies reported significant differences in gut microbiome composition between colorectal cancer patients and various controls. Overall, there was consistent evidence that the abundance of proinflammatory opportunistic taxa, such as Fusobacterium, Parvimonas and

Porphyromonas, was increased in colorectal cancer patients compared to non-cancer cases while a depletion of butyrate-producing bacteria was observed. Sixteen studies described differences in oral microbiome composition between oral cancer patients and controls. Enrichment of Fusobacterium and depletion of Streptococcus was reported in patients with oral cancer compared with controls. Interestingly, many bacteria from the oral cavity were not only associated with oral cancer but also with other cancer sites.

With respect to objective I, the discovery of consistent associations for certain taxa of the gut microbiome and colorectal cancer risk, and for the oral microbiome and oral cancer risk, represented a significant advancement in the current evidence on the relationship between the human microbiome and cancer in epidemiological studies.

However, this strength was somewhat outweighed by certain methodological barriers and the overall evidence was still too weak to draw firm conclusions. For instance, other differences in the abundance of certain bacteria were found between cancer cases and controls, but were less consistent between studies. As stated in the introduction of the thesis, these inconsistent findings are likely due to the fact that measurement and analysis of microbiome data are still very limited in the field of cancer epidemiology. Most studies on the relation between the human microbiome and cancer have employed case-control and other cross-sectional designs without repeated measurements. Thus, it remains difficult to establish the causality of the cancermicrobe associations. It is not clear whether the carcinogenic process changes the local micro-environment and creates new niches for bacteria, or if an alteration of microbiome composition and functions contribute to carcinogenesis (129,187). In addition, poor reproducibility between studies and more specifically, variations in study design and methods used to characterise the microbiome can impact the comparability between these different studies (196). Moreover, much is still unknown on microbiome stability and therefore, different sample storage strategies (259,260), laboratory methods, sterilisation procedures, sequencing strategies and various processing pipelines for the raw microbiome sequence data ( 261) can all impact the comparability of different studies (196,262). Standardised methods for the collection of samples, preparation and handling of samples, and bioinformatics processing of data are now needed (196). Therefore, repeated, prospectively collected samples from populationbased cohort studies, using standardised protocols, are necessary for better understanding of the temporal nature of microbial associations with cancer.

The second part of the thesis aimed to investigate the association between insulin resistance and inflammation, established colorectal cancer risk factors, and the gut microbiome using population-based epidemiological data (objective II). To this end, we examined gut bacteria 16S rRNA gene sequence data with serologic levels of homeostatic model for insulin resistance, HbA1c and CRP in two populations of individuals from the Northern Finland Birth Cohort and TwinsUK. Globally, we found that higher levels of these metabolic parameters were associated with decreasing richness or diversity of microbiome composition in the two cohorts. Most importantly, this relationship between high levels of metabolic parameters and lower microbiome diversity remained statistically significant, even after control for body mass index and other health indicators. In addition, in both cohorts, insulin resistance was associated with unweighted UniFrac but not with weighted UniFrac. As seen in table 2 of the introduction, UniFrac is a measure based on phylogenetic measures of community beta diversity. The unweighted UniFrac measure allows the detection of differences in the presence or absence of lineages of bacteria in different communities (263). On the other hand, weighted UniFrac is a quantitative variant of UniFrac and helps detecting differences in abundance even when the overall groups of organisms that are present in each sample remain the same. In other words, when considering the full microbial community, we found that insulin resistance was associated with differences in the presence or absence of bacteria in different communities but not with differences in abundance. We have also investigated the association between insulin resistance and specific genera using the zero-inflated beta regression model in which, the presence/absence of the taxon in the samples is modelled with a logistic component and the non-zero abundance of the taxon is modelled with a beta component (264).

Using these indicators, we found that insulin resistance was associated with the relative abundance of specific genera rather than their presence or absence.

In regard to objective II, the Northern Finland Birth Cohort and TwinsUK data sets are currently quite unique in that fecal samples had been systematically collected and despite their cross-sectional design, by utilising microbiome data obtained from these samples we were able to provide evidence that support previous experimental observations on the link between metabolic health and the gut microbiome. Our findings suggest that there is a relationship between the gut microbiome and metabolic health that is independent of obesity and other measured factors. However, due to the cross-sectional nature of these two studies, it is impossible to conclude from our findings if these differences in microbial composition and taxa associations are a cause or a consequence of metabolic dysfunction. This relationship is likely complicated by the fact that the interactions between the gut microbiome, the host metabolism, the immediately with no solution were estimated. Reassuringly, the major source of variation of fecal microbial profiles using 16S rRNA gene sequencing was the between individual variability, followed by between collection methods. Overall, accuracy of beta diversity measures was generally very high when compared to samples frozen immediately with no solution. However, these collection methods differed in the relative abundance of various phyla and genera, and alpha diversity measures. In addition, microbial profile stability was very high for specimen collection cards and seemed generally acceptable for FIT tubes, except for Hemotrust tubes stored at room temperature and OC-Auto Sampling tubes stored at 30 C. Most importantly, our findings indicated that passage through colorectal cancer screening tests did not impact microbiome stability in FIT tubes.

With respect to objective III, the evaluation of these fecal sample collection methods used in ongoing colorectal cancer screening programs indicates that opportunistic collection of fecal samples in FIT tubes after colorectal cancer screening is feasible.

This supports the hypothesis stated in the introduction that colorectal cancer screening registries in which millions of FIT samples are being collected each year, might provide rich opportunities to establish cohorts of individuals with pre-diagnostic collected fecal samples. Our study was informative on several levels since it showed that most, but not all, fecal sample collection methods were viable for microbiome analysis, and therefore provided information that researchers may wish to consider in planning and executing future prospective studies. The variations in accuracy and mostly in stability between the different types of FIT tubes for the relative abundance of various bacteria highlighted the importance of using at least one consistent method for study comparisons. As seen in objective I, conducting pooled analyses or meta-analyses is not possible if individual studies collect fecal samples using different methods.

However, to attain adequate power to detect disease associations with microbiome profiles, microbiome data will likely have to be pooled across multiple studies. In addition, variations in accuracy and mostly in stability were observed between the different types of FIT tubes. A large variety of FIT tubes from different manufacturers are available on the market, and each FIT buffer might have different DNA-stabilizing and anti-microbial properties. The most commonly employed OC-Auto Sampling FIT seems to be reliable for microbiome analysis, however other types, such as Hemotrust tubes, revealed limitations in terms of stability at room temperature. Moreover, the low microbial profile stability of OC-Auto Sampling tubes stored at 30 C indicated that seasonal variation should be taken into account when collecting fecal samples from colorectal cancer screening programmes. Although opportunistic collection of fecal samples from populations using colorectal cancer screening methodologies is a costeffective option that might facilitate the inclusion of participants and the collection of repeated specimens, effort should be granted to coordinate protocols across studies for the use of similar types of collection methods.

Research perspectives

With respect to the findings of this thesis, future research aiming at improving the understanding of the role of the intestinal microbiota in colorectal cancer development should consider the following challenges and opportunities.

One major limitation identified from the first part of this thesis was that it was not possible to provide quantitative results from meta-analysis due incomparable results from individual studies. As previously indicated, comparing published data is complicated by lack of standard processing and analysis method. Prior meta-analyses of case-control gut microbiome studies focusing on obesity, inflammatory bowel disease or colorectal cancer have been conducted, however, results were not always consistent (265-268). For example, a meta-analysis from multiple microbiome studies to identify microbial markers associated with colorectal cancer revealed that the ability to detect certain microbiome-colorectal cancer associations was limited due to differences across studies in terms of sample collection, DNA extraction methods and 16S rRNA gene sequencing region as characterised by samples clustering primarily by their original studies rather than colorectal cancer case-control status (268). In addition, these meta-analyses have been limited by focusing on one or two diseases, and therefore, focusing on a broader landscape of diseases, such as different cancer sites, is even more complicated. Pending the implementation of collaborative microbiome cohorts with global standardisation, the sharing of microbiome data along with metadata should be encouraged to allow members of the scientific community to replicate, confirm and build on individual existing findings (261,262). However, if collecting raw amplicon sequencing data sets could permit systematic re-processing and re-analyse of the data in a way that allows meta-analysis, many other possible sources of variation should still be considered, such as experimental and sequencing artefacts or host confounders [START_REF] Falony | Population-level analysis of gut microbiome variation[END_REF][START_REF] Vujkovic-Cvijin | Host variables confound gut microbiota studies of human disease[END_REF].

Findings from the second part of this thesis revealed an interesting statistical consideration in the analysis and interpretation of microbiome data. As mentioned above, in both the Northern Finland Birth Cohort and TwinsUK, we found that insulin resistance was associated with differences in the presence or absence of bacteria in different communities but not with differences in abundance when using weighted and unweighted UniFrac beta diversity measures. On the other hand, when using a zeroinflated beta regression model, we found that insulin resistance was associated with the relative abundance of specific genera rather than their presence or absence. One explanation could be that the association between insulin resistance and microbiome diversity captured by beta diversity measures was likely driven by a consortium of bacteria present at low relative abundance, as beta diversity matrices take into account the entire microbial community while the zero-inflated beta regression model directly addresses the distribution of single bacteria (269). Another explanation might be that the specific lineages that are driving the association with insulin resistance were excluded from the zero-inflated beta regression model due to very low relative abundance or prevalence since this model directly addresses the distribution of genera relative abundance that resemble a zero-inflated beta distribution (264). Finally, one other explanation could be that beta diversity matrices were computed at the ASVs level and not at the genus level. Microbiome data are high-dimensional structured multivariate sparse data and because of their compositional nature, microbiome data analysis can be challenging (270,271). The Northern Finland Birth Cohort and TwinsUK data could provide opportunities to evaluate current methodologies employed in microbiome statistical analysis and could help to develop new standardised methods for future studies.

In the Northern Finland Birth Cohort analysis, a potentially important finding was that the relationship between higher levels of insulin resistance and lower microbiome diversity was independent of body mass index suggesting that the observed relationship between the body mass index and the gut microbiome diversity might also be influenced by measures of insulin resistance. In addition, although this finding were only observed in the Northern Finland Birth Cohort, the association of body mass index and microbiome diversity was attenuated when adjusting the model for insulin resistance, supporting that some of the body mass index-microbiome relation might be driven through insulin resistance. However, this hypothesis needs to be further explored. One possibility would be to stratify the data by body mass index and verify if the association between insulin resistance and gut microbiome diversity is still observed in lean individuals. However, this analysis would require a larger sample size as only few participants in our study had normal weight with high insulin resistance levels or were obese with low insulin resistance levels. A recent study from Korea evaluated differences in microbiota composition between 317 metabolically healthy and 430 metabolically unhealthy obese individuals (272). Significant differences in gut microbiome composition was observed between the two groups and metabolically unhealthy obese individuals had lower microbial diversity compared to metabolically healthy obese controls. Although these results should be interpreted carefully due to the cross-sectional design of the study and the selection of obese individuals only, these findings support the hypothesis that there is a link between the gut microbiota and metabolic health independent of obesity. Another study, using data from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, has shown that metabolic health (characterised by insulin levels) might be a risk factor for colorectal cancer independent of obesity (203). However, the etiology of poor metabolic heath in lean phenotype is still unknown and therefore, makes the prevention of associated diseases such as colorectal cancer difficult. One potential explanation could be that alterations of the gut microbiome could interfere with intestinal permeability, increasing the absorption of LPS which initiates activation of Toll-like receptors, inducing increased inflammation and impairment of insulin signalling pathways (273). Understanding the relationship between the gut microbiome and insulin resistance in lean persons could open up new research lines for the prevention of colorectal cancer by targeting the gut microbiome through diet and drugs.

The Northern Finland Birth Cohort represents a valuable platform for future microbiome analysis as many different types of data have been collected. For instance, global biochemical profiles were also measured using a non-targeted metabolomics approach in a subset of fecal samples and have been explored in relation to obesity (204). The availability of both microbiome and metabolomics data will allow us to explore connections between the gut microbiome and fecal metabolome. Such integrated analysis will provide further insight into the likely complex relationship that we observed between gut microbiome profiles and markers of metabolic health. As mentioned in the introduction, 16S rRNA gene sequencing data does not provide sufficient resolution for species-level analyses and therefore, insights into the functional relationships between the gut microbiome and the host remain challenging. The use of metabolomics in the Northern Finland Birth Cohort might help to infer the functional status of host-microbial relationships in fecal specimens (204-206). Indeed, while 16S rRNA gene profiling provides information on gut microbiome composition and diversity, metabolomics could additionally describe microbial activity. We plan to run such microbiome-metabolomics analyses in the Northern Finland Birth Cohort, to better understand the link between metabolic health parameters and the gut microbiome.

In addition to fecal samples, serum samples are also available in the Northern Finland Birth Cohort and as such, potential associations between metabolic profiles in fecal and serum samples could be analysed and compared with gut microbiome composition. Previous studies using mouse models have shown that the accumulated levels of gut microbial metabolites in the intestinal tract does not always correspond with that in the host circulation (207,208). Another extension of our work could consist of parallel analyses of fecal and serum metabolites to help understand the biosynthetic pathways of gut microbial metabolites under different pathophysiological states of the host or identify gut microbiome-derived metabolites.

The observed association of microbiome diversity measures with serologic levels of metabolic health biomarkers in the Northern Finland Birth Cohort may have also been inflated or attenuated by unmeasured or poorly measured confounders, such as dietary intake. Given the diversity of foods in diets, accuracy of dietary exposure is always challenging in free-living populations, and some degree of measurement error is inevitable due to the inaccurate identification of different food components (209,210).

In the Northern Finland Birth Cohort, dietary questionnaire data and fecal metabolomics, along with serum metabolomics, could be analysed to identify metabolites related to specific food components or food patterns and assess associations of these markers with gut microbiome composition and diversity, and metabolic health parameters (211,212). We plan to run this analysis in the next phase of our research.

It is important to place the results from the third part of this thesis in context with cost and feasibility for implementation of fecal sample collection in large-scale studies and use for other -omics analyses. For example, immediately freezing samples with no solution at -80°C is likely to be infeasible in larger population-based studies and, in absence of DNA stabilising agents, their stability when left at room temperature for longer periods of time is questionable (189,213-216). In addition, although considered as the "gold standard", it is difficult to ascertain whether the results seen with these samples is representative of the actual microbiome composition in the intestinal tract of the host, especially since bacterial growth is possible during freezing/thawing processes or collection conditions (e.g. self-collection at home) (217-219).

On the other hand, antimicrobial agents in the buffer allows DNA stabilisation which confer acceptable microbial stability for FIT tubes stored at room temperature (194,234,242,243), but not at high temperatures. However, FIT tubes also include stabilising solutions which are proprietary and stability may differ by manufacturers, as described in this thesis. In addition, FIT tubes have previously been found to be suboptimal for metabolomics studies, especially when stored at ambient temperature (224). Laboratory practices should also be considered as the implementation in largescale studies might require automatization of certain processes. For example, we found that due to the rigid plastic frame of certain type of FIT tubes, manual buffer extraction was complicated. Based on qPCR data, our findings indicated that it is unnecessary to extract the complete volume of the solution to obtain sufficient DNA which might be logistically easier in population-based studies. In addition, depending on the manufacturer, FIT tubes are of different sizes and some can be quite large, which could be another challenge to address if thousands of samples need to be stored at -80°C in future population-based studies.

123

In our study, the specimen collection cards stored at room temperatures for ten weeks showed excellent stability when compared to the immediately frozen cards, as previously described (230,233,(235)(236)(237)(238)(239)(240). In addition, they also have the advantage to be easily transportable and storable, and cheap, which are important considerations in the context of setting up large-scale population-based studies. Specimen collection cards were also found to be optimal for metabolomics studies, as they were reproducible, stable, and accurate in different studies (233,288).

Further analyses could be conducted to evaluate these collection methods since additional samples frozen immediately at -80°C with no solution are stored at IARC.

For example, effects of long-term storage without buffer can be tested as microbiome composition could be investigated over long periods in many future population-based prospective cohorts, and new analyses may be required after several years. The effect of long-term freezing on fecal microbiome stability has previously been investigated for storage at -80°C for more than two years (282) and for storage at -20°C for more than 14 years (289). In these studies, long-term storage seemed to induce few significant changes in microbial community, with few variations in some relative abundances of specific taxa but similar microbiome diversity measurements, confirming stability of the fecal microbiome during long periods of freezing, even for freezing conditions at -20°C.

The replication and validation of these results using our samples would be interesting, even if cryopreservation protocols, such as the time and storage conditions until freezing as well as the freezing process itself, should not be ignored (247). In addition, technical reproducibility of our results could also be performed by re-processing the samples frozen immediately at -80°C with no solution in another laboratory, taking account of the potential effect of longer storage conditions. Finally, the demultiplexed 16S rRNA gene sequencing data could also be used to compare the impact of different bioinformatics pipelines and processes on the results. For example, comparison of Swarm (253) with some of the most widely-used denoising methods, such as DADA2 [START_REF] Callahan | DADA2: High-resolution sample inference from Illumina amplicon data[END_REF] and Deblur (290), could be run on an artificial community to estimate alpha diversity measures and microbial composition and then replicated in our dataset on host-associated communities.

Previous methodological work, in addition to our findings indicating that detection of occult blood in stool samples does not impact microbiome accuracy and stability in FIT tubes, now serve as a proof-of-concept for the establishment of an international microbiome network that has been initiated by investigators from IARC and the National Cancer Institute (USA), along with researchers with expertise in the microbiome and representatives of colorectal cancer screening programs from different countries. One project from this international consortium that is already ongoing is a study of FIT-collected fecal samples in the colorectal cancer screening program of the Piedmont region in Italy. Gut microbiome profiles will be characterised and compared using shallow shotgun sequencing in screened-individuals who were FIT-positive and diagnosed with colorectal neoplasms, or FIT-positive but colorectal neoplasm-free after colonoscopy, or FIT-negative. Another aspect of this network comprises the implementation of feasibility studies nested within various colorectal cancer screening programs to evaluate the achievability of establishing a large multicountry prospective cohort of individuals with repeated fecal microbiome samples as well as epidemiologic and other clinical data.

Conclusion

This thesis has contributed to the investigation of the role of the gut microbiome in colorectal cancer using epidemiological approaches. The systematic review of the current evidence linking the human microbiome with cancer stressed that prospective studies with standardised methods are now needed to quantify the potential effect of the gut microbiome on cancer risk. Although, studying the direct relationship between the gut microbiome and colorectal cancer risk is currently not possible due to the lack of prospective studies with prediagnostic fecal samples, this thesis provided informative insights on the association between the gut microbiome and some parameters of metabolic health that are recognised as colorectal cancer risk factors.

Finally, the evaluation of microbiome stability and accuracy in fecal samples collected using different methods employed in ongoing colorectal cancer screening programs indicated that commonly used fecal sample collections such as fecal immunotests and paper-based collection cards are, in general, suitable media for microbiome measurements though storage factors such as ambient temperature can impact on stability for some methods. In addition, the opportunistic collection of fecal samples in fecal immunochemical test tubes after colorectal cancer screening is likely a viable method for establishing cohorts with prediagnostic fecal specimens. In conclusion, this thesis presents the state-of-the-art on epidemiological evidence for the role of the microbiome in tumorigenesis, provides novel insights on the association of metabolic risk factors for colorectal cancer with the gut microbiome in population-based studies, and finally has generated important methodological data on the impact of fecal sample collection tools on microbial measurements that is needed for future epidemiological research on the microbiome and colorectal cancer, as well as other chronic diseases.

Supplementary Table S3: The design of the studies used in the articles, presenting sample size, age and origin of participants and adjustments available for analyses. The term "case control samples" is used for studies using adjacent normal tissue from the same subject as controls. [START_REF] Ahn | Human gut microbiome and risk for colorectal cancer[END_REF]Allali 2018;[START_REF] Chen | Associating microbiome composition with environmental covariates using generalized UniFrac distances[END_REF][START_REF] Sinha | Collecting Fecal Samples for Microbiome Analyses in Epidemiology Studies[END_REF]Vogtmann, 2016;[START_REF] Ahn | Human gut microbiome and risk for colorectal cancer[END_REF]Zackular, 2014;Zeller, 2014 Porphyromonas [START_REF] Francino | Antibiotics and the Human Gut Microbiome: Dysbioses and Accumulation of Resistances[END_REF]Alexander, 2016;[START_REF] Amitay | Fusobacterium and colorectal cancer: causal factor or passenger? Results from a large colorectal cancer screening study[END_REF][START_REF] Baxter | DNA from fecal immunochemical test can replace stool for detection of colonic lesions using a microbiota-based model[END_REF]Dai, 2018;Fukugaiti, 2015;Hale, 2018;Mira-Pascual, 2014;Yachida, 2019;[START_REF] Yu | Fusobacterium nucleatum Promotes Chemoresistance to Colorectal Cancer by Modulating Autophagy[END_REF] Gardnerella vaginalis Lower alpha diversity in CRC 5 Gut [START_REF] Ahn | Human gut microbiome and risk for colorectal cancer[END_REF]Gao, 2015;Ohigashi, 2013;Richard, 2018;Yu, 2015 Higher alpha diversity in CRC 7

Gut

Burns, 2015; [START_REF] Chen | Associating microbiome composition with environmental covariates using generalized UniFrac distances[END_REF]Feng, 2015;McCoy, 2013;Mira-Pascual, 2014;Thomas, 2016;[START_REF] Amir | Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns[END_REF] Lower alpha diversity in BC [START_REF] Baxter | DNA from fecal immunochemical test can replace stool for detection of colonic lesions using a microbiota-based model[END_REF][START_REF] Chen | Associating microbiome composition with environmental covariates using generalized UniFrac distances[END_REF]Flemer, 2016;Mira-Pascual, 2014;Saito, 2019;[START_REF] Ahn | Human gut microbiome and risk for colorectal cancer[END_REF][START_REF] Amir | Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns[END_REF] Significant for BC (higher in cases)

  Jang HH, Kim G, Zouiouich S, Cho SY, Kim HJ, Kim J, Choe JS, Gunter MJ, Ferrari P, Scalbert A, Freisling H (2020) Taxonomic composition and diversity of the gut microbiota in relation to habitual diet in Korean adults. Preprint. doi: 10.21203/rs.2.22869/v1. (Submitted)

  Chapter I: Introduction ..............................................................................................

Figure 3 .

 3 Figure 3. The gut microbiome influences several hallmarks of cancer through diverse pathwaysFromFulbright et al, 2017 (128)

  F. nucleatum promotes cell proliferation by binding to E-cadherin on the colorectal cancer cell surface through FadA adhesin and activates the oncogenic Wnt/ β-catenin signalling pathway (168). Through the effect of Fap2 adhesin, F. nucleatum facilitates colorectal cancer enrichment by binding to a disaccharide sugar motif and alters the function of tumor-infiltrating lymphocytes and natural killer cells by binding to the inhibitory immune receptor TIGIT (169,170). F. nucleatum also facilitates resistance to chemotherapy by activating autophagy through LPS and Toll-like receptor 4 expressed on colorectal cancer cells (171).

Figure 4 .

 4 Figure 4. Three examples of microorganisms that might drive colorectal cancerFromGarrett, 2015 (129)

  decades. Colorectal cancer survival was therefore strongly correlated with stage at diagnosis, which supports the importance of colorectal cancer screening programs where neoplastic lesions can be detected at an early stage (174). The recommended screening for the average-risk population (i.e. individuals aged 50 years or older, with no additional risk factors) is one of the following: an annual or biennial stool test, sigmoidoscopy every 5 years, or colonoscopy every 10 years (Figure5) (175).

Figure 5 .

 5 Figure 5. Schematic representation of colorectal cancer screening protocol using FITFrom Eiken Chemical Co., Ltd. (176)

  resistance and inflammation, recognised colorectal cancer risk factors, and the gut microbiome in population-based epidemiological studies. While the gut microbiome has been hypothesised to influence insulin resistance development, human data on insulin resistance and other metabolic parameters and the gut microbiome are still limited as very few cohorts have collected both blood and fecal specimens. Insulin resistance lies on the causal pathways of diabetes and other metabolic diseases, and is an established risk factor for colorectal cancer. Therefore, deciphering the pathophysiology of insulin resistance and inflammation using population-based data might help to understand the mechanisms of colorectal cancer development and strengthen evidence for potential preventive strategies.

Table 3 -FigureFigure 1 -

 31 Figure

  putative gold standard), two GenSaver specimen collection cards (Ahlstrom-Munksjö, Helsinki, Finland) used in Afghanistan, two GenCollect specimen collection cards (Ahlstrom-Munksjö, Helsinki, Finland) used in Afghanistan, four OC-Auto Sampling tubes (Eiken Chemical, Tokyo, Japan) used in France (and most other European countries), two Hemotrust tubes (Biosynex, Illkirch-Graffenstaden, France) used in Morocco, two One-Step FOB tubes (Padyabteb, Tehran, Iran) used in Iran, and two Specimen Collection Container A tubes (Alfresa Pharma, Osaka, Japan) used in Turkey. Aliquots were then directly frozen at -80°C, or stored at room temperature, 4°C

Zouiouich S ,

 , Mariadassou M, Rué O, Vogtmann E, Huybrechts I, Severi G, Boutron-Ruault MC, Senore C, Naccarati A, Mengozzi G, Kozlakidis Z, Jenab M, Sinha R, Gunter MJ, Leclerc M (2020) Comparison of fecal sample collection methods for microbial analysis embedded within colorectal cancer screening programs. mSphere. (Submitted) Role: First author, contribution to study conceptualisation and logistic, conducted sample collection, conducted analysis, writing original draft and submission.AbstractColorectal cancer screening programs with collection of fecal samples may provide a platform for population-based gut microbiome-disease research. We investigated the impact of fecal sample collection and storage method on the accuracy and stability of 16S rRNA and bacterial load data across seven different collection methods (i.e. no solution, two different specimen collection cards and four types of fecal immunochemical test (FIT) used in four different countries) among 19 healthy volunteers. Intraclass correlation coefficients (ICCs) were calculated for the relative abundance of the top three phyla, the most abundant genera, alpha diversity metrics and the first principal coordinates of the beta diversity matrices to estimate stability of fecal microbial profiles after storage for 7 days at room temperature, 4 C, 30 C and after screening for the presence of occult blood in the stool, and accuracy compared to samples frozen immediately with no solution (i.e. the putative gold standard). When compared to the putative gold standard, significant variation was observed for all collection methods, however, inter-individual variability was much higher than the variability introduced by the collection method. Stability ICCs were high (≥0.75) for FIT tubes that underwent colorectal cancer screening procedures, except for the relative abundance of Actinobacteria (0.65), and were lower for different FIT tubes stored at 30°C (range, 0.41-0.90) and at room temperature (range, 0.06-0.94). Our findings indicate that the use specimen collection cards and different types of FIT are acceptable tools for fecal sample collection methods and inform on their utility for developing microbiome-focused cohorts nested within screening programs.ImportancePopulation-based studies with prediagnostic collection of fecal samples are lacking and are necessary for providing robust evidence on the role of the gut microbiome in disease development. Our study indicates that microbial profiles obtained from FIT and specimen collection cards are appropriate methods to collect fecal samples for gut microbiome analysis in population-based cohort studies. Furthermore, our findings suggest that opportunistic collection of fecal samples in FIT tubes after colorectal cancer screening is feasible, thereby permitting the potential establishment of cohorts within such screening programs.IntroductionEvidence on the role of the human microbiome (i.e. the collection of microbes and microbial genes found in the human body) in the development of chronic diseases such as obesity, diabetes and, potentially cancer, is growing(108,227,228). However, most of the current literature on the association of the microbiome with chronic diseases is based on cross-sectional studies that used diverse methods (229). Most established prospective cohorts did not collect fecal samples and repeated, prospectively collected samples are likely necessary for advancing understanding of the relationship between the microbiome and chronic disease development. Colorectal cancer screening programs, which provide screening through fecal tests such as the fecal immunochemical test (FIT), may offer great potential for establishing population-based cohort studies with multiple fecal specimens and epidemiologic data.

Fecal

  sample collection -At recruitment, participants were provided with a fecal sample collection kit including all materials needed for the fecal sample collection at the workplace or at home along with collection instructions. To collect the fecal sample, participants were asked to provide a fecal sample in the provided fecal collection containers (Sarstedt, Nümbrecht, Germany) by filling the scoop contained in each tube.The participant collected the fecal sample at the workplace (n=9) or at home (n=10) before going to work and returned it to the study coordinator within a few hours after collection. The study coordinator then delivered it to the laboratory for immediate processing. The participants then completed a questionnaire that was used to obtain information on the time and date of sample collection, typical bowel movements and general information.Following the collection step, the fecal samples were manually homogenized and aliquoted for the different collection methods (FigureS1in supplementary material).For each participant, fecal samples were aliquoted into two cryotubes without solution (considered to be the putative gold standard, average weight per cryotube 208.1 mg), two GenSaver specimen collection cards (Ahlstrom-Munksjö, Helsinki, Finland) used in Afghanistan, two GenCollect specimen collection cards (Ahlstrom-Munksjö, Helsinki, Finland) used in Afghanistan, four OC-Auto Sampling tubes (Eiken Chemical, Tokyo, Japan) used in France, two Hemotrust tubes (Biosynex, Illkirch-Graffenstaden, France) used in Morocco, two One-Step FOB tubes (Padyabteb, Tehran, Iran) used in Iran, and two Specimen Collection Container A tubes (Alfresa Pharma, Osaka, Japan) used in Turkey. For specimen collection cards, a disposable wooden spatula was used to smear a small portion of the homogenised feces on each window of the specimen collection cards; the flaps on the cards were closed and then each card was placed in a separate biohazard bag with desiccant bags. The FIT tubes were filled following the instructions provided by the different colorectal cancer screening programs.

  the PowerFecal DNA Isolation kit (MoBio Laboratories, Carlsbad, CA, United States) according to the manufacturer's protocol with minor modifications consisting of i) decreasing the volume of buffer based on the 500 μL aliquots taken from the FIT kits or ii) for the GenCollect and GenSaver keeping the paper filters during the first centrifugations and cell lysis. DNA purity and concentrations of FIT tubes and specimen collection cards were estimated using NanoDrop (Thermo Fisher Scientific, Waltham, Massachusetts, USA) measurements. To account for lack of specificity of NanoDrop, qPCR was used to compare biomass and DNA yield differences in all samples. DNA extracts were stored at -20°C before being used for real-time qPCR and 16S rRNA gene V3-V4 PCR amplification.DNA extracts were used for bacterial biomass quantification by qRT-PCR analysis of the 16S rRNA genes as previously described (249). Total bacterial biomass , annealing at 59 ºC), Fast SYBR Green MasterMix (Applied Biosystems) and the StepOne Real-Time PCR system (Applied Biosystems, Life Technologies). A standard curve was generated from serial dilutions of a known copy number of the target gene cloned into a plasmid vector. Final dilution of DNA samples for qPCR were 10 -3 or 10 -4 -fold depending on the sample. Bacterial biomass was obtained from two distinct qPCR runs for each participant (TablesS2a, S2b, S2c and S2d in supplementary material). A sample of 25ng of DNA underwent PCR amplification of the V3-V4 region of the 16S rRNA gene; DNA from the specimen collection cards and cryotubes were diluted to 1/10 if their concentration exceeded 30 ng/μL. Sequencing was performed at the GenoToul INRAE platform (Castanet-Tolosan, France) using Illumina technology with MiSeq kit V2 2 x 250 bp.

-

  Data were stored on secured servers at INRAE MAIAGE (Jouy-en-Josas, France) and IARC(Lyon, France). Sequencing data were analyzed with Find, Rapidly, Otus with Galaxy Solution (FROGS) v3.1.0 (250). Briefly, this pipeline included a pre-processing step where reads were merged with Paired-end read merger (PEAR) (251), dereplicated, and filtered according to their length, mismatches in primers with cutadapt (252), and N content. This step was followed by Swarm clustering (253) with an agglomeration distance of d = 3. Chimera detection was then performed using VSEARCH (254) before applying an OTU abundance filter (OTUs <0.005% of the total abundance are discarded). The most abundant sequence of each OTU was then affiliated with 100% similarity with blastn against the Silva v138 database (255). Two samples were discarded because of low sequencing depth and all remaining samples were rarefied to 7,144 reads per sample using R package phyloseq. Diversity metrics were then computed to represent the diversity of OTUs in each sample (alpha diversities: Shannon index, number of observed OTUs and Inverse

FiguresFigure 1

 1 FiguresFigure1Impact of specimen collection methods on microbial alpha diversity indexes.

Figure 2 -

 2 Figure 2 -Variability of microbial phyla and prevalent genera obtained from the collection methods.

Figure 3 -

 3 Figure 3 -The accuracy of phylum distribution and diversity metrics accuracy differ upon the collection method.

Figure 4 -

 4 Figure 4 -The accuracy of genera distribution differ upon the collection method.

Figure 5 -

 5 Figure 5 -Impact of storage conditions on the microbiota composition and diversity indexes.

Figure 6 -FiguresFigure S1 -Figure S2 -

 6S1S2 Figure 6 -Impact of storage conditions on the most abundant genera.

Figure S3 -

 S3 Figure S3 -Percent variability explained by individuals and collection methods using a distance-based coefficient of determination (R 2 ) for four beta diversity estimates.Based on Bray-Curtis, Jaccard, weighted and unweighted UniFrac distance matrices, inter-participant variability (individual, grey) explained 79%, 54.7%, 64.1% and 71.3% of variability, respectively; whereas the collection method (protocol, orange) explained 6.9%, 4.8%, 4.7% and 14.8% of variability, respectively.

Figure S5 -

 S5 Figure S5 -Distance between the gold standard and each collection method based on Jaccard (A), Bray-Curtis (B), unweighted UniFrac (C) and weighted UniFrac (D) distance matrices. Technical variability was quantified based on the distance between the two replicates of the gold standard and is highlighted by the red line.

  host immune system and potential associated chronic diseases, such as obesity, could be multidirectional or even cyclical. Conclusions from both objective I and objective II indicate that epidemiological findings from cross-sectional population-based studies should encourage the development of prospective studies with longitudinal collections of fecal and blood samples to further interrogate the causal nature of the association between colorectal cancer risk factors and the gut microbiome.The third part of the thesis aimed to evaluate the impact of fecal sample collection methods used in ongoing colorectal cancer screening programs on the gut microbiota (objective III). We investigated seven methods of fecal sample collection gathered from nineteen volunteers. These methods included no solution, two different specimen collection cards (recently used by an IARC-based study in Afghanistan), and four types of FIT used in ongoing colorectal cancer screening programs in France (and most other European countries), Morocco, Iran and Turkey. Stability of fecal microbial profiles after storage for 7 days at room temperature, 4 C, 30 C and after screening for the presence of occult blood in the stool, and accuracy compared to samples frozen

Table 3 . Possible molecular mechanisms of insulin resistance pathogenesis

 3

	From Boucher et al, 2014 (105)
	Cause	Mechanism	Effect
	Lipotoxicity
	Inflammation
	Hyperglycemia Mitochondrial dysfunction	Activation of Ser/Thr kinases	Inhibitory phosphorylation of insulin-signalling molecules
	Endoplasmic
	reticulum stress
	Genetic	Point mutations in insulin receptor and insulin-signalling molecules	Increased protein turnover Reduced expression and ligand affinity Decreased signalling capacities
	mutations	Single-nucleotide polymorphism causing increased gene expression	Increased phosphatase and tensin homolog action leading to reduced phosphatidylinositol (3,4,5)-triphosphate levels
		Hyperactivation of protein	Reduced phosphorylation of
	Lipotoxicity	phosphatase protein	insulin receptor and insulin-
		phosphatase 2A	signalling molecules
			Inhibition or insulin receptor
			tyrosine kinase activity
		Cytokine-induced suppressor of	Competition for insulin receptor
		cytokine signalling-3 protein	substrate binding to insulin
	Inflammation	activation	receptor
			Increased insulin receptor
			substrate degradation
		Cytokine-induced reduction in
		gene expression

Table 4 . Dietary and lifestyle factors associated with strong evidence to colorectal cancer risk

 4 Adapted from World Cancer Research Fund, 2017 (152)

		Decreases risk	Increases risk
			Processed meat
	Convincing	Physical activity	Alcoholic drinks Body fatness
			Adult attained height
		Wholegrains
		Foods containing dietary
	Probable	fibre	Red meat
		Dairy products
		Calcium supplements
	Further, two dietary patterns have been consistently observed to be associated with
	colorectal cancer. The first pattern indicates a potential protective effect of a diet
	characterised by high intake of fruit and vegetables as well as fibre and whole grains,
	nuts or legumes, fish or other seafood, calcium and dairy products (152). The other
	pattern, considered as unhealthy and mainly represented by the so-called Western

diet, is characterised by high intake of red and processed meat, sugar-sweetened beverages, refined grains, desserts and potatoes

(155)

. Biological mechanisms linking dietary patterns and colorectal cancer are probably multifaceted, reflecting a complex interplay of various dietary components instead of the effect of individual nutrients or specific foods (155). colorectal cancer and may directly account for more than 10% of colorectal cancer cases worldwide

(159,160)

. In obesity there is dysregulation of adipose tissue metabolism and immunology characterised by high levels of proinflammatory adipokines, which could contribute to the development of chronic low-grade inflammation promoting tumour growth and progression (161,162). However, important questions remain regarding the mechanisms underlying the link between obesity and colorectal cancer. For example, it is not known if obesity-related inflammation increases colorectal cancer risk directly or secondarily through processes such as insulin resistance (163).

  Despiteits poor sensitivity for the detection of colorectal adenomas, significant reductions in colorectal cancer mortality have been reported with the use of FOBT in screeningbased randomised trials(174,177,178). Subsequently developed after FOBT, the fecal immunochemical test (FIT) for the detection of human haemoglobin in stool has higher

	sensitivity for the detection of colorectal cancers and early stages compared with FOBT
	(179,180). Observational studies on the evaluation of FIT in colorectal cancer
	screening programs are very consistent and one, reported relative risks of death from
	colorectal cancer 10% to 40% lower among individuals screened by FIT (181-183).
	Therefore, the FIT has been recommended as the first option for detecting fecal occult
	blood in colorectal cancer screening (184) and is increasingly being used in countries
	with an organised colorectal cancer screening program (185). For example, in France,

Several stool tests are being used in countries where colorectal cancer screening registries are implemented, each of these modalities differing by specificity, sensitivity, acceptance, and economic impact. One screening tool, which has been widely implemented for its excellent specificity, is the fecal occult blood test (FOBT).

the FIT has been offered every two years to average-risk individuals, aged 50 to 74, since 2015. According to Santé Publique France, approximately 5,300,000 individuals were screened by FIT between 2018 and 2019, in France. Of these, around 198,000 (3.8%) were FIT-positives (186). These colorectal cancer screening registries in which millions of FOBT or FIT samples are being collected each year might provide rich opportunities to establish cohorts of individuals with pre-diagnostic collected fecal samples.

Chapter III: Markers of metabolic health and gut microbiome diversity: findings from two population-based cohort studies 1. Background

	While the gut microbiome has been hypothesised to influence insulin resistance and
	chronic inflammation development (93,94,107,108), human data on insulin resistance
	and other metabolic parameters and the gut microbiome are still limited as very few
	cohorts have collected both blood and fecal specimens. Insulin resistance and
	inflammation lie on the causal pathways of diabetes and other metabolic diseases, and
	Huybrechts I*, Zouiouich S*, Loobuyck A, Vandenbulcke Z, Vogtmann E, Pisanu S, are established risk factors for colorectal cancer (118-120), and therefore, deciphering
	Iguacel I, Scalbert A, Indave I, Smelov V, Gunter MJ, Michels N (2020) The human the pathophysiology of insulin resistance and inflammation using population-based
	microbiome in relation to cancer risk: a systematic review of epidemiological studies. data might help to understand the mechanisms of colorectal cancer development and
	Cancer Epidemiology, Biomarkers & Prevention. doi: 10.1158/1055-9965.EPI-20-strengthen evidence for potential preventive strategies.
	0288.*co-first authors
	Role: Co-first author, contribution to data extraction and quality controls in text and
	tables, contribution to original draft preparation and revised version.

6. Scientific article

	Abstract
	Aims/hypothesis: The gut microbiome is hypothesized to influence development of
	insulin resistance and other metabolic parameters, however, data from population-
	Zouiouich S, Loftfield E, Huybrechts I, Viallon V, Louca P, Vogtmann E, Wells P, based studies are limited. We investigated associations between serologic measures
	Steves C, Herzig KH, Menni C, Jarvelin MR, Sinha R, Gunter MJ (2020) Markers of of metabolic health and the gut microbiome in the Northern Finland Birth Cohort 1966
	metabolic health and gut microbiome diversity: findings from two population-based (NFBC1966) and in TwinsUK.
	cohort studies. Diabetologia. (Provisionally accepted -Under revision)
	Methods: Among 506 individuals from the NFBC1966, with available fecal microbiome Role: First author, contribution to analytical strategy, conducted analysis in NFBC data (16S rRNA gene sequence) data, we estimated associations between gut microbiome and provided code for TwinsUK data, writing original draft and submission, writing diversity metrics and serologic levels of homeostatic model for insulin resistance revised version. (HOMA-IR), glycated haemoglobin (HbA1c) and C-reactive protein (CRP) using
	multivariable linear regression models adjusted for gender, smoking status and body
	mass index (BMI). We used general linear models with a quasi-Poisson distribution
	and Microbiome Regression-based Kernel Association Tests (MiRKAT) to estimate
	associations of metabolic parameters with alpha-and beta-diversity metrics,
	respectively, and generalized additive models for location scale and shape (GAMLSS)

Description of the study population stratified by BMI category in

	NFBC1966 and TwinsUK
		NFBC1966 (n=506)	TwinsUK (n=1140)
			BMI, kg/m 2			BMI, kg/m 2
		18.5 to 25	25 to 30	over 30	18.5 to 25	25 to 30 over 30
	n (%)	148 (29.2)	232 (45.9)	126 (24.9)	499 (43.7)	411 (36.1)	230 (20.2)
	Male, n (%)	32 (17.1)	108 (57.8)	47 (25.1)	0	0	0
	Female, n (%)	116 (36.4)	124 (38.9)	79 (24.7)	499 (43.7)	411 (36.1)	230 (20.2)
	Age, mean (SD)	46.6 (0.6)	46.6 (0.5)	46.7 (0.6)	62.9 (8.9)	64.2 (8.5)	61.8 (8.4)
	Never smoker, n (%)	98 (33.9)	128 (44.3)	63 (21.8)	300 (43.7)	235 (34.2)	152 (22.1)
	Former smoker, n (%)	29 (23.6)	61 (49.6)	33 (26.8)	152 (44.9)	127 (37.6)	59 (17.5)
	Current smoker, n (%)	21 (22.3)	43 (45.8)	30 (31.9)	47 (40.8)	49 (42.6)	19 (16.5)
	HOMA-IR, mean (SD)	1.32 (0.7)	2.26 (1.3)	4.61 (4.4)	0.84 (1.32)	1.12 (1.46)	1.56 (1.37)
	CRP, mean (SD)	1.08 (1.7)	1.45 (2.7)	2.95 (6.6)	1.88 (2.95)	2.89 (6.29)	5.17 (8.41)
	HbA1c, mean (SD)	5.35 (0.3)	5.49 (0.7)	5.65 (0.6)	-	-	-

Table 2 -Associations of metabolic parameters (numerical and categorical) with measures of alpha-diversity in NFBC1966 and TwinsUK NFBC1966 (n=506) a TwinsUK (n=1140) b Shannon's diversity Observed ASVs Shannon's diversity Observed ASVs Least-squares mean (95% CI) Estimate 95% CI P- value Least-squares mean (95% CI)

 2

		P-	value
		95% CI
		Estimate
	Least-	squares	mean (95%	CI)
		P-	value
		95% CI
		Estimate
		Least-squares	mean (95% CI)
		P-	value
		95% CI
		Estimate

Table S1 -Association of measures of alpha-diversity with BMI before and after adjustment for metabolic markers in NFBC1966 and the TwinsUK NFBC1966 (n=506)ᵅ TwinsUK (n=1140)ᵝ Model adjusted for Shannon's diversity Observed ASVs Shannon's diversity Observed ASVs Estimate P-valueᵅ Estimate P-valueᵅ Estimate P-valueᵝ Estimate P-valueᵝ

 S1

	<0.001	<0.001	<0.001	-
	-0.009	-0.007	-0.008	-
	<0.001	0.006	<0.001	-
	-0.004	-0.002	-0.003	-
	0.001	0.489	0.082	0.015
	-0.008	-0.002	-0.005	-0.006
	No adjustment -0.002 0.258	HOMA-IR 0.002 0.253	CRP 0.000 0.972	HbA1c -0.001 0.564	ᵅAdjusted for BMI, gender and smoking status	ᵝAdjusted for BMI, age and smoking status

Table S2 -Association of BMI, before and after adjustment for metabolic markers, with community composition using measures of beta-diversity in NFBC1966 and TwinsUK NFBC1966 (n=506)ᵅ TwinsUK (n=1140)ᵝ

 S2

	Unweighted	Unifrac P-	value	<0.001	<0.001	<0.001	-
	Binomial Jaccard Weighted Unifrac P-value P-value P-value	<0.001 <0.001 <0.001	<0.001 <0.001 0.001	<0.001 <0.001 <0.001	---
	Unweighted	Unifrac P-	value	0.004	0.258	0.004	0.037
	Model Binomial Jaccard Weighted Unifrac adjusted for P-value P-value P-value	No adjustment 0.004 <0.001 0.113	HOMA-IR 0.099 0.004 0.241	CRP 0.015 <0.001 0.011	HbA1c 0.015 <0.001 0.09	ᵅAdjusted for gender and smoking status	ᵝAdjusted for age and smoking status

Table S3 (a) -Association of HOMA-IR with genera in NFBC1966 ASVs Mu (mean parameter) Sigma (precision parameter) Nu (probability at zero)

 S3

	Adj. p-value	0.5113	0.9982	0.9563	0.3907	0.1463	0.7036	0.0943	0.9995	0.1025	0.8095	0.7753	1.0000	0.3426	0.1664	0.9995	0.9997
	Estimate	-0.0833	0.0402	-0.0112	0.8720	-0.1222	0.1305	0.0988	-0.0415	-0.1227	-0.0214	-0.0953	0.0475	-0.1017	-0.1058	0.0415	-3.9031
	Adj. p-value	<0.0001	0.9982	0.0048	<0.0001	<0.0001	0.9760	0.0005	<0.0001	0.0001	0.0006	0.3736	0.1427	<0.0001	<0.0001	<0.0001	<0.0001
	Estimate	0.6482	-0.9280	-0.0328	-0.7770	0.0433	-0.0220	0.1721	0.1682	0.1890	0.2179	0.0436	-0.0590	0.7611	-0.6237	-2.8626	1.7224
	Adj. p-value	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	0.0006	0.0056	0.0121	0.8515	0.8671	0.9995	0.9997
	Estimate	-0.0223	1.3963	-0.5189	11.6284	0.0494	-0.2263	-0.0570	2.6445	0.1647	-0.1753	-0.0779	0.0633	0.0308	-0.0069	0.0409	1.0614
	Genus	Desulfovibrio	Megamonas	Unknown	rc4-4	Paraprevotella	Peptococcus	Unknown	Gardnerella	Mitsuokella	Prevotella	Oscillospira	Blautia	Butyricicoccus	Odoribacter	Corynebacterium	Arcanobacterium
	Family	Desulfovibrionaceae	Veillonellaceae	Peptostreptococcaceae	Peptococcaceae	Prevotellaceae	Peptococcaceae	Mogibacteriaceae	Bifidobacteriaceae	Veillonellaceae	Prevotellaceae	Ruminococcaceae	Lachnospiraceae	Ruminococcaceae	Odoribacteraceae	Corynebacteriaceae	Actinomycetaceae

Adjusted for BMI, gender and smoking status

Table S3 (b) -Association of CRP with genera in NFBC1966 ASVs Mu (mean parameter) Sigma (precision parameter) Nu (probability at zero)

 S3

	Adj. p-value	0.9747	0.9999	0.9970	0.3926	0.7983	0.8639	0.7581	0.2738	0.8018	1.0000
	Estimate	-0.0192	0.4157	-0.0188	0.1757	0.1023	0.0449	0.0514	0.4615	0.2016	-0.4791
	Adj. p-value	0.0000	0.0000	0.0000	0.9490	0.0000	0.1787	0.7581	<0.0001	<0.0001	<0.0001
	Estimate	-0.0659	-0.4562	0.1622	0.0302	-0.7206	-0.0758	0.1893	-1.5997	0.3027	0.2245
	Adj. p-value	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	0.0001	0.2070	0.2334	1.0000
	Estimate	-0.2106	-0.8197	-0.1915	-1.2657	-0.6372	0.2825	-0.4148	-0.1705	7.6385	0.0864
	Genus	Megamonas	rc4-4	Prevotella	Unknown	Unknown	Peptococcus	Sutterella	Odoribacter	Butyricicoccus	Arcanobacterium
	Family	Veillonellaceae	Peptococcaceae	Paraprevotellaceae	Paraprevotellaceae	Peptostreptococcaceae	Peptococcaceae	Alcal	Odoribacteraceae	Ruminococcaceae	Actinomycetaceae

Adjusted for BMI, gender and smoking status

Table S3 (c) -Association of HbA1c with genera in NFBC1966 ASVs Mu (mean parameter) Sigma (precision parameter) Nu (probability at zero)

 S3

	Adj. p-value	0.9729	0.5390	0.4174	0.9547	0.0132	0.9224	0.4060	0.3199	0.4788
	Estimate	-0.0227	1.7686	0.7325	-0.0237	3.8717	0.0874	-0.4044	0.9462	-0.2817
	Adj. p-value	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	0.4788
	Estimate	-0.3171	-0.9332	0.3281	-6.8202	-1.1690	0.3782	2.7598	1.6649	-2.6073
	Adj. p-value	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	0.0014	0.3082	0.3199	0.5934
	Estimate	-1.9110	-0.7623	-2.4020	-2.0149	-34.2932	1.4136	0.8421	-0.5572	-0.2741
	Genus	Unknown	rc4-4	Peptococcus	Mitsuokella	Gardnerella	Oxalobacter	Butyricicoccus	Desulfovibrio	Odoribacter
	Family	Peptostreptococcaceae	Peptococcaceae	Peptococcaceae	Veillonellaceae	Bifidobacteriaceae	Oxalobacteraceae	Ruminococcaceae	Desulfovibrionaceae	Odoribacteraceae

Adjusted for BMI, gender and smoking status

Chapter IV:

Comparison of fecal sample collection methods for microbial analysis embedded within colorectal cancer screening programs 1. Background Population

  -based studies with gut microbiome data collected prior to chronic disease onset are extremely limited. Colorectal cancer screening programs, which provide screening through fecal tests such as the fecal immunochemical test (FIT) and fecal

occult blood test (FOBT), may provide a platform for establishing cohorts with repeated, prospectively collected fecal specimens.

  to be higher in the general population, compared to the gold standard, where provision of a whole fecal sample could make subjects uncomfortable and reluctant to participate. Furthermore, in this study, stability was assessed over the course of several days to several weeks at room temperature, but also for different

	. Second, we used
	16S rRNA gene sequencing to characterize the microbial composition, while other
	profiling methods such as whole-genome shotgun metagenomics are becoming more
	commonly employed in high-income settings(244). However, 16S rRNA gene conditions directly reflecting settings in colorectal cancer screening (i.e. shipping at
	sequencing remains the most affordable method to study the gut microbiome diversity, different temperatures reflecting seasonal variation, colorectal cancer screening
	especially in the context of large epidemiologic cohorts. Additionally, in low-to-middle procedures including mailing and occult blood detection test). This is the first study in
	income countries, where sequencing technologies are not always available, these which the impact of colorectal cancer screening procedures on fecal samples using
	collection methods, and more specifically specimen collection cards, could be used to FIT tubes has been demonstrated. Importantly, OC-Auto Sampling FIT tubes that went
	detect specific biomarker species that have been associated with specific diseases for through colorectal cancer screening procedures and tests had good stability, opening
	opportunities for establishing prospective cohorts within screening populations.
	Methods
	Study participants -Nineteen healthy participants from the International Agency for
	Research on Cancer (IARC) personnel were recruited in Lyon, France. Eligible
	bacterial DNA obtained by qPCR indicated that sufficient material was available in participants were at least 18 years of age or older, had not taken antibiotics in the past
	these tubes. Based on qPCR data, the collected volume could be lowered from 500 3 months and were willing to provide a fecal sample. All participants provided informed
	microliters to 200 microliters to obtain sufficient DNA for several analyses and consent, and the study was approved by the IARC Ethics Committee. On average,
	rendering it unnecessary to extract all of the liquid which might be logistically participants were 40.2 years old and had a body mass index of 22.7 kg/m 2 . The

example Fusobacterium nucleatum or Parviromonas micra which have been associated with colorectal cancer development

(229,245)

. Finally, as previously described, freezing procedures might have an impact on relative abundance of Grampositive and Gram-negative bacteria (246,247), and therefore, considering samples without solution frozen immediately as the gold standard method might be suboptimal.

However, in the context of large population-based cohorts, immediate DNA extraction after defecation is likely unfeasible, and therefore, standardization of storage protocols is necessary.

Our study included many different collection methods, including two different specimen collection cards and four different FIT tubes, which are currently being used in ongoing colorectal cancer screening programs around the world. The different collection methods tested in this study has allowed us to highlight a number of important considerations for sample collections in large population settings such as acceptability from participants, safety for processing samples, the volume of kit necessary, the storage logistics and cost. For instance, due to the rigidity of the tubes, the solution in OC-Auto Sampling tubes was not easy to sample safely, however the amount of challenging. Additionally, storage of specimen collection cards is easier and cheaper than tubes, which might help low-to-middle income countries to develop infrastructure for microbiome research. Finally, because of the small size of the kits, the acceptance is expected

In conclusion, our study supports previous findings indicating that microbial data obtained from FIT tubes and specimen collection cards are relatively stable and accurate and may be appropriate methods to collect fecal samples for gut microbiome analysis in population-based cohort studies. Furthermore, our findings suggest that opportunistic collection of fecal samples in FIT tubes after colorectal cancer screening is feasible, thereby permitting the potential establishment of cohorts within such screening programs. Since different collection methods and high temperatures impact the stability and accuracy, it is important for future investigators, in the context of the implementation of large-scale epidemiologic studies, to coordinate their efforts and follow standardized protocols in order to accurately compare the microbiome between sites, groups or countries and be able to pool microbial data. majority of participants were female (78.9%), had no weight variation in the past six months (73.7%) and reported having a regular bowel movement at least once per day (94.8%) (Table

S1

in supplementary material).

Table 1 .

 1 After sample collection and processing at IARC, the two cryotubes and one of each sample type were immediately stored at -80 C. One of the OC-Auto Sampling tubes was placed in a blue mailer for colorectal cancer screening and mailed to the Reference Centre for Epidemiology and Cancer Prevention in Piemonte (CPO Piemonte) laboratory within 24 hours after processing. To mimic mailing during seasonal temperatures in France,

	one of the OC-Auto Sampling tubes was stored at a winter temperature (4 C) for 7
	days in a refrigerator, returned to room temperature for at least 4 hours, and then
	frozen at -80 C. The remaining OC-Auto Sampling tubes was stored at a summer
	temperature (30 C) for 7 days in a water bath, returned to room temperature for at
	least 4 hours, and then frozen at -80 C. The remaining FIT tubes (i.e. Hemotrust, two
	One-Step FOB and Specimen Collection Container A tubes) were stored for 7 days at

room temperature and then frozen at -80°C. The remaining half of the specimen collection cards (i.e. GenCollect and GenSaver cards) remained at room temperature in a closed cupboard from the date of collection until the date of DNA extraction (average 70.2 days).

Table 1 -

 1 Collection methods for fecal samples and number of aliquots used for microbiome analyses per participants, IARC, Lyon, France, 2019.

	Subdoligranulum,	Roseburia,	Eubacterium	eligens	group,	Blautia,
	Christensenellaceae R-7 group, Ruminococcus), which were present in at least 50%
	of fecal samples with relative abundance of ≥0.1%, (ii) our three alpha diversity metrics,
	and (iii) the first multidimensional scaling axis, also called first principal coordinate
	(PC1), of our four beta diversity metrics. The first axis explained 8.2%, 15.8%, 17.6%,
	and 49.7% of the variability for Jaccard, Bray-Curtis, unweighted UniFrac and weighted
	UniFrac distances, respectively. To calculate accuracy ICCs, we compared one
	replicate of samples without solution frozen immediately (considered as the gold
	standard), selected randomly, to one sample from each of the other collection methods

To estimate a distance-based coefficient of determination (R 2 ) explained by participant and collection method from unweighted UniFrac, weighted UniFrac, Jaccard and Bray-Curtis distance matrices, permutational multivariate analysis of variance was performed (adonis() function, vegan package, R) (202). We calculated intraclass correlation coefficients (ICCs) using the variance components from a oneway ANOVA to evaluate the stability and accuracy of the different fecal collection methods. The ICCs were calculated based on (i) the square root of the relative abundances of the three most dominant phyla (Actinobacteria, Bacteroidetes, and Firmicutes) ant the most abundant genera (Faecalibacterium, Bacteroides, UCG-002, for each participant. To calculate stability ICCs at different temperatures and procedures for each fecal collection method, we compared one sample frozen immediately to one stored at different conditions for each participant. The 95% confidence interval was estimated using the ICCest() function from the R ICC package with default option confidence interval = "Smith".

Table S1 -

 S1 Characteristics of study participants (n=19).

	Tables
	Characteristic	N (%)	Mean (± SD)
	Female	15 (78.9)
	Age, years		40.2 ± 8.43
	Body Mass Index, kg/m 2		22.7 ± 3.14
	No weight variation in the past 6 months	14 (73.7)
	Bowel movement at least once/day	18 (94.8)
			109

Table S2d -

 S2d Results from qPCR, means for all collection methods.

	log(Bacterial	quantity/mL)							9.21	9.06	9.11	8.75	9.66	9.37	8.83	8.45	9.54	9.26
	Bacterial	quantity/mL							2.34E+09	1.77E+09	1.56E+09	7.28E+08	5.37E+09	3.48E+09	9.09E+08	3.64E+08	4.20E+09	2.75E+09
	log(Bacterial	quantity/slices)			10.58	10.21	10.52	10.36
	Bacterial	quantity/slices			4.39E+10	1.92E+10	4.03E+10	3.56E+10
	log(Bacterial	quantity/g)		10.90
	Sample Bacterial mass quantity/g (mg)	284.47	131.79 9.99E+10
	Bacterial DNA	concentration	(ng/μl)	87.95	62.78	43.12	36.62	48.24	38.03	4.34	3.71	3.40	3.13	3.30	3.59	1.75	1.71	1.63	-0.23
		Sample name		Mean cryotube1 (-80°C)	Mean cryotube2 (-80°C)	Mean GenSaver (-80°C)	Mean GenSaver (RT)	Mean GenCollect (-80°C)	Mean GenCollect (RT)	Mean OC-Auto Sampling (-80°C)	Mean OC-Auto Sampling (+4°C)	Mean OC-Auto Sampling (+30°C)	Mean OC-Auto Sampling (screening)	Mean Hemotrust (-80°C)	Mean Hemotrust 19 (RT)	Mean Specimen Collection Container A (-80°C)	Mean Specimen Collection Container A (RT)	Mean One-Step FOB (-80°C)	Mean One-Step FOB (RT)

Table S3 -

 S3 Adjusted means for observed OTUs, the Shannon index and the inverse Simpson index by fecal sample collection method.

	Collection

method Observed OTUs Shannon index Inverse Simpson index Adj. Mean Lower 95% CI Upper 95% CI Adj. Mean Lower 95% CI Upper 95% CI Adj. Mean Lower 95% CI Upper 95% CI

	30.5	28.8	26.4	27.5	23.9	26	18.8	21.7	19.3	23.8	23.4	24	23.6	20.1	19.7	23.1
	21.5	19.8	17.4	18.5	14.9	16.9	9.8	12.7	10.3	14.7	14.4	15	14.5	10.8	10.7	14.1
	26	24.3	21.9	23	19.4	21.4	14.3	17.2	14.8	19.2	18.9	19.5	19	15.4	15.2	18.6
	4.24	4.18	4.17	4.21	4.14	4.21	3.85	3.97	3.94	4.14	4.03	4.01	3.98	3.72	3.82	3.91
	3.81	3.75	3.73	3.78	3.71	3.78	3.42	3.54	3.51	3.71	3.59	3.58	3.54	3.27	3.38	3.48
	4.03	3.97	3.95	3.99	3.93	4	3.64	3.76	3.73	3.92	3.81	3.8	3.76	3.49	3.6	3.7
	335	333	379	378	376	387	371	358	383	377	363	351	339	294	324	306
	257	255	300	299	297	308	292	280	305	298	284	273	260	213	246	227
	296	294	340	339	337	347	331	319	344	338	324	312	300	253	285	266
	No solution (-80°C)	No solution 2 (-80°C)	GenCollect (-80°C)	GenCollect (RT)	GenSaver (-80°C)	GenSaver (RT)	Hemotrust (-80°C)	Hemotrust (RT)	OC-Auto Sampling (-80°C)	OC-Auto Sampling (+30°C)	OC-Auto Sampling (+4°C)	OC-Auto Sampling (screening)	One-Step FOB (-80°C)	One-Step FOB (RT)	Spec. Coll. Cont. A (-80°C)	Spec. Coll. Cont. A (RT)

Table S4 -

 S4 ICCs for accuracy of microbiome diversity metrics of each fecal sample collection method compared to the "gold-standard" (samples with no solution, -80°C frozen immediately).

	wUniFrac PC1	Lower Upper	95%CI 95%CI	Shannon index 0.87 Lower Upper 0.66 95%CI 95%CI 0.99 0.97	0.71 0.97	0.90 0.99 0.41 0.92	0.42 0.92	0.50 0.94 0.84 0.99	0.59 0.96 0.53 0.94	0.44 0.93 0.09 0.82	0.75 1.00	0.26 0.88 0.32 1.00	0.13 0.58 0.75 0.98	0.46 0.94 0.71 0.97	0.19 0.76 0.60 0.96	0.17 0.85 0.68 0.97	0.38 0.91 0.54 0.95	0.70 0.97 0.57 0.95	0.65 0.97	0.77 0.98	0.62 0.96
			ICC	0.93	ICC 0.81	0.84	0.95 0.66	0.67	0.72 0.92	0.77 0.74	0.68 0.46	0.99	0.57 0.76	0.32 0.87	0.70 0.84	0.29 0.78	0.51 0.83	0.65 0.74	0.84 0.76	0.81	0.88	0.79
	UniFrac PC1	Lower Upper	95%CI 95%CI	Lower 0.99 0.83 95%CI Observed OTUs Upper 95%CI 1.00 0.99	0.94 1.00	0.90 0.58 0.99 0.95	0.40 0.92	0.71 0.95 0.97 1.00	0.02 0.94 0.78 1.00	0.70 0.60 0.97 0.96	0.97 1.00	0.53 0.38 0.95 1.00	0.11 0.97 0.72 1.00	0.83 0.94 0.99 1.00	0.28 0.96 0.92 1.00	0.79 0.96 0.98 1.00	0.70 0.97 0.97 1.00	0.53 0.97 0.95 1.00	0.64 0.97	0.60 0.96	0.59 0.96
			ICC	0.99	ICC 0.91	0.97	0.95 0.77	0.66	0.84 0.97	0.40 0.97	0.84 0.78	0.98	0.74 0.80	0.31 0.98	0.91 0.97	0.60 0.98	0.89 0.98	0.84 0.99	0.74 0.98	0.81	0.78	0.77
	Bray-Curtis PC1	Lower Upper	95%CI 95%CI	Firmicutes 0.93 Lower Upper 0.84 95%CI 95%CI 1.00 0.99	0.84 0.99	0.85 0.99 0.77 0.98	0.03 0.76	0.17 0.84 0.79 0.98	0.01 0.46 0.87 0.99	0.44 0.93 0.92 1.00	0.86 0.99	0.03 0.49 0.64 1.00	0.11 0.82 0.89 0.99	0.66 0.97 0.88 0.99	0.30 0.93 0.85 0.99	0.63 0.96 0.87 0.99	0.26 0.88 0.93 1.00	0.10 0.55 0.89 0.99	0.17 0.61	0.18 0.62	0.13 0.71
			ICC	0.96	ICC 0.92	0.92	0.92 0.88	0.37	0.51 0.89	0.45 0.93	0.68 0.96	0.93	0.42 1.00	0.47 0.94	0.82 0.94	0.61 0.92	0.79 0.93	0.57 0.96	0.36 0.94	0.27	0.26	0.29
	Jaccard PC1	Lower Upper	95%CI 95%CI	Lower 0.97 0.75 95%CI Bacteroidetes Upper 95%CI 1.00 0.98	0.86 0.99	0.90 0.68 0.99 0.97	0.17 0.85	0.34 0.96 0.90 1.00	0.14 0.93 0.58 1.00	0.58 0.70 0.95 0.97	0.92 1.00	0.19 0.83 0.67 0.99	0.15 0.95 0.70 1.00	0.70 0.92 0.98 1.00	0.27 0.94 0.92 1.00	0.71 0.94 0.98 1.00	0.50 0.96 0.94 1.00	0.18 0.97 0.62 1.00	0.16 0.69	0.18 0.68	0.08 0.74
			ICC	0.98	ICC 0.86	0.93	0.95 0.83	0.51	0.62 0.98	0.31 0.96	0.77 0.84	0.96	0.24 0.91	0.27 0.97	0.84 0.96	0.60 0.97	0.84 0.97	0.72 0.98	0.26 0.98	0.26	0.25	0.33
	Inverse Simpson	Lower Upper	ICC 95%CI 95%CI	Actinobacteria 0.90 Lower Upper 0.81 0.23 ICC 95%CI 95%CI 0.20 0.99 0.66	0.43 0.06 0.80	0.89 0.80 0.99 0.45 0.09 0.81	0.32 0.09 0.73	0.65 0.38 0.91 0.51 0.17 0.84	0.81 0.65 0.97 0.25 0.18 0.68	0.80 0.64 0.96 0.26 0.16 0.69	0.64 0.36 0.92	0.28 0.14 0.70 0.27 0.22 0.75	0.66 0.40 0.92 0.39 0.00 0.78	0.45 0.06 0.83 0.50 0.16 0.84	0.78 0.59 0.97 0.56 0.24 0.87	0.49 0.14 0.84 0.49 0.15 0.84	0.67 0.43 0.92 0.73 0.51 0.94	0.75 0.55 0.95 0.61 0.33 0.90	0.83 0.69 0.97	0.83 0.68 0.97	0.80 0.64 0.96
		Collection method		No solution 2nd (-80°C)	Collection method OC-Auto Sampling (-80°C)	OC-Auto Sampling (+4°C)	No solution 2nd (-80°C) OC-Auto Sampling (+30°C)	OC-Auto Sampling (-80°C) OC-	OC-Auto Sampling (+4°C) Auto Sampling (screening)	OC-Auto Sampling (+30°C) Hemotrust (-80°C)	OC-Hemotrust (RT)	Auto Sampling (screening) One-Step FOB (-80°C)	Hemotrust (-80°C) One-Step FOB (RT)	Hemotrust (RT) Spec. Coll. Cont. A (-80°C)	One-Step FOB (-80°C) Spec. Coll. Cont. A (RT)	One-Step FOB (RT) GenSaver (RT)	Spec. Coll. Cont. A (-80°C) GenSaver (-80°C)	Spec. Coll. Cont. A (RT) GenCollect (RT)	GenSaver (RT) GenCollect (-80°C)	GenSaver (-80°C)	GenCollect (RT)	GenCollect (-80°C)

Table S5 -

 S5 ICCs for stability of microbiome diversity metrics of each fecal sample collection method stored in different conditions compared to those directly frozen

	Shannon index	Lower Upper	95%CI 95%CI	0.66 0.97	0.44 0.93		0.78 0.98	0.33 0.57	0.47 0.97	0.76 0.98	0.87 0.99	0.92 1.00	wUniFrac PC1	Lower Upper	95%CI 95%CI	0.82 0.99	0.54 0.95		0.79 0.98	0.61 0.96	0.61 0.98	0.68 0.97	0.93 1.00	0.91 1.00
			ICC	0.81	0.68		0.88	0.12	0.72	0.87	0.93	0.96			ICC	0.90	0.75		0.89	0.79	0.80	0.83	0.96	0.95
	Observed OTUs	Lower Upper	95%CI 95%CI	0.39 0.91	0.32 0.89		0.73 0.98	0.08 0.81	0.61 0.98	0.74 0.98	0.90 0.99	0.86 0.99	UniFrac PC1	Lower Upper	95%CI 95%CI	0.69 0.97	0.55 0.95		0.89 0.99	0.66 0.97	0.43 1.00	0.95 1.00	0.95 1.00	0.96 1.00
			ICC	0.65	0.61		0.85	0.45	0.80	0.86	0.95	0.93			ICC	0.83	0.75		0.94	0.81	0.87	0.97	0.97	0.98
	Firmicutes	Lower Upper	95%CI 95%CI	0.71 0.98	0.50 0.94		0.64 0.96	0.10 0.72	0.24 0.93	0.40 0.92	0.92 1.00	0.79 0.98	Bray-Curtis PC1	Lower Upper	95%CI 95%CI	0.84 0.99	0.81 0.99		0.82 0.99	0.89 0.99	0.69 1.00	0.89 0.99	0.99 1.00	0.97 1.00
			ICC	0.84	0.72		0.80	0.31	0.58	0.66	0.96	0.89			ICC	0.92	0.90		0.90	0.94	1.00	0.94	0.99	0.98
	Bacteroidetes	Lower Upper	95%CI 95%CI	0.76 0.98	0.45 0.93		0.72 0.98	0.39 0.91	0.47 0.97	0.67 0.97	0.91 0.99	0.84 0.99	Jaccard PC1	Lower Upper	95%CI 95%CI	0.67 0.97	0.39 0.91		0.72 0.98	0.76 0.98	0.92 1.00	0.90 0.99	0.93 1.00	0.97 1.00
			ICC	0.87	0.69		0.85	0.65	0.72	0.82	0.95	0.91			ICC	0.82	0.65		0.85	0.87	0.96	0.95	0.96	0.99
	Actinobacteria	Lower Upper	ICC 95%CI 95%CI	0.79 0.62 0.96	0.41 0.04 0.79		0.65 0.39 0.91	0.55 0.23 0.87	0.21 0.31 0.73	0.58 0.27 0.88	0.91 0.84 0.99	0.93 0.86 0.99	Inverse Simpson	Lower Upper	ICC 95%CI 95%CI	0.78 0.60 0.96	0.56 0.24 0.87		0.76 0.56 0.95	0.06 0.40 0.51	0.66 0.37 0.95	0.80 0.64 0.96	0.95 0.90 0.99	0.91 0.84 0.99
		Collection method		OC-Auto Sampling (+4°C)	OC-Auto Sampling (+30°C)	OC-Auto Sampling	(screening)	Hemotrust (RT)	One-Step FOB (RT)	Spec. Coll. Cont. A (RT)	GenSaver (RT)	GenCollect (RT)		Collection method		OC-Auto Sampling (+4°C)	OC-Auto Sampling (+30°C)	OC-Auto Sampling	(screening)	Hemotrust (RT)	One-Step FOB (RT)	Spec. Coll. Cont. A (RT)	GenSaver (RT)	GenCollect (RT)

Table S5 :

 S5 Overview of all bacteria reported in the studies included in this systematic review and their direction of association with cancer risk

	RESEARCH ANALYSED DEFINITION OF THE SAMPLE SIZE MEAN AGE OF ORIGIN OF EXCLUSIONS CONFOUNDERS/ADJUSTMENTS Cameron, 2017 Sputum Lung cancer 10 patients referred Cases: 58.8 (14.8) UK Not available Smoking status, Antibiotic use, years [44-84] with CIN1 and 15 °insufficient data on monthly family income, parity antibiotics within preoperative divided by the height in localization, tumor esophagitis as the allowed the full China, and years Loke, 2018 Gut Colorectal cancer 17 Malaysian left-sided CRC: Malaysia pre-operative radiation, gender, race, age, type of tumor, polyps and 130 inflammatory bowel Ahn, 2013 Gut Colorectal cancer 141: 47colorecal CRC: 62.9 years CRC: 72.3%white, °less than 100mg of CRC and HC were matched for 54.8 years [±SD: systemic infection without cancer. No adjustments Mira-Pascual, Gut Colorectal cancer 20: 15 case (7tumor, CRC: 71.1 years Oviedo, Asturias °patients with Cases and HCs were matched on 53.2 years [±SD: hypertension °family antibiotics or probiotic 32; HC: 34 years (42-77) diets °taking gender and BMI. cancer (HNPCC)) oestrogenic or other family history, Lower urinary TC: 48.47 6.52 China Not available gender, age, BMI, T status, N Not available China Not available Not available confirmed squamous quids and cigarettes, and dental conditions not mentioned. HIV or other secondary years [±SD 14.43], Most of case USA current smokers and gender and age time of saliva active bacterial or viral MICROBAL COMPOSITION OUTCOME ARTICLE MICROBIOME CASES PARTICIPANTS SUBJECTS Case control microbiome with possible LC, of CO level (ppm), FEV1% of Urbaniak 2016 Breast tissue Breast cancer cases 81 pat. of whom 13 Age range [19-90 Canada Adjustments for contamination with CIN2 or CIN3, the questionnaire or in and oral contraceptive use. recent four weeks, chemotherapy, 230 meters squared) > 30; differentiation, lauren typing, diseased controls visualization of the validation in 16 Mexican cases: Case control microbiome patients 60.9 (95% CI: chemotherapy location, stage, biofilm controls disease (IBD) or other Case control microbiome cases cancer (CRC), 94 [±SD: 12.9] HC: 25.5 % black, 2.1% lyophilized feces age, sex, BMI and race before 1.3] made. 2014 microbiome cases 8 polyp), 5 HCs [SD: 10.1] Polyp: (Spain) hereditary syndromes the basis of the design for age 5.4] history of CRC therapies within the medications at the time 37-78 years Caucasians, USA Not mentioned Not mentioned hormone prescription tract symptoms, Number of HCs: 46.03 7.96 status, M status cell carcinoma located care habits organism (bacteria and thin group: 48.60 >=70 years users of antibiotics collection. infection in another ALTERATION QUALITY OF THE ARTICLE All buccal samples: mainly Bacteroidetes, Firmicutes, Clear differences between the sputum samples of (LC) Selection: **** Comparability: ** All groups: mainly Proteobacteria. (ECCA) ↑ Significant separation between (ECCA) and (BBP) Selection: **** Comparability: ** (IBC) ↑ Enterobacteriaceae, Bacillus and Staphylococcus Significant differences in microbial composition of Selection: **** Comparability: ** and L. crispatus ratio between (H) and (CIN). (GC) ↑ Lactobacillus, Escherichia-Shigella, Markedly increased bacterial load in GC ; Selection: *** Comparability: * (GC) ↑ 21 bacterial taxa, including Peptostreptococcus members of oral pathogenic taxa were over-represented Selection: *** Comparability: ** 11/16 samples dominated by H. pylori. (GC) ↓ H. pylori, Significant differences between the (GC) and (H) Selection: ** Comparability: (CRC) ↑ Bacteroides and Dorea ; Parcubacteria) Total number of OTUs and the Shannon and Simpson Selection: **** Comparability: ** (CRC) ↑ Firmicutes and Fusobacteria ↓ Chao1 Richness Index for normal, adenoma, and Selection: *** Comparability: (CRC) ↑ Bacteroides fragilis, Fusobacterium nucleatum, Identification of a group of bacteria that is consistently Selection: **** Comparability: ** (CAC) ↑ Enterobacteriacae family and Sphingomonas Chao1: higher in H compared to CAC and normal mucosa Selection: **** Comparability: ** (CRC) ↑ Fusobacterium nucleatum spp. (IC/PA) ↑ observation of dynamic shifts in microbial composition, Selection: **** Comparability: ** (CRC) ↑ families Erysipelotrichaceae, Shannon: NSChao index: NSPCA analysis based on Selection: *** Comparability: ** (CRC) ↑ Clostridium lecithinase negative, and The total amount of bacteria is more abundant in the high Selection: *** Comparability: * No significant findings No clearly discernible statistically significant Selection: **** Comparability: * (CRC) ↑ Fusobacteria ↑ Families Eubacteriaceae, Shannon: NS Chao 1 index (richness): NS Selection: ** Comparability: ** (CRC) ↑ phylum Fusobacteria ↑ Shannon: NS Richness: lower in cases Selection: ** Comparability: * (CRC) ↑ OTUs associated with Fusobacterium, Relative abundance data from the human gut Selection: **** Comparability: ** (CRC) ↑ families Porphyromonadaceae, Shannon index: NS Chao index: NS Selection: *** Comparability: ** (CRC) ↑ phylum Fusobacteria ↑ genera Chao and ACE indices: NS Shannon: higher in controls Selection: * Comparability: ** (CRC) ↑ Escherichia coli strains ↑ E. coli phylogroup Not mentioned Selection: **** Comparability:-(CRC) ↓ Ruminococcus, Parabacteroides, Principal Component Analysis (beta diversity): NS Selection: ** Comparability: ** (postmenopausal BC) ↑ 38 species including Escherichia Relative species abundance in gut microbiota did not Selection: **** Comparability: ** (PC) ↑ Bacteroides and Streptococcus species Mean beta-diversity distances Selection: **** Comparability: ** (BC) ↑ 19 genera ↓ 8 genera Eight metabolites combined with 5 genera were more Selection: **** Comparability: ** (HNSCC) ↓ Corynebacterium, Kingella oral microbiome may be associated with subsequent Selection: *** Comparability: ** (OC) ↑ Bacillus, Enterococcus, Parvimonas, the oral microbiome compositions revealed Selection: *** Comparability: ** (OCC/OPC) ↑ Actinobacillus, Actinomyces, the oral microbiome is able to predict the presence of Selection: **** Comparability: ** (OSCC) ↑ Dialister ↓ Scardovia Alterations following tooth loss, itself a major risk factor Selection: **** Comparability: ** All (FAC) samples: mainly Bacteroidetes, Firmicutes, (FAC) ↓ diversity, microbiota was getting less diverse the Selection: * Comparability: (OSCC) ↑ numbers of microbes, especially aerobic The majority (68%) of the cultures from all groups: Selection: **** Comparability: ** (LC) ↓ Treponema and Filifactor in BALF ↓ Filifactor Patients with lung cancer have lower microbial Selection: **** Comparability: ** (CRC) ↑ Treponema denticola and Prevotella multiple oral bacterial taxa were associated with Selection: **** Comparability: ** Cancer group: ↓ Neisseria, Haemophilus, Fusobacterium Significant ↑ mirror-like tongues and thicker tongue Selection: **** Comparability: ** All samples: mainly Bacteroidetes, Firmicutes, Significant difference in OTU diversity and richness. Selection: **** Comparability: ** (PC) ↑ Porphyromonas gingivalis and The oral microbiota may play a role in the aetiology of Selection: *** Comparability: ** All samples: mainly Firmicutes, Proteobacteria, Significant differences between (PC) and (H). Using Selection: *** Comparability: ** All samples: mainly Firmicutes, Proteobacteria, Significant difference in the thickness of tong coating. Selection: **** Comparability: ** (T) ↑ Staphylococcaceae, Staphylococcus ↓ possible pathophysiological correlation between the Selection: *** Comparability: * Reference healthy controls (H). cancer cancer pat.: 75 from who 50 pat. psychological problems status, oncogenic HPV infection, controls platelet medications, Cohort microbiome without =weight in kilograms BMI, complications, tumor mid-esophageal colonoscopies that Case control microbiome and 54 controls from median age 67 adenoma data from Denmark enrollment benign intestinal (8.9) colorectal adenoma, data collection Case control microbiome cases [±SD: 1.1] HCs: taken antibiotics °any significantly older than patients week of stool collection Case control microbiome cases [±SD: 8.7] HCs: such as diabetes or matched sex, age, and BMI tumor who had been on Case control microbiome cases swab samples: CRC: 86) HCs: 56 diseases or particular the basis of the design for age, years [±SD: 15.2)] polypoid colorectal months; and any H: 65 (60-68.5) in the last 6 mo Abnormal prostate examination, 48.65±6.61 status pathologically consumption of alcohol, betel immunocompromising 49-82]. age of controls is 6.03 co-infected with HCV, group: 53.78 saliva samples. within 5 years from the three months or an Aviles-Jimenez, 2016 Liu, 2018 Airway Lung cancer 24 lung cancer Lung cancer: China chemotherapy, Gender, Age, BMI, Smoking, which four were Controls: 73.3 Predicted Case control microbiome benign breast years] Benign from the skin, batch effects and 72 healthy specimens. upper gastrointestinal normal, 247 use of antibiotics, tumor stage, HP infection, (DC) and 37 healthy entire colon and patients and 24 median age 62 54.2-67.6) treatment or had a past cancers, (d) patients healthy controls 58.5 years [±SD: others ; HC: 86.2% available the test. Kanazawa, 1996 Gut Colorectal cancer 27: High risk group: High risk group: Tochigi, Japan °patients with lesions CRCs and HCs were matched on Case control 63.3 [±SD: 13.1] (familial adenomatous and gender. °antibiotic use within 3 previous six weeks of sample collection, or within 12 months negative prostate biopsies, In the (LSCC) China °pat. with a history of No corrections, but each patient in the oral cavity, (FAC) was 41 Germany / fungi) infection years [±SD 8.56], (PC) 71.1 years, U.S.A. °participants part of the body Proteobacteria, and (H). Exposure: ** Fusobacteria, Acidobacteria was observed. Exposure: ** breast tissue in healthy Exposure: ** Nitrospirae, Burkholderia Exposure: ** stomatis, Streptococcus and formed strong co-Exposure: ** Propionibacteirum spp., tissue. Exposure: ** ;Bacteroides, higher in CRC patients Exposure: ** Proteobacteria cancer group were Exposure: ** Porphyromonas associated with CRC and Exposure: ** genus (CAC) ↓ near the tumor in CAC Exposure: ** Atopobium parvulum and gene abundance in gut Exposure: ** Prevotellaceae, unweighted UniFrac: Exposure: ** Lactobacillus ↓ yeast risk group Exposure: *** relationships between the Exposure: *** clostridiales Family XI PCoA based on the Exposure: ** Fusobacterium ↓ genera (However, these differences Exposure: *** Porphyromonas, microbiome differentiates Exposure: *** Fusobacteriaceae and unweighted Unifrac PCA Exposure: ** Peptostreptococcus, Exposure: *** D Exposure:*** Pseudoflavonifractor and Exposure: *** coli, Klebsiella sp_1_1_55, differ significantly between Exposure: ** (BrayCurtis/Unweighted Exposure: ** effective in distinguishing TC Exposure: ** risk ofHNSCC,with the Exposure: ** Peptostreptococcus, and significant differences Exposure: ** Aggregatibacter, OCC and OPC with sensitivity Exposure: ** for oral cancer, are likely a Exposure: ** Proteobacteria and more the samples are Exposure: ** bacteria. Lesions site of the mutagenic levels of Exposure: ** in saliva diversity than healthy Exposure: ** intermedia, Actinobacteria, subsequent CRC risk Exposure: ** and Porphyromonas (i.e. coating in the cancer group. Exposure: ** Protebacteria, Fusobacteria, (ESCC): ↓ microbial Exposure: ** Aggregatibacter pancreatic cancer. Exposure: ** Bacteroidetes and Streptococcus and Neisseria Exposure: ** Bacteroidetes, (GCtk) significantly ↓ Exposure: ** Lactobacillales, composition of the local Exposure: ** Hu, 2015 Case control microbiome patients with 60.58 radiation therapy, Tumor characteristics eventually (7.2) tumors, 45 invasive patients median controls (H). endoscopy, prior peritumoral and 229 probiotics, prebiotics, antibiotic use, PPI use, Pre-controls (HC). showed no significant controls from years right-sided CRC: history ofCRC or had a family history of (HC) 12.9] white, 11.7% black, Case control microbiome cases 13 male patients 60 years [±SD:7.3] that might have been the basis of the design for age HCs: 52.6 [±SD: polyposis (FAP) and months prior to the °history of Familial used antibiotics within Premenopausal China diarrhea, diabetes, Age, BMI, Age at menopause, active surveillance, Gleason group, 11 antibiotic use in the is his or her own control. Not available USA Not available Clinically normal samples oropharynx, years, (FAC2) was and the 7 healthy other ages are undergoing active Italy self-reported lower diet, lifestyle (LC) ↑ Streptococcus lung cancer-associated Selection: **** Actinobacteria, and and Planctomycetes, ↑ controls vs. malignant fungorum,and anginosus, Parvimonas micra, occurrence network in GC Staphylococcus spp., and Phascolarctobacterium, 66.4±32.6, 61.9±29.3, and asaccharolytica, Parvimonas shows potential in the Fusobacterium and Actinomyces odontolyticus microbiota and metabolites Coriobacteriaceae, Significant fecal profile of the bacterial Incertae sedis and unweighted Unifrac: Eubacterium ↑ B. fragilis, exhibited p>0,5 after Enterobacteriaceae ↓ OTUs healthy colons from peptostreptococcaceae ↓ analysis: Significant Fusobacterium ↓ class Holdemania ↓ family Prevotella amnii, premenopausal breast UniFrac): Patients with patients from H strongest links for Slackia between epithelial precursor Capnocytophaga, and specificity of 100 and result of severe ecological Tenericutes. All (FAB) and moving into a tumourous (OSCC): ↑ amount of acetaldehyde. No correlation controls in both saliva and Bifidobacteriaceae, Fusobacterium Significant bacterial changes. and Actinobacteria (in order diversity. ↓ Streptococcus ~ actinomycetemcomitans ↓ Actinobacteria. (PC1): ↑ 31 as biomarker: 96.4% Actinobacteria, Fusobacteria, microbial community Streptococcaceae, microbial niche and the 2016; Gao, 2015; McCoy, 2013; Gao 2017; Xu 2017 Shin, 2017 Schmidt, 2014; Yost, 2018 Fan, 2016; Fan, 2018 Not available MEASUREMENT OF THE MICROBIOME 16S rRNA gene DNA extraction and 16S rRNA gene analysis of the V1-V2 region. DNA extraction and 16S rRNA gene analysis of the V4 region + PCR. DNA extraction and 16S rRNA gene analysis of the V6 region. 16S rRNA gene sequencing (V1-V3 region) 16S rRNA gene sequencing (V4 region) DNA extraction and high-throughput RNA sequencing (no further details 16S rRNA gene sequencing (V4-V5 region) 16S rRNA gene sequencing Shotgun Metagenomic sequencing 16S rRNA gene sequencing (V3-V4 region) Shotgun Metagenomic sequencing PCR amplification of the V1-V3 region of bacterial 16S rDNA was performed culture culture 16S rRNA gene analysis of the V3 region (454 pyrosequencing) whole genome shotgun sequencing (Illumina) 16S rRNA gene analyis of V4 region (Illumina) 16S rRNA gene analysis of V1-V3 region (454 pyrosequencing) 16S rRNA gene analysis of the V3 region (454 pyrosequencing) culture and PCR whole genome shotgun sequencing (Illumina) Shotgun Metagenomic sequencing 16S rRNA gene sequencing (V1-V2 region) 16S rRNA gene sequencing (V3-V4 region) 16S rRNA gene sequencing (V3-V4 region) 16S rRNA gene sequencing (V4 region) 16S rRNA gene sequencing (V6-V8 region) 16S rRNA gene sequencing (V4 region) DNA extraction and 16S rRNA gene analysis of the V1-V2 region, as well as Gas chromatography + several agar media. 16S rRNA gene sequencing (V4 region) 16S rRNA gene sequencing (V4 region) DNA extraction and 16S rRNA gene analysis of the V2-V4 region. DNA extraction and 16S rRNA gene analysis of the V3-V4 region. 16S rRNA gene sequencing (V3-V4 region) DNA extraction and 16S rRNA gene analysis using universal primers + DNA extraction and 16S rRNA gene analysis of the V2-V4 region. Massive ultradeep pyrosequencing Microbiota body site Bile duct Oral Oral Oral Oral McCoy, 2013 ( Case control samples Gut microbiome Colorectal cancer cases 10 subjects; 19 samples: 10 samples on-tumor and 9 non-Zhu, 2018 Gut Breast cancer 18 premenopausal Case control microbiome with cancer, 41 without cancer) Feng, 2019 Case control Gut microbiome Thyroid cancer 30 TC patients and 35 healthy controls Gong, 2013 Case control Laryngeal microbiome Laryngeal cancer cases 29 pat. with laryngeal squamous Hu, 2016 Case control Oral microbiome Oral cancer 16 patients with OSCC, 10 with OLK, and 19 HCs Banerjee, 2017 Oral Oral cancer 100 OCSCC patient Case control microbiome 151 controls healthy tumour-free subjects that matched the locations of tumouradjacent (OSCC), 40 healthy controls (22 m. and 18 fem.) Henrich, 2014 Case control Oral microbiome Oral cancer cases 2 pat. with Fanconi Anaemia and oral year follow-up of patients and controls nested in cohort study) controls (H). 11 pat. with an oropharyngeal squamous cell carcinoma (OPSCC): 25 healthy subjects cases, and a thin group with 5 cases. Olson, 2017 Case control Oral microbiome Pancreatic cancer 40 newly diagnosed PDAC patients, 39 IPMN patients, and 58 controls, chronic pancreatitis (CP). Torres, 2015 Case control Oral microbiome Pancreatic cancer cases 108 subjects: 8 with pancreatic cancer vocal cord polyps Cavarretta, Prostate Prostate cancer 16 participants: ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME Liu, 2018 (LC) Lung cancer Protected bronchial Airway Hosgood, 2014 Case control (LC) Lung cancer, (H) Healthy controls. Buccal and sputum samples Lung microbiome Aviles-Jimenez, 2016 Case control (ECCA) Extrahepatic cholangiocarcinoma (BBP) benign pathology of the Epithelial cells of the bile duct Bile duct microbiome Urbaniak 2016 Case control (BBC) Benign breast cancer (IBC) Invasive breast cancer Breast tissue Breast tissue microbiome Wang, 2016 Case control (GC) Gastric cancer gastric mucosa Gastric microbiome Coker, 2018 Case control (SG) Superficial gastritis (AG) atrophic gastritis gastric mucosa Gastric microbiome Seo, 2014 Case control samples (GC) Gastric cancer (H) The adjacent normal gastric mucosa. Gastric tumor tissue and adjacent normal mucosa Gastric microbiome Thomas, 2016 Case control (CRC) Colorectal cancer (H) Healthy controls tissue samples Gut microbiome Xu, 2017 Case control (CRC) Carcinoma (A) Adenoma (H) Healthy controls Biopsy on the mucosa Gut microbiome Dai, 2018 Case control (CRC) Colorectal cancer Stool Gut microbiome Richard, 2018 Case control (CAC) Colitis associated cancer, (SC) Sporadic cancer, mucosa on the tumor site and normal mucosa near the tumor Gut microbiome Yachida, 2019 Cohort (CRC) Colorectal cancer (IC) Intramucosal carcinomas Stool Gut microbiome Chen, 2012 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Kanazawa, 1996 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Mastromarin o, 1978 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Wu, 2013 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Yu, 2015 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Zackular, 2014 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Chen, 2012 Case control (CRC) Colorectal cancer (H) Healthy controls Swab samples were collected from each subject prior to bowel cleansing. All Gut microbiome Gao, 2015 Case control (CRC) Colorectal cancer (H) Healthy controls After a standard bowel cleansing, colorectal tissue samples were Gut microbiome Kohoutova, 2014 Case control (CRC) Colorectal cancer (H) Healthy controls The usual bowel preparation was either polyethylene glycol or sodium Gut microbiome Warren, 2013 Case control samples (CRC) Colorectal cancer total RNA was isolated from frozen surgical sections of CRC and matched Gut microbiome Zhu, 2018 Case control (BC) Breast cancer Stool Gut microbiome Liss, 2018 Case control (PC) Prostate Cancer (H) Controls Rectal swab Gut microbiome Feng, 2019 Case control (TC) thyroid carcinoma (H) healthy controls Stool Gut microbiome Hayes, 2018 Nested case-control (HNSCC) Head and neck squamous cell carcinoma mouthwash samples Oral microbiome Oral microbiome Oral microbiome oral microbiome Oral microbiome Oral microbiome Oral and lung microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Prostate microbiome * * Number of studies 1 1 1 2 2 Lee, 2017 Case control (OC) oral cancer saliva samples Lim, 2018 Case control (OCC) Oral cavity cancer and (OPC) oropharyngeal Oral rinse samples Börnigen, 2017 Case control (OSCC) Oral squamous cell carcinomas Oral rinse samples Henrich, 2014 Case control (FAC) Fanconi Anaemia and oral squamous cell cancer. (FAB) Oral swab Marttila, 2013 Case control (OSCC) Oral squamous cell carcinoma. (OLD) Oral lichenoid Microbial sample of the oral mucosa Wang, 2019 Case control (BALF) fluid bronchoalveolar lavage (LC) Lung cancer Buccal (saliva) and lower respiratory tract BALF samples Yang, 2019 Case control (CRC) Colorectal cancer mouth rinse samples Han, 2016 Case control (CRC) Colorectal cancer. (LC) Lung cancer. (GC) Gastric cancer. Tongue coating samples and images. Chen, 2015 Case control (ESCC) Esophageal squamous cell carcinoma. (DYS) Dysplasia. Saliva Fan, 2018 Case control (PC) pancreatic cancer oral wash samples Farrell, 2012 Case control (PC) Pancreatic cancer. (H) Matched controls. Saliva Hu, 2015 Case control (GC) Gastric cancer, (H) Healthy controls, (GCtn) Gastric Tongue coating samples and images Cavarretta, 2017 Case control (T) Tumor (N) Non tumor radical prostatectomy-specimens Supplementary Taxonomic level -Phylum RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS smoking female RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS 15 invasive breast [33-70] Breast RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS neoplasia (CIN), dependencies, or smoking status, alcohol drinking RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS and 8 HP-negative coagulants, anti-RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS Liu, 2019 Gastric Gastric cancer 276 GC patients GC: 61.11 ± 11.82 China body mass index (BMI age, gender, weight, height, RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS (ED), 17 pat with RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS complete RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS Yu, 2017 Gut Colorectal cancer 74 patients with CRC Chinese cases: China/Denmark all patients with validation of the results with RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS weeks leading up to RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS 62 patients with Controls: 58.6 colorectal cancer, RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS were insufficient for RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS Fukugaiti, 2015 Gut Colorectal cancer 17: 7 CRC, 10 HCs CRC: 65.4 years Sao Paulo, Brazil °patients who had Patients with cancer were RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS were used within 1 RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS Wu, 2013 Gut Colorectal cancer 39: 19 CRC, 20 HCs CRC: 58.3 years Beijing, China °complicating diseases The HCs were selected based on RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS samples 10cm off radiotherapy. °patients RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS Chen, 2012 Gut Colorectal cancer 66 subjects with gut CRC: 65 years (37-China °diabetes, infectious CRCs and HCs were matched on MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS 13.1)] HCs: 52.6 hereditary non MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS prescription within 6 MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS PC: 66.5 (62-70) USA Any antibiotic therapy age, BMI, Diabetes, Race, PSA, MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS Control: MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS oral human papillomavirus-16 MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS Not available Taiwan (i) diagnosis of sex, age, education, MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS they had any MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS 67 years [range MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS age of 66. Mean MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS Controls: 48.20 ± anticancer treatment MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS controls. Thick MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS questionnaire or no MEAN AGE OF PARTICIPANTS ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS other malignancies ORIGIN OF SUBJECTS EXCLUSIONS CONFOUNDERS/ADJUSTMENTS use during the previous MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE MICROBAL COMPOSITION ALTERATION OUTCOME QUALITY OF THE ARTICLE Reference Kasai, 2016; Vogtmann, 2016; Wu, 2013; Yu, 2015; Zeller, 2014; Alexander, MEAN AGE OF PARTICIPANTS MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME MEASUREMENT OF THE MICROBIOME Microbiota body site Gut RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE Liss, 2018 Gut Prostate Cancer 105 samples (64 RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE Hsiao, 2018 Oral Oral cancer 138 OSCC cases and RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE three buccal sites in RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE cell carcinoma RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE Cohort study (1 (HNSCC), 25 healthy RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE cirrhosis RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE tick group, with 9 RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE (H2) 27 pat. with RESEARCH ARTICLE ANALYSED MICROBIOME DEFINITION OF THE CASES SAMPLE SIZE 28 subjects with RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME ANALYSED MICROBIOME * Number of studies 10 RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN RESEARCH ARTICLE Abbreviations for cases & controls ANALYSED SPECIMEN Cancer and Result Acidobacteria Increased in ECCA Actinobacteria Increased in GC Taxonomic level -Phylum Cancer and Result Fusobacteria Increased in CRC Fusobacteria Increased in HNSCC Fusobacteria Increased in OC Fusobacteria Associated with low risk of PC or decreased unilateral lobar undergoing surgery or diagnosed with LC), breast cancer and 23 age: 36 years IBC Mannell, 1983 Esophageal Esophageal cancer 101 subjects: 50 pat. Not mentioned. South Africa / gastric surgery or HP tumoral tissues or synbiotics in the operative chemotherapy, Baxter, 2016 Gut colorectal cancer 490 patients: 120 29-89 years with Canada and USA Excluded if they had No adjustment clinical alterations were Denmark. 62.8 (95% CI: inflammatory bowel colorectal cancer in 2.1% others (not (who had previously ; HCs: 62.2 years overlooked at the initial and gender 15.2] hereditary non study participation Adenomatous Polyposis at least one month of cases: 37.06 ± ulcerative colitis, ethnicity score, Number of aberrant patients were previous 3 months °pat. adjacent to the cancers are hypopharynx and 27 years, (FAB) Controls: hypertension, controls: 52.14 CPS II cohort: USA Not available Controls were matched to cases not (clearly) chemotherapy or urinary tract symptoms (H) ↑ Staphylococcus microbiota profile is distinct Comparability: ** Fusobacteria. Sputum of (LC): Total NOS score: 8 Methylophilaceae, Total NOS score: 8 disease. Total NOS score: 8 Lachnospiraceae Total NOS score: 6 Slackia exigua and Dialister compared with other Total NOS score: 7 Corynebacterium spp. ↑ Total NOS score: 4 Parabacteroides, Total NOS score: 8 87.8±37.5, respectively, Total NOS score: 4 micra, Prevotella intermedia, diagnosis of CRC across Total NOS score: 8 Ruminococcus genus Total NOS score: 8 during multistep CRC Total NOS score: 8 Peptostreptococcaceae ↑ Total NOS score: 7 Total NOS score: 7 flora and colon cancer were Total NOS score: 8 Staphylococcaceae, ↑ Significant Total NOS score: 6 Parvimonas micra, correcting for colonoscopy) Total NOS score: 6 associated with Bacteroides, carcinomas Total NOS score: 9 Bifidobacteriaceae and Total NOS score: 7 Flavobacteria, Total NOS score: 6 Total NOS score: 7 Ruminococcaceae ↑ Total NOS score: 7 Enterococcus gallinarum, cancer patients and Total NOS score: 8 prostate cancer are Total NOS score: 8 Total NOS score: 8 larynxcancer and those with Total NOS score: 7 lesion and cancer patients Total NOS score: 7 Fusobacterium, 90% Total NOS score: 8 disruption due to habitat loss Total NOS score: 8 (H): mainly Actinobacteria, state. Total NOS score: 3 anaerobic bacteria. Lesion between acetaldehyde levels Total NOS score: 8 BALF samples. Total NOS score: 8 Bacteroidetes, Prevotella Total NOS score: 8 periodonticum, Haemophilus Total NOS score: 8 of frequency). (ESCC): ↑ ↑ TNM stage, ↑ Total NOS score: 8 Fusobacteria, Leptotrichia Total NOS score: 7 species/clusters and ↓ 25. sensitivity and 82.1% Total NOS score: 7 and TM7. (GC): ↑ diversity. Total NOS score: 8 Streptococcus presence of the tumor itself Total NOS score: 6 Schmidt, 2014 Aviles-Jimenez, 2016 masses (paired Controls: 56.61 other treatments for and six had no LC disease free controls patients median Case control microbiome cases with esophagus eradication treatment previous month; sample collection Case control microbiome CRC, 198 adenomas, a median of 60 undergone surgery, included. Allali, 2018 Case Gut Colorectal cancer 11 CRC patients and Cases: 52.8 ± 54 Morocco no gastrointestinal age, gender, tumor location, 47.6-78.1) disease first-and second-significant different undergone surgery [±SD: 7.4] preoperative polypoid colorectal Yu, 2015 Gut Colorectal cancer 128: 74 CRC, 54 HCs CRC: 67 years; China °use of antibiotics colonoscopy (when correcting (FAP) °patients with sample collection 5.23 Crohn's disease, or microbiome profiles younger than or with a history of active referred here as "matched larynx (ICD-10 codes: median age 58 USA Case: older than 17 age-and gendermatched was 27 years, diabetes, obesity, years [±SD 10.63]. 73.7 by cohort, age (5-year), sex, race mentioned. radiation therapy or or a pathologic from that found in healthy Exposure: ** ↑ Granulicatella, Fusobacterium, Prevotella, (GC) ↑ Lactococcus, samples from lung cancer before after one year. age: 62 years carcinoma (EC) and Yu, 2017 Gastric Gastric cancer 80 paired samples median 62 (58-China Not available Alcohol and pickled vegetable preoperative and 172 without years radiation, or Amitay, 2017 Gut Colorectal cancer 500 participants, 63.2 years Germany inflammatory bowel sociodemographic, nutritional control microbiome 12 healthy disorders, no antibiotic BMI, dietary data, family history Mori, 2018 Case Gut Colorectal cancer Normal biopsies: 18 Not available Italy Subjects with family Demographic information, clinic degree relatives and no between CRC and for sigmoid colon examination °neither cancer (HNPCC) Case control microbiome cases HCs: 63 years within the past 3 for colonoscopy, the differences inflammatory bowel Flemer, 2016 Gut Colorectal cancer Cork °personal history of Age matched study design Postmenopausal other infectious Not available China other ethnicities except adjustment for sex and age exactly 60 years bacterial or viral controls C00-C10, C12-C14, years (IQR 53-66) years and not newly controls (FAB2) was 33 metabolic syndrome, (CRC) 55.45 years China °controls with Adjustments for age, sex, BMI, PLCO cohort: 63.8 (white, other) and calendar year use of antibiotics two International Prostate controls, and the altered Abiothrophia and Helicobacter and Veilonella, and cancerous site and bronchoscopy, Aviles-Jimenez, Bile duct Cholangiocarcinoma 200 subjects: 100 (ECCA) 66.2 years Mexico Case-control matching for sex, Healthy controls 51 healthy controls Case control microbiome (non-malignant and 67) consumption, history of cancer, chemotherapy, colonic lesions chemotherapy for Case control microbiome including 46, 113, disease or had a history and lifestyle factors individuals Controls: 49.3 ± use during the last 3 of cancer control microbiome subjects history of cancer data, comobordities data family history of HC); Washington cancer and who the high risk or control Ohigashi, 2013 Gut Colorectal cancer 152: 93 CRC, 49 HCs CRC: 68.9 years Tokyo, Japan °history of colectomy Univariate analysis (the 3 groups months °on a in richness and alpha diversity disease °undergone Case control microbiome cases mentioned CRC, IBD or IBS. Not mentioned Canada Not mentioned Not mentioned cases: 57.45 ± diseases Han Chinese; age under while 18 patients infections in other C32); (ii) no previous diagnosed with a measures of tobacco, alcohol years and (H) irritable bowel [±SD 1155], (LC) digestive diseases, smoking status, hypertension of oral wash collection. weeks prior to saliva Symptom Score were cancer-associated microbiota Total NOS score: 8 Streptococcus. No difference Campylobacter, ↓ (CC) characterised by the contralateral antibiotics in the prior 3 2016 microbiome cases pat. with [range 50-82 age (± 5 years) and place of median age: 53 (H). matched tumor tumor grade and stage, radiotherapy, or other current CRC prior to 110 and 231 of CRC 46 months and those who 21 subjects were neoplastic polyps or DC developed new groups had been Case control microbiome cases (22diagnosed with [±SD: 12.1] or proctectomy did not differ significantly in age, vegetarian diet exhibited p>0,5) previous colorectal °treated with 7.41 antibiotics, steroid 18 years; living in were strictly parts of the body ° cancer diagnosis; (iii) histologically confirmed and marijuana use and medical 45.8 years [range syndrome, non-55.14 years [±SD respiratory diseases, and diabetes. Confounders: age, sex, race, collection °participants excluded is not restricted to tumor in the lung microbiota Nesterenkonia, Rothia and presence of Sneathia spp. noncancerous site) months Case control extrahepatic years], (BBP) 53.1 residence + clinical variables incl. years Wang, 2016 Gastric Gastric cancer 315 patients, 55.8±13.5 years China endoscopic findings of Not available tissues) collected metastasis, survival (days) biological treatment baseline samples or individuals with had been recently diagnosed with hereditary syndromes Amiot, 2015 Gut Colorectal cancer 55: 33 advanced advanced France °previous history of No statistical differences in second or third receiving anticancer adenoma in the past adenoma: 66.6 °obstructive CRC gender, BMI or in past histories °invasive medical surgery antibiotics in the month hormones, Chinese Shanxi Province for less older than 60 controls not free of age 20 years; (iv) Han squamous cell and dental history. 32-43]. alcoholic fatty liver 9.80], (GC) 56.20 oral disease, malignant body mass index (BMI), smoking with invasive surgery in Finland unsuitable lesion type Not available tissue between the controls from Mesorhizobium. and Fusobacterium spp. and and 18 healthy cholangiocarcinoma years [range 23-the time of evolution of lithiasis, Audirac-Cervical Cervical cancer 32 subjects: 8 with (CC) 43 years Mexico °insufficient reads Adjustments for age, parity, Case control microbiome including 212 peptic ulcer, polyps, or from GC patients before gastrectomy had inflammatory colorectal cancer, diagnosed and had not ADK, 21 with HRA, in first degree relatives Case control microbiome cases colorectal neoplasia neoplasia: 59.4 colorectal surgery, gender, BMI, history of polyps, colonic epithelial drugs, antibiotics, 3 years, and 27 did [±SD: 9.2] ; non-°patients treated with of cholecystectomy, intervention within the Allali, 2015 Gut Colorectal cancer 22 subjects; 44 63.6 years [42-88] US Not mentioned No significant confounding by prior to surgery herbal medicine than 10 years; years. In (H), 131 cancer °controls with ethnicity and (v) ability carcinoma of the oral Controls: China the participant age, gender, BMI, smoking disease, coeliac disease years [±SD 10.24] tumor and cancer status, alcohol consumption and the past year. (dysplastic nevus or (LC) ↑ Streptococcus and several transcriptomic Selection: **** Laibin and of Reshui, but a absence of organisms from controls undergoing (ECCA) and 100 pat. 83]. H. bilis/ H. hepaticus infection, Chalifour, 2016 microbiome cases cervical cancer and [±SD 11],(SIL) 40 °patient´s recruitment contraceptive method and HPV-patients with any other local lesions ; Yu, 2017 Gastric Gastric cancer 160 gastric cancer Chinese China/Mexico Chinese cases: <18 Age, gender, tumoral regions of Aviles-Jimenez, Gastric Gastric cancer cases 15 pat.: 5 pat. with (NAG) 44 years Mexico °pat. with Adjustments for sex and age. bowel disease, known advanced yet started treatment 18 with LRA, 14 with under 60 years of age, (20CRC and 13 with years [± SD: 6.9]; inflammatory or familial history of neoplasia.), 14 male medicines containing not have adenomas) adenoma: 65.6 antibiotics at the point appendectomy or breast past 3 months °past Prospective microbiome cases samples: 22 samples region of the colon, age, gender. CRC: 68 years Shanghai, China °age >75y °usage of Age, BMI and gender are not Controls: 43.8 Finland Not available Tumor site, Age, Gender (including oral, exposure to antibiotics, patients were evidence of epithelial to provide informed cavity or oropharynx 56.9±6.1 manifested other basic history, drinking history and liver cirrhosis and (H) 53.57 related symptoms in history of diabetes (GC) 57.46 years China °controls with stomach Adjustments for other), or insufficient Veillonella signatures previously Comparability: ** difference in the cancer pat. (CCA) dominated by Gastric tissue microbiome Selection: **** the Bifidobacteriaceae bronchoscopies with benign sex, age or body mass index. Case control HPV+ (CC), 4 with years [±SD 14], on the same day of genotype. chronic gastritis and Patients who showed Case control microbiome patients with 80 cases:60.8 years years old, with cancer the stomach, country 2014 microbiome non-atrophic [range 32-76], immunodeficiency hereditary non-adenomas, non-Burns, 2018 Gut Colorectal cancer 88 tissue samples Not available USA Not available one tumor and one normal HP. and (e) patients had advanced adenoma) HCs: 52 years infectious bowel polyps/CRC/cancer, diabetes HCs living Lactobacillus or [±SD: 13.5] of hospitalization °feces cancer). history of any cancer nested case on tumor, 22 {±SD: 7.3] HCs: 71 antibiotics and significantly different between (19-65) intramuscular, or probiotics, younger than or dysplasia. Normal: 52 ± 14 Taiwan Not available sex, age, alcohol consumption, consent Control: older than 17 93 of the Finland °ex-smokers with a Adjustments for age, smoking, LAC: 54.8±10.7 pulmonary diseases, antibiotics, probiotics years [±SD 8.32]. the last two years. (PAD1) 73.7 years U.S.A. °cases with a history of Case-control matching for age, [±SD 8.43], (H) discomfort over the chemotherapeutics and surgery, amount of DNA in the identified as relevant to lung Exposure: ** from those two villages. Dietziaceae, was clearly distinguishable Comparability: ** family. (CC) ↓ Lactobacillus Tsay, 2018 Case Airway Lung cancer 39 subjects with 61.2 years USA prior history of cancer age, gender, race, smoking pathology of the squamous (H) 34 years [±SD menstrual period 103 patients with histological evidence of from China and 80 old other than GC or with Case control gastritis (NAG), 5 (IM) 67 years °diabetes or other polyposis CRC, or advanced adenomas Case control microbiome from 44 individuals sample from each individual Richard, 2018 Gut Colorectal cancer 7 CAC patients, 10 colitis associated France infectious colitis, gender, age, Montreal used antibiotics in last and 22 HCs 13with [±(SD: 12)] diseases and patients mellitus, dyslipidemia, specific Bifidobacterium and from patients taken °inflammatory disease control samples samples with [±SD: 5.4)] additional groups Stomach: 69.4 intravenous injection), immunomodulators exactly 60 years Epithelial betel chewing, cigarette young normal Australia local and/or systemic Gender, Age, Race, Smoking, years with no history of participants cessation of less than 5 alcohol, tooth brushing, tooth LSCC: 62.4±8.4 oral disorders or the or both within 8 weeks [±SD 5.7], (H1) cancer prior to sex, race and calendar year. 54.55 years [±SD past three years, BMI, diabetes, hypertension, samples. cancer pathogenesis are Reshui: ↑ Proteobacteria, ↑ Pseudomonadaceae, from the bile duct. (CCA) Exposure: ** crispatus, Lactobacillus iners control microbiome final lung cancer or recent (1 mo) status, pack-years, diagnosis common bile duct intraepithelial 8]. ° the non-use of gastric cancer atrophy or intestinal from Mexico Mexican cases: previous treatment for pat. with intestinal [range 60-71], chronic diseases °pat. familial adenomatous and without any Dai, 2018 Gut Colorectal cancer 271 controls and Cohort C1 China, Austria, USA, Cohort C1: Prior to Sample size, Age, Gender, BMI Case control microbiome SC patients and 10 cancer (CAC): coagulation disorders classification, Type of cancer, 6 months before normal colonoscopy with need for diet and medication of the two antiphlogistic after starting pre-of the intestine. distance off tumor gastrointestinal (36-98) or probiotics such as and acid blockers for while 18 patients precursor lesion smoking, and family history of healthy controls: antibiotics prior to Alcohol, Tumor characteristics cancer who were younger than 41 years °treatment with loss, eating between meals, SCLC: 62.4±8.4 presence of removable before enrolment, or EAC cases: 68.0 USA Not available adjusting for BMI, smoking, and 73.7 years [±SD pancreatic Adjustments for pancreatic 9.63]. malignant tumors, oral smoking and drinking. Croatia positive history of Not available associated with enrichment Total NOS score: 8 Neisseria, ↓ Bacilli and Oxalobacteraceae. (CCA) tumor tissue ~ adjacent and Gardnerella vaginalis, ↑ diagnoses and 36 antibiotic use (BBP). lesions and HPV+ douches and no sexual metaplasia 64.5 years old GC metaplasia (IM) and (GC) 70.6 years who received certain polyposis neoplasms Case control microbiome 255 CRC cases (American): Germany, France surgery and treatment HS 50.7 (10) years or concomitant Previous abdominal surgery, colonoscopy. and 9 with small emergency groups only the mean age of medicines. operative preparation Zackular, 2014 Gut Colorectal cancer 60: 30 CRC, 30 HCs CRC: 59.4 years CRC: non-hispanic °known HIV or chronic There were no significant unknown disorders °evidence of Pancreas: 62.3 yogurt during the 3 the past month; history were strictly 50 ± 11 cancer 26 sample collection evaluated as an years, 162 were oral antiseptic or periodontitis, frequency of partial dentures or consumption of 6.7 alcohol 5.7]. (PAD2) 63.8 adenocarcinoma cancer status, race, BMI, diseases or gastric sexually transmitted or of the lower airway Streptococcus. normal tissue ↑ normal tissue. Significant Total NOS score: 8 Fusobacterium subjects with non-Chng, 2016 Bile duct Cholangiocarcinoma 60 pat. with (CCA OVa) 57.9 Thailand, Singapore Case-control matching for age, (SIL) and 20 healthy activity in previous days Castaño-Gastric gastric cancer gastric cancer (GC, n Not available Singapore/Malaysia Subjects who had been age, gender or country of origin Mexican cases: <18 5 pat. with an [range 52-81]. medication during the Sinha, 2016 Gut Colorectal cancer 42 CRC cases and 89 Cases: 63.4 (13.1) USA Not available Controls were frequency Flemer, 2017 Gut Colorectal cancer 59 patients Not available Ireland personal history of CRC, age-matched controls 60.96(13.56) Cohort C2: Not sporadic cancer treatment with Current treatment, previous Saito, 2019 Case Gut Colorectal cancer 81 Japanese CRC: 67±9 Japan Not available Gender, age, BMI, diabetes, early adenomas) colonoscopy °use of patients were significantly Kasai, 2016 Gut Colorectal cancer 58: 9 CRC (3invasive CRC: 54.3 years Yokkaichi, Japan °current use of Total cholesterol and high-with bowel cleansing Case control microbiome cases [±SD: 11] HCs: white: 28, other:2; viral hepatitis °known differences in age or current Allali, 2015 Case Gut Colorectal cancer 23 subjects; 46 69.8 years [49-85] Spain Not mentioned No significant confounding by infection (57-67) months before fecal of gastrointestinal tract older than 60 Cancer 53 ± 10 elderly normal outpatient for any between 41-58 antibiotics in the past dentist visits, mouthwash use, orthodontic appliances; unhealthy substances EAC matched years [±SD 5.2], (except non-melanoma smoking status, alcohol diseases °subjects that recent urinary microbiota with oral (LC) ↑ Granulicatella Spontaneous sputum Selection: *** Enterobacteriaceae, differences with the controls. necrophorum. (SIL) cancer diagnoses, 10 Case control microbiome cases cholangiocarcinoma years [range 38-or Romania gender and anatomical subtype. controls with no of the sampling Rodríguez, 2017 microbiome = 12) and controls prescribed antibiotics, years old, with any intestinal-type of last three months °pat. Case control microbiome matched controls matched to cases by gender and Case control microbiome undergoing surgery IBD or IBS Cohort C2 available (SC): 68.8 (12.1) anticoagulant therapy, treatment control microbiome patients, including CRA: 66±8 hypercholesteronlemia, antibiotics for at least 2 higher. No adjustments made. Case control microbiome cases cancer, 6 carcinoma [±SD: 7.9] HCs: antibiotics °history of density-lipoprotein cholesterol and with oral 55.3 years [±SD: HCs: non-hispanic NHPCC or FAP medication use among both control (Spain) microbiome cases samples: 23 samples region of the colon, age, gender. CRC: 67 years [ Czech Republic Not mentioned Not mentioned Small intestine: sample collection infections, such as years. Normal: 40 Malaysia currently having Not available healthy controls: benign condition years and 74 month °food or fluid dentures and self-reported dry systemic diseases, such (including alcohol, controls: 68.0 (H2) 63.8 years skin cancer) °controls consumption status and history had used any infections, diabetes and commensals adiacens correlated with appears to be a viable source Comparability: ** Lachnospiraceae, Sphingomonadaceae and healthy control (CCA) and adjacent 71], (CCA non-cervical lesions (H): °records of used Case control (functional non-steroidal anti-autoimmune disease, gastric cancer (GC). who previously Controls: 58.4 body mass index (BMI) for CRC, 21 (Austrian): Cohort C3: No years and treatment with 47 CRA and 24 Controls: 58±15 hypertension, antibiotic months prior to in adenoma), 49 HCs 48.8 years (SD: or current chronic levels were significantly lower in antibiotics (CRC) 9.2] white: 21, other:9 °inflammatory bowel groups. However, among our on tumor, 23 ±SD: 11 )] HCs: 55 61.5 (39-79) chemotherapy, Campylobacter and 59 ± 5.6 years USA Not available age, gender OPMD: 54 malaise, pregnancy or 61 (OC1) 69.2 years U.S.A. °cancer pat. from study No corrections, but each patient older than 58 intake, smoking or mouth and burning mouth. as diabetes mellitus, cigarette/ tobacco and 6.6 [±SD 5.4]. with cancer prior to of diabetes. antibiotics within the obesity (LC) ↑ Firmicutes, TM7, Number of OTUs: higher in LC Selection: **** Enterococcus sp. 130, of bacterial biomarkers Exposure: ** Bifidobacteriaceae. (CCA) subjects matched normal OVa) 56.6 years 10 HPV-and 10 medication in the last dyspepsia (FD), n = inflammatory drugs diabetes, or cancer received therapy for H. (13.0) individuals with 67.1(10.91) antibiotics and no healthy subjects antibiotics or antifungal intramucosal CRC treatment, reason for enrolment. 8.2) bowel or liver disease the cancer subjects. The average Vogtmann, Gut Colorectal cancer 104: 52 CRC, 52 HCs CRC:61.8 years; CRC: 75,0% white, Not mentioned CRCs and HCs were matched on (USA) disease °any surgery, samples, men, whites and those samples 5cm off [±SD: 15)] Colon: 74.8 (64-radiation, or surgery Salmonella, for the past Cancer: 60 lactation, infected with high-risk controls: [range 62-84], 1 were excluded from is his or her own control. years. tooth brushing in the gastritis, hepatitis and drug abuse) ESCC cases: 6.6 selection. past two months. Veillonella, Megasphaera compared to BML Comparability: ** Streptococcus intermedius, which may have utility as cancer tissue ↑ Lee, 2016 Lung Lung cancer 28 patients were 64 ± 11 years South Korea less than 20 years of Demographic and clinical data, samples, 5 non-[range 33-69]. For HPV+. 30 days previous to 20) (NSAIDs) or acid other than GC, and pylori eradication. Adjustment on age, sex, race, polyps and 56 Cohort C3 invasive medical (HS): 48.3 (13.4) treatments in the two patients, and 10 colonoscopy Chen, 2012 Case Gut Colorectal cancer 43: 21 CRC, 22 HCs CRC: 64 years (37-China °diabetes, infectious CRCs and HCs were matched on °history of age and BMI of the cancer 2016 microbiome cases HCs: 61.2 years 23,1% black, 1,9% the basis of the design for radiation or with greater BMI were more tumor 84) prior to fecal sample month; presence of HIV, under antibiotic 59 (H1) no age study 2. past 90 min. °recent other cancers in Age at enrollment USA antibiotics treatment controls matched to cases based 6.5 China Not available Not available Chao 1 and Shannon: higher Exposure: ** Escherichia coli, S. viridans, biomarkers for LC status and Total NOS score: 7 Stenothrophomonas. Cohort microbiome included in this age, pregnant, or had including age, gender, body cancer hepatic the other sampling °molecular suppressants in the with a previous Seo, 2014 Gastric Gastric cancer cases 16 pat. with gastric 67.18 years South Korea / hospital and BMI healthy (Chinese): intervention for 3 years months before healthy subjects control microbiome cases 78) HCs: 64 years diseases or particular the basis of the design for age chemotherapy or subjects were significant higher Case control others ; HC: 90,4% gender and BMI chemotherapy for their likely to have colorectal cancer. Burns, 2015 Gut Colorectal cancer 44 subjects; 88 64.9 years Minnesota °no availability of Age, gender, tumor stage and Rectum: 73.6 collection digestive diseases medication within the OCC or OPC: 65 mentioned. (OC2) alcohol intake or addition to PBC; available by during the seven days on age, race, smoking, season-ESCC matched in LC compared to BML Acinetobacterjunii, and stage. dominated by Fusobacterium spp, Sneathia spp., Shuttleworhia satelles and Megasphaera spp. Higher alpha diversity in the (SIL) group and (CC) group than in the (H) + notably different beta diversity in every stage of cervical cancer. Selection: **** Comparability: ** Exposure: * Total NOS score: 7 All groups: dominated by study; 20 patients undergone any mass index (BMI), pulmonary samples, 2 bile fluid samples, mean HPV+ diagnosis. two-month period prior treatment for GC Case control microbiome cancer from who [range 37-75] for Thomas, 2016 Gut Colorectal cancer 18 rectal-cancer Cases: 59.3 ± 8.8 Brazil No subjects had Chi-Square tests were Gao, 2017 Gut Colorectal cancer 65 patients with CRC 63.89 years for China antibiotics and body mass index (BMI), age, 65.90(10.61) months; no vegetarian inclusion Yachida, 2019 Gut Colorectal cancer 616 participants Not available Japan hereditary or suspected body-mass index (BMI), alcohol (37-84) diets. °taking and gender and the BMI radiation therapy and than those of the control (same samples white, 5,8% black, current CRC °colonic No adjustments. Case control microbiome cases samples: 44 samples [±SD:16.7] patient-matched tumor site (but none of these (39-85) Not available USA patients who received Not available except gastric cancer; past 3 months, involve Not available Sri Lanka Not available demographic and clinical 59 years [range measurable amount of immune-compromising category (no prior to sample of-study enrollment and controls: 66.8 Total NOS score: 8 Streptococcus sp. 6 Firmicutes, Actinobacteria, were diagnosed with procedure other than function, smoking status, samples and 4 non-age is 59.6 years Oh, 2015 Cervical Cervical cancer 120 women: 70 with 42 subjects Korea °history of Adjustments for age, marital to recruitment as well Coker, 2018 Gastric Gastric cancer 81 cases; validated Not available China Not available age, gender, H. pylori status and two samples each the 11 pat whose Case control microbiome subjects, 18 non-personal or familial performed on subject's Case control microbiome males and 63.08 probiotics within 4 gender, and disease history Cohort C4 diet; no history of Shah, 2018 Gut Colorectal cancer tumor-adjacent Not available USA Not available clinical and demographic factors Cohort microbiome who underwent hereditary disease (for consumption (grams per day) medications at the time regular use of subjects. Smoking and alcohol as Ahn 2013) 3,8% others (USA) adenoma samples on tumor, 44 normal and tumor factors were found to have a Treated: 66.9 antibiotic therapy presence of type 2 surgery, chemotherapy characteristics 39-78], in (CIS) alcohol in the saliva by diseases, such as mean) collection recruitment method 6.4 (IBC) ↑ Fusobacterium, Significant differences in Selection: **** Bacteroidetes, lung cancer, and 8 bronchoscopy to smoking amount, comorbidities, cancer gastric [range 46-82]. Case control microbiome cases (CIN) cervical younger than 39 gynecological cancers, status, menopause, smoking as subjects known to Case control microbiome in 126 additional tissue positions were taken: one samples were cancer controls Controls: 55.2 ± history of colorectal categorical data such as gender, years for females weeks, acute diarrhea, (German and cancer or inflammatory Case control microbiome biopsies (n = 294 of the participants, location of colonoscopy example, familial and smoking habits (Brinkman of sample collection immunomodulators intake results are the same in Weir, 2013 Case Gut Colorectal cancer 21: 10 CRC, 11 HCs CRC: 63.7 years; Colorado, USA °use of antibiotics Univariate analysis (the majority Zeller, 2014 Gut Colorectal cancer 114: 53 CRC, 61 HCs CRC: 68.5 years; France °previous colon or BMI and gender did not differ matched adjacent tissue specimens significant impact). (53-78) within 28 days from diabetes, autoimmune or radiation treatment Not available USA any treatment, Controls matched for age and the pat. was 84 head space GC. human Stool CRC: Ireland Personal history of CRC, Age, BMI, gender, tumour size, (ESCC) 64.8 years China °cases collected during Case-control matching for sex Not available USA Not available tissue adjacent to the tumor 12 of the (NSCLC): colonized were diagnosed with evaluate the lung mass. and final diagnosis mucosa samples intraepithelial years, 37 °insufficient data on status, oral contraceptive use be infected with the cases sample of the gastric used for 15.7 cancer or colitis (either alcohol and tobacco use and confirmed simple French): disease of intestine pairs, n = 588 the tumor in the colon, and adenomatous index), dietary habits °use of antibiotics (steroids, interferons, both groups. No adjustments control microbiome cases HCs: 40.7 years within two months of of taxa that significantly differed Case control microbiome cases HCs: 63 years rectal surgery, significantly when adjusted in tissue samples 62 years [±SD: Colorado (86% non-antibiotic exposure Not mentioned their prostate biopsy diseases and other within the past 1 Range from 40-64 including surgery, sex, non-smokers years, (PRE) 68.4 (OSCC) 65.6 years Finland °pat. with antimicrobial Adjustments for impact of immunodeficiency virus 65.3±10.8 IBD and irritable bowel rectal bleeding, alcohol [±SD 8.0], (DYS) November of 2010 and and age. Adjustments for (PC1) 66.5 years U.S.A. °cases with evidence of Case-control matching for age, deemed noncancerous by with fungi. 0 of the (H) a benign mass-like (non-CCA). Of the neoplasia (CIN) 55 between 40 and the questionnaire and histological grade but not Human Hu, 2018 Gastric gastric cancer 6 patients with GC superficial China use ofantibiotics within Gender, Age, Diabetes, BMI cancer (GC), one of statistical ulcerative, Crohn's, vital status. Student t-tests were adenoma or polys, 68.44(12.22) Cohort C4: No previous biospecimens) and stage and grade of tumor were polyposis, hereditary within at least one etc.) or probiotics were made. study participation in stool samples between °inflammatory or analysis, age does. No Chen, 2012 Case Gut Colorectal cancer 27 subjects; 91 61 years [37-81] China °diabetes, infectious Matched on the basis of the 6.86] Hispanic) from the study malignant tumors; and 63 ± 11 USA insufficient reads age, gender, race, tumor month radiotherapy and/or Only male years [range 49-[range 39-85], therapy within the past drinking and smoking on (HIV) or ongoing Swab CRC: syndrome. consumption, smoking status 65.5 years [±SD March of 2011 to avoid education, smoking, alcohol [±SD 8.9], (H1) locally advanced gender and ethnicity. pathological analysis referred as colonized. Mostly Aspergillus lesion CCA, 28 pat. are CIN1, 15 CIN2 or 49 years, 30 °inadequate blood for significant. Immunodeficiency Case control microbiome and 5 patients with gastritis: 60.5 ± 6months, receiving the adjacent normal analyses. radiation or infectious performed to compare inflammatory bowel colon or rectal surgery, matched tumor not available for all of the non-polyposis month of sample °persons who did not °regular use of NSAIDs, healthy and CRC cohorts were a infectious injuries of adjustments, sensitivity analysis. control microbiome cases samples: 27 samples diseases or particular design for age, gender and BMI. CRC: 63.5 years Nijmegen Not mentioned Not mentioned Cases: median 63 USA history of previous age, BMI, characteristics Median 64 years USA recent antibiotic use, age, body mass index (BMI), and history of localization, T-stage, N-stage, Patients: 61.6 Austria undergone long-term age, alcohol consumption, chemotherapy, if they >15 natural teeth and were in 79] and (H2) 30 (OLD) 54 years 7 days °pat. diagnosed acetaldehyde production. immunosuppressive 65.7±10.9 7.6], (H) 66 years confounders (i.e. drinking, family history of ESCC, 66.4 years [±SD pancreas cancer due to Corrections for smoking and matched controls niger (5), by Aspergillus Carpagnano, Lung Lung cancer cases 43 pat. with non-(NSCLC) 68.4 Italy Adjustments for sex, age, Opisthorchis CIN3, and 50 healthy subjects between evaluation, chronic Virus (HIV SG 6.5 years chemotherapy or gastric mucosa (H). colitis, chronic differences in the means disease (IBD), or colorectal cancer, biopsy:fecal pairs (n participants colorectal cancer, collection °watery provide consent °age statins or probiotics result of disease status and not the intestine. °Patients on tumor, 27 diets °taking (49-71) (57.5-67) cancer (except non-relevant to breast cancer risk significant prostate size gastrointestinal tract smoking history, alcohol use, yrs., SD = 8.2 yrs antibiotic use (as per tumour size, lymph node status, were pregnant or good general health years [range 30-[range 24-74] and with human therapy; and other (CRC) 53.24 years China °controls with Adjustments for age, weight, [±SD 7.3]. ambient temperatures MFT, times of tooth brushing per 10.5]. (PC2) 69.9 arterial involvement or drinking history. ochraceus (3) and Penicillum 2014 microbiome small cell lung years [±SD 9.2], histotype, stage, smoking habit, viverriniviverrini controls (H). 50 and 59 years disease, drug Li, 2017 Gastric Gastric cancer 33 individuals Not available China active gastroduodenal age, gender, HP state, sampling gastric radiotherapy prior to Nasrollahzadeh, Gastric Esophageal cancer 91 subjects: 19 pat. (ESCC) and (ESD) Iran °samples < 1000 reads. Case-control matching for age inflammatory illnesses), between both groups for age, irritable bowel inflammatory, or = 42 pairs, n = 84 microsatellite stool, stool too thin to >65years °chronic bowel of differences in age or BMI) with need for samples 2-5cm from medications at the time Controls: median melanoma skin cancer), (details not available); gastrointestinal surgery, previous treatment Healthy controls: their medical histories), smoking habit, and tumour nursing, received 30]. (H) 30.4 years immunodeficiency virus diseases known to [±SD 9.70], (H) gastrointestinal smoking, hypertension and and different dietary day, daily consumption of years [±SD 11.6], direct extension into spp. (4). Case control cancer (NSCLC) and (H) 64.1 years pack years, time since quitting associated (OVa), and 12 subjects dependency, Case control microbiome including 9 subjects ulcers on endoscopy, location, and HP-eradication adenocarcinoma: the collection of 2015 microbiome cases with esophageal 64.5 years [±SD and sex. previous colonic or height, weight, BMI. syndrome infectious injuries of biospecimens) instability-high), an collect Mastromarino, Gut Colorectal cancer 20: 13 CRC, 7 HCs CRC: 58 years ; New York, USA °antibiotic treatment or Not mentioned disorders or food emergency the tumor, 27 of sample collection, or 61 (59-64.25) inflammatory bowel adjustment on oestrogens disorders, hormonal or chemoradiotherapy, CPS-II cohort:71.0 USA Not available controls matched on age, sex, 46.7 yrs., SD = vaccinated in the six HPVpositivity antibiotics or [range 19-56]. or hepatitis virus affect the oral and 51.57 years [±SD diseases, oral diseases, diabetes. Chemotherapy and habits during different pickled vegetables and daily (H2) 65.1 years adjacent organs, Fungal colonization by Aspergillus niger, Aspergillus ochraceus and Penicillium ssp. in the EBC of (NSCLC) and not of the (H). Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 Atopobium, Hydrogenophaga, Gluconacetobacter and Lactobacillus microbial composition of breast tissue in benign vs. malignant disease. Comparability: ** Exposure: ** Total NOS score: 8 Proteobacteria, Tenericutes, Fusobacteria, and candidate division TM7. Predominance of A. vaginae, L. iners and G. vaginalis and paucity of L. crispatus = risky microbial pattern. Synergistic effect of 21 healthy controls [±SD 13.1]. smoking in subjects with fungal and 32 pat. are not 60 years or older. °psychological with HP-associated acid suppressive 55.2 ± 5.6 years specimens, the use of Case control squamous cell 11.8], (DC) 63.6 small bowel resection, Xu, 2017 Gut Colorectal cancer 61 healthy controls, Not available China Not available Not available the intestine; no need Zhang, 2018 Gut Colorectal cancer 30 initially CRC: 60.5 (9.8) China (a) patients younger gender, age, BMI, alcohol, inflammatory bowel Feng, 2015 Gut Colorectal cancer 103: 46CRC, 57 HCs CRC: 67.1 years ; Austria °use of probiotics or CRCs and HCs were matched on 1978 microbiome cases HCs: 46 years other therapy for at allergies/dietary Alexander, 2016 Gut Colorectal cancer 18 subjects; 54 76 years [55-85] London, UK °patients undergoing Not mentioned samples 10-20cm used antibiotics within disease, diverticulitis, systemic therapy for and cholecystectomy. [6.4] years race/ethnicity, and time since 15.1 yrs months prior to study periodontal therapy in infection. airway microbiota 8.01], calculated malignant tumor and surgical treatment are possible seasons) °cases with no consumption of fresh fruits. [±SD 10.1] and metastatic pancreatic risky microbial pattern with (H). colonization. (non-OVa). problems. chronic gastritis, 9 therapy including PPIs proton pump inhibitors carcinoma (ESCC), years [±SD 14.0] nor previous colon Case control microbiome 47 adenoma for need for emergency Case control microbiome diagnosed CRC A-CRA: 59.6 than 40 years of age, smoking, lesion location, disease, an abdominal Case control microbiome cases HC: 63.8 years antibiotics within the the basis of the design for age, Case control least 4 weeks prior to restrictions Case control microbiome cases samples: 18 samples emergency surgery from the tumor at least one month of or bariatric surgery; prostate cancer Liver cancer: China Not available age, gender, BMI, PLCO cohort: 62.7 mouthwash collection inclusion the previous 3 months, Cases: 50.30 ± China Cases: diagnosed with No significant differences for the prior cancer related reasons for a thicker tongue histopathological (CP) 57.8 years cancer, chemotherapy high risk HPV infection on Hosgood, 2014 Lung Lung cancer cases 8 never smoking The age range of China °controls previously Case-control matching by age Hieken 2016 Breast tissue Breast cancer cases 28 pat. of whom 13 Benign patients USA Adjustments for contamination Seo, 2016 Cervical Cervical cancer 137 subjects: 65 pat. 43.6 years [±SD South Korea °histories of Adjustments for daily intake of gastric intestinal and histamine (H2)-or other digestive 18 pat with and (HC) 62.1 adenomas or familial patients, and 52 colonoscopy patients, 88 (10.3) (b) persons not Han hypertension, heart disease, surgical history or for last 3 months gender and BMI collection of the fecal °chemotherapy or on tumor, 18 °patients treated with sample collection CRC: 71.1 years Oviedo, Spain °hereditary syndromes Age-matched and sex-matched other gastrointestinal 46.95±5.6 clinicopathological data, CT scan, [4.8] years Not available China Not available Not available if they had any (OSCC) 62 years Hungary °controls not free of (OPSCC) mean U.S.A. / 5.86 intrahepatic between the groups in age, sex bigger study symptoms in the last 2 coating; no adjustment was confirmation °cases [±SD 11.0]. or radiation therapy CIN risk. Different A. vaginae Case control microbiome female lung cancer (LC) and (H) was diagnosed with lung and hospital Case control microbiome benign non-atypia median [range] from the skin, batch effects Case control microbiome cases (CIN) with cervical 11.2]. gynecologic cancers, nutrients, age, BMI, marital metaplasia, 7 gastric receptor blocker, system drugs within 4 esophageal year [±SD 16.3]. polyposis syndrome. carcinoma patients Hale, 2018 Case Gut Colorectal cancer 83 individuals (dMMR): 74 (18) USA chemotherapy or sex, age, BMI, smoking status, advanced colorectal Polyps: 56.5 (8.9) people, (c) patients diabetes which stool samples samples. °No laxatives radiation treatments samples 5cm off neoadjuvant [±SD: 10.1] Polyp: (familial adenomatous subjects surgery within 6 Early HCC: histopathology images and diet adjustment on age, sex, race, systemic illnesses, [range 44-86], (H) oral pathology. age of 62 years, cholangiocarcinoma or distribution, and body mass group containing years. made. with no complete prior to saliva collection history of use of Not available (CIN): higher numbers of OTU. Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 Lactobacillus iners-dominant microbial type B + A. vaginae-dominant microbial type C: ↑ risk of CIN. Synergistic effect between semi-Western diet and microbial type C. No synergistic effect between semi-Western diet and microbial type B. Semi-Western diet: ↑ risk of CIN + synergistically ↑ risk with the dominance of A. vaginae. Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 (H) and (EC): mainly Streptococcus viridans, Haemophilus influenza and Neisseria catarrhalis, Klebsiella pneumoniae and Streptococcus group B. No significant difference in the number and type of bacterial species between (H) and (EC). Selection: *** Comparability: Exposure: * Total NOS score: 4 Fusobacteriaceae enrichment of pro-inflammatory oral bacterial species, increased abundance of lactic acid producing bacteria, and enrichment of short chain fatty acid production pathways in GC Selection: **** Comparability: * Exposure: ** Total NOS score: 7 (GC) ↑ Flavobacterium, Klebsiella, Serratia marcescens, Stenotrophomnonas, Achromobacter and Pseudomonas GC samples tended to have lower bacterial diversity compared with other samples with similar H. pylori levels. H. pylori colonization results in alterations of gastric microbiota and reduction in bacterial diversity. Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 (High tumor grade vs. Low tumor grade) ↑ Helicobacter pylori ↓ Bacteroidetes & Lactobacillales gastric non-malignant tissue microbiota features were associated with a known gastric cancer risk factor (family history of UGI cancer) and clinical features (tumor grade and metastasis) Selection: *** Comparability: ** Exposure: ** Total NOS score: 7 (GC) ↑ Bacteroidetes, Firmicutes, Fusobacteria, and Spirochaetes ↓ Proteobacteria Comparison with other body sites suggested that stomach microbiota resembled oral microbiota in phylum-level taxonomical profiles, but not Selection: **** Comparability: * Exposure: ** Total NOS score: 7 pneumosintes ↓ 10 bacterial taxa compared to SG precancerous stages (GC) ↑ Neisseria, Alloprevotella, and Aggregatibacter ↓ Sphingobium yanoikuyae GC patients was characterized by reduced species richness, enrichment of 13 bacterial taxa and depletion of 31 taxa Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 (GC) ↑ Prevotella melaninogenica, Streptococcus anginosus and Propionibacterium acnes ↓ Helicobacter pylori, Prevotella copri and Bacteroides uniformis the stomach microhabitats determined the overall structure and composition ofthe gastric microbiota, regardless ofdifferent GC stage and type. Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 Gastric tissue of all groups: mainly Proteobacteria and Firmicutes. (GC) ↑Lactobacillus coleohominis and Lachnospiraceae, ↓ 2 TM7, 2 Porphyromonas and Neisseria. Differences between (GC) and (NAG), but not between (IM) and (GC) or (IM) and (NAG). From (NAG) to (IM) to (GC), the microbiota diversity showed a trend to diminish. Selection: **** Comparability: * Exposure: * Total NOS score: 6 Clostridium spp. and Prevotella spp. All groups: mainly Firmicutes, Bacteroidetes and Proteobacteria. (EC) ↑ Clostridiales and Erysipelotrichales, ↓ Helicobacteraceae. Significant differences in gastric mucosa of (ESCC) and (ESD) compared to (H). Selection: *** Comparability: * Exposure: ** Total NOS score: 6 (CRC) ↑ Porphyromonas assaccharolytica, Peptostreptococcus stomatis, Parvimonas micra, and Fusobacterium nucleatum ↓ Ruminococcoaceae and Lachnospiraceae families The gut microbiota can be used to differentiate healthy individuals from those with colonic lesions. Selection: **** Comparability: * Exposure: ** Total NOS score: 7 (CRC) ↓ Clostridia, Lachnospiraceae ↑ Fusobacterium, Porphyromonas CRC cases had strong microbe-metabolite correlations that were predominated by Proteobacteria and Actinobacteria. Selection: **** Comparability: * Exposure: ** Total NOS score: 7 Desulfovibrio, and Odoribacter (H) ↑ Planctomycetes ; Pseudomonas, Escherichia, Acinetobacter, Lactobacillus, and Bacillus (CRC) ↑ Fusobacterium nucleatum Fusobacterium is a passenger that multiplies in the more favorable conditions caused by the malignant tumor rather than a causal factor in colorectal cancer development Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 (CRC) ↑ Bacteroidetes Cluster 2, Firmicutes Cluster 2, Pathogen Cluster and Prevotella Cluster ↓ Bacteroidetes Cluster 1 and Firmicutes Cluster 1 CRC-associated microbiota profiles differ from those in healthy subjects Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 (CRC) ↑ Fusobacteria ↓ Firmicutes and Actinobacteria specific mucosa-associated microbiota signature and function are significantly changed in the gut ofCRC patients Selection: ** Comparability: ** Exposure: * Total NOS score: 5 p=0.0006 unweighted UniFrac distance: ADONIS, normal-adenoma: R2 =0.015, p=0.05; normal-cancer: R2 enoma-cancer: R2 =0.051, p=0.01; ad=0.059, p=0.01 (CRC) ↑ Fusobacterium nucleatum and Peptostreptococcus stomatis, Parvimonas micra and Solobacterium moorei consistent faecal microbial changes in CRC across four cohorts, identification of novel bacterial candidates that may be involved in the development and progression of CRC, Selection: **** Comparability: Exposure: ** Total NOS score: 6 (CRC) ↑ Porphyromonas, Clostridium, Ruminococcus, Selenomonas, and Fusobacterium Phylogenetic diversity: a moderate but statistically significant effect was observed when we compared control and CRC groups in the second age category (ages 50-79) Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 Changes in abundances of 31 microbial taxa including several taxa within the phylum Bacteroidetes by tumor stage CRC microbiomes are correlated with tumor mutational profiles, pointing towards possible mechanisms of molecular interaction Selection: **** Comparability: Exposure: ** Total NOS score: 6 Alistipes finegoldii,and Thermanaerovibrio acidaminovorans multiple populations despite technical and biological variations. (dMMR CRC) ↑ Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum (pMMR CRC) ↓ B. fragilis Tumor MMR status strongly predicted microbial community variance and was associated with distinct microbial, metabolic, and interaction profiles. Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 (H) ↑ 24 OTUs including Alistipes, Oscillibacter, Bacteroides, Pseudoflavonifracor and Succinivibrio (top five) local microbiome dysbiosis may contribute to functional changes at the cancer sites Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 (HP) ↑ Firmicutes and Actinobacteria, Lachnospiraceae (ADK) ↑ Sutterella and Escherichia/ Shigella identification of specific microbial biomarkers associated with each carcinogenic stage Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 (CRC) ↑ Fusobacterium, Parvimonas and Streptococcus (CRC) ↓ Faecalibacterium and Ruminococcaceae With the exception of these limited taxa, the majority of findings from individual studies were not confirmed by other 16S rRNA gene-based datasets. Selection: *** Comparability: * Exposure: ** Total NOS score: 6 (CRC) ↑ 24 species ↓ 10 species gut microbiome and inflammation may gradually form a microenvironment to promote the development of CRC Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 (CRA/CRC) ↑ Fusobacterium, Parvimonas, and Atopobium (Controls) ↑ Lachnobacterium, Salmonella, and Moraxellaceae Two distinct aggregations were observed in the data ofUnweighted UniFrac PCoA. Selection: *** Comparability: ** Exposure: ** Total NOS score: 7 progression (CRC) ↓ Firmicutes (most prominent for the class Clostridia; family Lachnospiraceae; genus Coprococcus) ↑ genera Fusobacterium, Atopobium and Porphyromonas Shannon: lower in cases Eveness: NS Selection: ** Comparability:** Exposure: ** Total NOS score: 6 (CRC) ↑ genera Lactobacillus, Leuconostoc, Pediococcus, Blautia coccoides ↑ species E. coli, Faecalibacterium prauznitzii ↑ genera Bacteroides, Prevotella ↓ species Clostridium Not mentioned Selection: * Comparability: ** Exposure: *** Total NOS score: 6 genera Peptostreptococcus and Anaerotruncus (CRC) ↑ Bacteroides, Alistipes (putredenis), Esherichia, Parvimonas, Bilophila (wadsworthia) and Fusobacterium ↓ Genera Ruminococcus, Bifidobacterium and Streptococcus Shannon: NS Richness: higher in cases (Significant) Selection: *** Comparability: ** Exposure: *** Total NOS score: 8 (CRC) ↑ Fusobacterium nucleatum and C. difficile Not mentioned Selection: *** Comparability: * Exposure: *** Total NOS score: 7 (CRC) ↑ Fusobacteria ↑ genera Actinomyces, Atopobium, Fusobacterium and Heamophilus ↓ genus Slackia ↑ species Actinomyces odontolyticus, Bacteroides fragilis, Clotridium nexile, Fusobacterium varium, Heamophilus parainfluenzae, Prevotella stercorea, Streptococcus gordonii, and Veillonella dispar ↓ species Eubacterium coprostanoligens Initial T-RFLP analysis did not reveal any statistically significant differences between control and cancer subjects. NGS found that the gut microbiota differs between control and cancer subjects. Selection: *** Comparability: ** Exposure: *** Total NOS score: 8 evident (CRC) ↑ Methanobacteriales and Methanobrevibacterium PCoA based on UniFrac: NS Selection: *** Comparability: * Exposure: ** Total NOS score: 6 (CRC) ↓ Clostridium coccoides group, Clostridium leptum subgroup, Bifidobacterium, Atopobium cluster, Enterobacteriaceae and Staphylococcus Total bacterial counts in the CRC group were significantly lower than those in the healthy group. Selection: ** Comparability: ** Exposure: ** Total NOS score: 6 Campylobacteraceae, Porphyromonadaceae, Fusobacteriaceae ↑ genera Fusobacterium, Campylobacter, Kingella, Leptotrichia, Solobacterium, Holdemania, Parvimonas, Anaerococcus, Oscillibacter, Fastidiosipila, Peptostreptococcus, Eubacterium, Gemella, Porphyromonas, Parabacteroides, Odoribacter ↑ 17 OTUs belonging to the genus Bacteroides and 5 OTUs that were closely related to Ruminococcus ↓ 24OTUs, most related to Faecalibacterium prausnitzii and Roseburia Solobacterium moorei, F. nucleatum, Peptostreptococcus stomatis ↓ Eubacterium ventriosum Porphyromonadaceae, Lachnospiraceae (CRC) ↑ Fusobacteria, Proteobacteria and Bacteroidetes ↓ Actinobacteria and Firmicutes ↓ genera Eubacterium, Ruminococcus, Bifidobacterium, Campylobacter, Acinetobacter ↑ genera Fusobacterium, Pseudoflavonifractor, Peptostreptococcus, Leptotrichia, Porphyromonas, Desulfovibrio, Parvimonas, Bilophila Shannon: NS Evenness: NS PCoA: NS Selection: **** Comparability: ** Exposure: *** Total NOS score: 9 Alcaligenaceae ↑ genera Gemella, Catonella,Porphyromonas, Filifactor, Fusobacterium, Peptostreptococcus and Mogibacterium ↓ genera Anaerostipes, Catenibacterium, Faecalibacterium, Blautia, Gardnerella and Bifidobacterium No significant findings were reported Unweighted UniFrac PCA analysis: Significant Selection: *** Comparability: * Exposure: *** Total NOS score: 7 Sphingobacteria, Alphaproteobacteria, Gammaproteobacteria ↓ genera Epilithonimonas, Flavobacterium, Pedobacter, Sphingobacterium, Caulobacter, Brevundimonas, Sphingomonas, Acidovorax, Janthinobacterium, Buttiauxella, Rachnella, Acinetobacter, Enhydrobacter, Psychrobacter, Pseudomonas, Nevskia, Stenotrophomonas, Nesterenkonia, Propionibacterium Fusobacterium, Campylobacter and Leptotrichia (GN) ↑ Enterobacteriaceae (GIT) ↑Ruminococcus, Subdoligranulum ↓ Lachnoclostridium and Oscillibacter (RN) ↓ Bifidobacteriaceae (CN) ↓ Lactobacillaceae Alpha diversity (Shannon index) and observed richness did not have significant differences between the groups (based on location of neoplasm). Beta diversity was not significantly different between the controls and the patients, either for the treated or the nontreated groups. Selection: *** Comparability: * Exposure: ** Total NOS score: 6 (BC) ↑ Clostridiaceae, Faecalibacterium and Ruminococcaceae ↓ Dorea and Lachnospiraceae Shannon: lower in BC cases Chao1 index: lower in cases Beta-diversity: higher in cases (unweighted UniFrac) Selection: **** Comparability: ** Exposure: *** Total NOS score: 9 ↓ alpha diversity Cases had significantly reduced alpha diversity and altered composition of both their IgA-positive and IgA-negative faecal microbiota Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 Actinomyces sp. HPA0247, Shewanella putrefaciens,and Erwinia amylovora ↓ 7 species including Eubacterium eligens and Lactobacillus vaginalis premenopausal controls (PC) ↓ faecalibacterium, lactobaccili,and Actinetobacter ↑ Veillonella, Streptococcus, and Bacteroides (H) ↑ clostridium XVIII & IV, lachnospira, acetanaerobacterium, faecalibacterium The majority of patients had similar bacterial communities within their urinary sample profile. Analysis of the bacterial taxonomies of the fecal samples did not reveal any clustering in concordance with benign or malignant prostate biopsies. Selection: **** Comparability: Exposure: ** Total NOS score: 6 (PC) ↑ Bacteriodes massiliensis (H) ↑ Faecalibacterium prausnitzii and Eubacterium rectalie Significant differences exist in the gut microbial composition of men with prostate cancer compared to benign controls Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 significantly more similar in taxonomic composition compared with the non-cancer group (GC) ↑ Lactobacillus, Escherichia, and Klebsiella ↓ butyrate-producing bacteria Intestinal microbiota in gastric cancer patients was characterized by increased species richness (Sobs index) Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 (HCC) ↑ Actinobacteria (vs cirrhosis), and 13 genera including Gemmiger and Parabacteroides (vs controls) Faecal microbial diversity was decreased from healthy controls to cirrhosis, but it was increased from cirrhosis to early HCC with cirrhosis. Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 All samples: mainly Firmicutes, Fusobacteria, Bacteroidetes, Proteobacteria, and Actinobacteria (in that order of frequency) and at genera level Streptococcus, Fusobacterium, Prevotella, Neisseria and Gemella. (LSCC) ↑ Fusobacterium, Prevotella and Gemella ↓ Streptoccocus and Rothia. Prevotella and Solobacterium were significantly more prevalent in T3-T4 tumors than T1-T2. Significantly different microbiomes between (LSCC) and controls. Selection: **** Comparability: Exposure: * Total NOS score: 5 (HNSCC) ↑ Fusobacteria and Proteobacteria ↓ Firmicutes, Actinobacteria and Streptococcus Alpha and beta diversity analyses revealed that normal tissues had the greatest richness in community diversity, while the metastatic populations were most closely related to one another. Selection: **** Comparability: * Exposure: ** Total NOS score: 7 (HNSCC) ↑ Parvimonas ↓ Actinomyces Shannon, phylogenetic divesity: no sgnifican difference Weighted, unweighted Unifrac: lower in tumor Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 a history of tobacco use (OSCC) ↑ Bacillus (OLK) ↑ Bacteroidetes, TM7, Haemophilus ↓ Firmicutes (H) ↑ Streptococcus and Abiotrophia Chao index: higher richness in OLK compared to HC Shannon and Simpson: higher diversity in OSCC compared to HC Selection: * Comparability: Exposure: ** Total NOS score: 3 (OCSCC) ↑ Proteobacterias Eshcherichia, Brevundimonas, Comamonas, Alcaligenes, Caulobacter, Cardiobacterium, Plesiomonas, Serratia, Edwardsiella, Haemophilus, Frateuria along with Actinobacteria Rothia and Bacteroidetes Peptoniphilus The screening of OCSCC samples as well as matched and non-matched controls have identified distinct viral and other microbial signature patterns associated with OCSCC Selection: **** Comparability: Exposure: ** Total NOS score: 6 (C/OPMD) ↑ Neisseria, Gemella and Granulicatella (H) ↑ Streptococcus and Veillonella significant difference between the normal and the cancer associated oral microbiota but not between the OPMD and the other two groups Selection: **** Comparability: Exposure: ** Total NOS score: 6 (H) ↑ Bacteroidetes (Prevotella), Proteobacteria (e.g. Haemophilus and Neisseria) and Firmicutes (e.g. Streptococcus and Veilonella) (OSCC) ↑ Actinomyces (Actinobacteria), Schwartzia (Firmicutes), Treponema (Spirochaetes) and Selenomonas (Firmicutes) differences in microbial abundance and diversity might inform disease status in OSCC patients Selection: *** Comparability: ** Exposure: ** Total NOS score: 7 (OCSCC) ↑ Fusobacterium, Dialister, Peptostreptococcus, Filifactor, Peptococcus, Catonella and Parvimonas Significantly greater bacterial diversity was observed in the cancer samples than in the normal samples Selection: **** Comparability: Exposure: ** Total NOS score: 6 (OSCC) ↑ Prevotella tannerae, Fusobacterium nucleatum, and Prevotella intermedia positive association between periodontopathogenic bacteria and OSCC risk and this relationship may be influenced by lifestyle and genetic factors Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 Oribacterium, Rothia, Haemophilus, Leptotrichia, Neisseria, Porphyromonas and Veillonella ↓ Paludibacter, Corynebacterium (OSCC) ↑ Capnocytophaga, Pseudomonas, and Atopobium, Campylobacter concisus, Prevotella salivae, Prevotella loeschii, and Fusobacterium oral taxon 204 (FEP) ↑ Lautropia, Staphylococcus, and Propionibacterium, Streptococcus mitis, Streptococcus oral taxon 070, Lautropia mirabilis, and Rothia dentocariosa OSCC tissues tended to have lower species richness and diversity Selection: **** Comparability: * Exposure: ** Total NOS score: 7 (OSCC) ↑ Fusobacteria Fusobacteria may be the leading phylogenetic group responsible for the increase in expression of virulence factors in the oral microbiome of OSCC patients Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 but may also contribute to the development of the disease. All samples: mainly Firmicutes, Bacteroidetes, Proteobacteria, Fusobacteria and Actinobacteria. (OC2) ↑ Fusobacteria and Bacteroidetes, ↓ Firmicutes and Actinobacteria, ↓ Streptoccocus and Rothia, ↑ Fusobacterium and Prevotella. (PRE) ↑ Bacteroidetes and ↓ Streptoccocus. Significant differences between the groups and within the same patient (oral lesions vs. anatomically matched samples). Selection: *** Comparability: ** Exposure: ** Total NOS score: 7 All samples: predominant fungal genus was Candida. (OSCC) ↑ frequency of oral yeast colonization, more yeast cells and ↑ fungal burden. (OSCC): ↑ diversity of fungi. No significant differences in lipase and protease activity. Selection: **** Comparability: Exposure: ** Total NOS score: 6 Bacteriodetes, Firmicutes and Proteobacteria. (FAC) ↑ Mycoplasmataceae (M. salivarium), Pseudomonadaceae, P. salivae and Prevotella spp., ↓ Streptococcus, Rothia mucilaginosa. All (FAC): Candida positive, all other samples Candida negative. High acetaldehyde producers (especially smokers and heavy drinkers): ↑ count of aerobes like Streptococcus salivarius, Streptococcus viridans, Corynebacterium sp., Stomatococcus sp., and yeasts (higher concentration + more frequently), also ↑ count of anaerobes. No bacterial species was significantly more frequent among the low producers. Significant differences between high and low producers. Smoking and heavy alcohol intake are strong predictors of microbial acetaldehyde production. Selection: *** Comparability: ** Exposure: ** Total NOS score: 7 sites of (OSCC) and (OLD): ↑ frequency and density of candida colonization + significantly more frequently mutagenic amounts of acetaldehyde. Cultures producing mutagenic concentrations of acetaldehyde: ↑ Candida colonization. and total amount of cultivable microbes in any patient group or sample site. Smokers: ↑ mean acetaldehyde production. Non-smokers: ↑ diversity. All samples: mainly Firmicutes, Bacteroidetes, Protebacteria, Actinobacteria and Fusobacteria (in order of frequency in (H) group). (HNSCC) ↑ Firmicutes, ↓ Bacteroidetes and Proteobacteria, ↑ Streptoccocus and Lactobacillus, ↓ Agreggatibacter, Lautropia, Haemophillus, Neisseria, Prevotella, Gemellaceae and Leptotricha. (OCSCC) ↑ Neisseria and ↓ Citrobacter than (OPSCC). (HNSCC): ↓ diversity. (OCSCC): ↑ diversity than (OPSCC). Selection: *** Comparability: * Exposure: ** Total NOS score: 6 (LC) ↑ Oribacterium and Fusobacterium Shannon, Simpson and Chao 1 indexes: higher diversity in LC Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 denticola and Prevotella sp. oral taxon 300 ↓ Prevotella melaninogenica, Firmicutes, Carnobacteriaceae, Streptococcaceae, Erysipelotrichaceae, Streptococcus, Solobacterium, Streptococcus sp. oral taxon 058 and Solobacterium moorei (CRC) ↓ Lachnospiraceae ↑ Streptococcus and Prevotellas pp., putative oral biofilm forming bacteria The heterogeneity of CRC may relate to microbiota types that either predispose or provide resistance to the disease, and profiling the oral microbiome may offer an alternative screen for detecting CRC. Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 Thick (CRC): ↓ OTU's, ↑ Prevotella, Leptotrichia and Actinomyces, ↓ Gemella, compared to thin (CRC) and (H). Thin (CRC): ↓ Veilonella compared to thick (CRC) and (H). General (CRC): ↑ Streptoccocus and ↓ Haemophilius than in (H). Significant thicker tongue coating (CRC). Different bacteria depending on the thickness of tongue coating. Selection: *** Comparability: ** Exposure: ** Total NOS score: 7 parainfluenzae, Peptostreptococcaceae bacterium, Prevotella aurantiaca, Prevotella salivae and a TM7). (EAC) ↑ Tannerella forsythia, Neisseria , Streptococcus pneumoniae (ESCC) ↑ Porphyromonas gingivalis specific bacterial pathogens may play a role in esophageal cancer risk, whereas other bacterial types may be associated with reduced risk Selection: *** Comparability: ** Exposure: ** Total NOS score: 7 Prevotella, Streptococcus and Porphyromonas, ↓ Megasphaera, Aggregatibacter, Atopobium, Lautropia, Actinobacillus, Bulleidia, Catonella, Filifactor, Corynebacterium, TG5, Acholeplasma, Moryella, Butyrivibrio, Dialister, Peptococcus, and Cardiobacterim. (ESCC) compared to (DYS) ↓Lautropia, Bulleidia, Catonella, Corynebacterium, Moryella, Peptococcus and Cardiobacterium. Lactobacillus ~ ↑ TNM stages. (PDAC) ↑ Firmicutes (H) ↑ Proteobacteria no differences in diversity of the oral microbiota among these groups, there were differences in the mean relative proportions of some taxa Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 P.gingivalis: associated with ↑ risk of panreatic cancer (OR 1.6) for low relative abundance and high relative abundance, thus showing a dose-response relationship. A. actinomycetemcomitans: associated with ↑ risk of pancreatic cancer (OR 2.20), more in ever-drinkers (OR 3.03) than in never-drinkers (OR 0,47). Fusobacteria and its genus Leptotrichia: associated with ↓ pancreatic cancer risk (OR 0.94). Carriage of the periodontal pathogens P. gingivalis and A. actinomycetemcomitans and ↓relative abundance of Fusobacteria and its genus Leptotrichia, were associated with subsequent risk of pancreatic cancer, unlikely due to smoking or other confounders. Selection: **** Comparability: ** Exposure: ** Total NOS score: 8 Potential biomarker candidates: Streptococcus, Prevotella, Campylobacter, Granulicatella, Atopobium and Neisseria. (PC2): ↓ Streptococcus and Neisseria, ↑ Granulicatella. (CP): ↓ Streptococcus and ↑ Granulicatella, compared to (PC2). specificity for distinguishing patients with pancreatic cancer from healthy subjects. Using Streptococcus and Granulicatella: 85.7% sensitivity and 55.6% specificity. All samples: mainly Proteobacteria, Actinobacteria, Bacteroidetes, Firmicutes, and Fusobacteria. (PC): ↑ Leptotrichia, ↓ Porphyromonas and Neisseria. No difference in Streptococcus or Granulicatella between (PC) and (H). (PC): ↑ abundance ratio of Neisseria to Porphyromonas. Significant differences in microbiome between (PC) and (H). No differences among the 3 main groups in beta diversity or alpha diversity. The Leptotrichia to Porphyromonas ratio as a potential diagnostic biomarker for pancreas cancer. Selection: *** Comparability: Exposure: ** Total NOS score: 5 Actinobacteria and ↓ Proteobacteria, ↓ Fusobacterium, Neisseria, Haemophilus and Porphyromonas. (GCtn): mainly Prevotella, Veillonella, Leptotrichia, Lactococcus, and Streptococcus. (GCtk): mainly Prevotella, Streptococcus, Actinomyces, Veillonella, and Leptotrichia, ↑ Actinomyces and Streptococcus compared to the others. (OC) ↑ Streptococcus, Staphylococcus, Bacillus, Mycoplasma, Chlamydophila, Pediococcus, Chyseobacterium, Fusobacterium, Prevotella, Escherichia, Treponema A distinct group of viral, bacterial, fungal and parasitic signatures of high significance in ovarian cases Selection: **** Comparability: Exposure: ** Total NOS score: 6 no difference in bacterial communities in the pharynx from patients with LC and VCP Inverse correlations were found between Streptococcus and other bacterial communities, suggesting that potential antagonism may exist among pharyngeal microbiota Selection: ** Comparability: * Exposure: ** Total NOS score: 5 No significant differences between M and MN No significant differences in the relative abundances of bacterial genera or bacterial diversity between the patient groups Selection: ** Comparability: * Exposure: ** Total NOS score: 5 (BC) ↑ Fusobacterium (H) ↑ Veillonella, Streptococcus and Corynebacterium microbial diversity and overall microbiome composition were not significantly different between groups Selection: **** Comparability: * Exposure: ** Total NOS score: 7 (BC) ↑ Actinomyces (HC) ↑ Streptococcus, Bifidobacterium, Lactobacillus, Veillonella Alpha diversity of the bladder cancer group was higher than that of the control group Selection: *** Comparability: * Exposure: ** Total NOS score: 6 Ren, 2019 Shin, 2017 Yang, 2019 Gao, 2017; Zeller, 2014 Yu, 2017 Zeller, 2014 Flemer, 2017 Flemer, 2017 Banerjee, 2017; Hu, 2016; Schmidt, 2014 Guerrero-Preston, 2016 Yu, 2017 Hu, 2016; Schmidt, 2014 Xu, 2017 Gao, 2017; Zeller, 2014; Yang, 2019 Flemer, 2017 Flemer, 2017 Lee, 2016 Olson, 2017 Guerrero-Preston, 2016 Shin, 2017 Yu, 2017 Aviles-Jimenez, 2016 Xu, 2017 Alexander, 2016; Zeller, 2014 Shin, 2017 Guerrero-Preston, 2016 Hu, 2015; Yu, 2017 Olson, 2017 Banerjee, 2017 Yu, 2017 Lee, 2016 Hu, 2016 57.1 (±11.5) years China Melanomas: 69.1 ± 16.9 Melanocytic nevi: 52.9 ± 17.6 Not available 67.4 years sequencing (V3-V4 region) 16S rRNA gene sequencing (V4 region) 16S rRNA gene sequencing (V1-V3 region) Subculturing of the colonies and incubated using 3 different agars. 16S rRNA gene sequencing DNA extraction and 16S rRNA gene analysis of the V3-V6 region. DNA extraction and 16S rRNA gene analysis of the V3-V5 region. DNA extraction and 16S rRNA gene analysis of the V3-V4 region. DNA extraction and 16S rRNA gene analysis of the V1-V3 region. DNA extraction and 16S rRNA gene analysis of the V1-V3 region. Incubated and then aerobic and anaerobic subculturing onto selective agar media. Identified by their morphology and biochemical reactivity. 16S rRNA gene sequencing 16S rRNA gene sequencing (V3-V4 region) 16S rRNA gene sequencing 16S rRNA gene sequencing (V3-V4 region) Shotgun Metagenomic sequencing 16S rRNA gene sequencing (V3-V4 region) DNA extraction and 16S rRNA gene analysis (no region mentioned). mentioned). DNA extraction and 16S rRNA gene analysis of the V3-V4 region. 16S rRNA gene sequencing (V4 region) 16S rRNA gene sequencing (V3-V4 region) 16S rRNA gene sequencing (V1-V2 region) 16S rRNA gene sequencing (V3-V4 region) 16S rRNA gene sequencing (V4 region) Metagenomic sequencing 16S rRNA gene sequencing (V1-V2 region) 16S rRNA gene sequencing (V5-V6 region) 16S rRNA gene sequencing (V3-V5 region) 16S rRNA gene sequencing (V3-V4 region) 16S rRNA gene sequencing (V4 region) 16S rRNA gene sequencing 16S rRNA gene sequencing (V3-V4 region) 16S rRNA gene sequencing (V3-V4 region) 16S rRNA gene analysis of the V3-V4 region (454 pyrosequencing) and qPCR real time qPCR of 16S rRNA using universal primers incorporating the FLX Titanium adapters and a sample barcode sequence. metagenomic shotgun sequencing (Illumina) real time qPCR of 16S rRNA T-RFLP and 16S RNA gene analysis of the V3-V4region (Illumina) 16S rRNA gene analysis (454 pyrosequencing) and real-time qPCR of specific bacteria real time-qPCR and RT-qPCR whole genome shotgun sequencing (Illumina) real-time quantitative PCR of total DNA and 16S rRNA gene analysis of the V3-V4 region (Illumina) 16S rRNA gene sequencing (V2, V4, V8, V3, V6-7, and V9 region) 16S rRNA gene sequencing of the V3-V4 region (Illumina) 16S rRNA gene sequencing (V4 region) 16S rRNA gene sequencing (V3-V5 region) Shotgun Metagenomic sequencing 16S rRNA gene sequencing (V3-V4 region) 16S rRNA gene sequencing DNA extraction and 16S rRNA gene analysis of the V1-V3 region. 16S rRNA gene sequencing (V4 region) 16S rRNA gene sequencing (V1-V4 region) 16S rRNA gene sequencing Pan-pathogen array technology (PathoChip) coupled with next-generation sequencing 16S rRNA gene sequencing (V6-V9 region) 16S rRNA gene sequencing 16S rRNA gene sequencing (V4-V5 region) 16S rRNA gene sequencing (V3-V5 region) 16S rRNA gene sequencing (V1-V3 region) Metatranscriptomic analysis DNA extraction and 16S rRNA gene analysis of the V4 region. Yeast colonization first by agar plates and incubation, then by macro-and microscopic morphology, catalase test and CHROMagar Candida plates, and finally MALDI-TOF analysis. a Candida-specific qPCR. Head space gas chromatography (GC) + several specific and non-specific agar media. DNA extraction and 16S rRNA gene analysis of the V3-V5 region. 16S rRNA gene sequencing (V3-V4 region) 16S rRNA gene sequencing (V3-V4 region) DNA extraction and 16S rRNA gene analysis of the V2-V4 region. 16S rRNA gene sequencing (V4 region) 16S rRNA gene sequencing (V4-V5 region) DNA extraction and 16S rRNA gene analysis of the V3-V4 region. HOMIM array was used for profiling. DNA extraction and 16S rRNA gene analysis using a 'universal' bacterial primer 515F + qPCR. Pan-pathogen array technology (PathoChip) coupled with next-generation sequencing 16S rRNA gene sequencing (V1-V2 and incomplete V3 region) 16S rRNA gene sequencing (V1-V3 region) 16S rRNA gene sequencing (V4 region) 16S rRNA gene sequencing (V3-V4 region) Oral Gut Oral Oral Gut Gastric Gut Gut Gut Oral Oral Gastric Oral Gut Gut & Oral Gut Gut Lung Oral Oral Oral Gastric Bile duct Bile duct Gut Gut Oral Oral Oral & Gastric Oral Oral Gastric Lung Oral 115: CRC: 59,HCs: 56 did not Gao, 2015 Case control Gut microbiome Colorectal cancer cases 22:11 CRC; 11 HCs Kohoutova, 2014 Case control Gut microbiome Colorectal cancer cases 150: 90 in 30 CRC patients (biopsies from caecum, transverse colon and rectum in all patients); 60 in 20 HCs (biopsies from caecum, transverse colon and rectum in all patients) Marchesi, 2011 Case control Gut microbiome Colorectal cancer cases 6 subjects; 12 samples: 6 samples on tumor and 6 from adjacent non-malignant tissue ("off-tumor") Mira-Pascual, 2014 Gut microbiome Colorectal cancer cases 20: 15 case (7tumor, 8 polyp); 5 HCs malignant samples from adjacent mucosa Warren, 2013 Case control samples Gut microbiome Colorectal cancer cases 65 subjects; 130 samples: 65 samples on tumor 65 control samples (off-tumor) Youssef, 2018 Case control Gut microbiome Stomach cancer Colorectal cancer 83 patients, who were diagnosed with different GIT neoplasms, and 13 healthy individuals Goedert, 2015 Case control Gut microbiome Breast cancer cases 96: 48 breast cancer patients, 48 HCs Goedert, 2018 Case control Gut microbiome Breast cancer 48 postmenopausal breast cancer cases and 48 contemporaneous, postmenopausal, normal-mammogram, age-matched controls Case control microbiome breast cancer patients, 25 premenopausal healthy controls, 44 postmenopausal breast cancer patients, and 46 postmenopausal healthy controls Alanee, 2019 Cohort Gut and urinary microbiome Prostate cancer 30 patients 60 samples, one urine and one fecal per patient Golombos, 2018 Case control Gut microbiome Prostate Cancer 20 men with either benign prostatic conditions (n = 8) or intermediate or high risk clinically localized prostate cancer (n = 12) Qi, 2019 Case control Gut microbiome Gastric cancer 116 gastric cancer patients and 88 healthy controls Ren, 2019 Case control Gut microbiome Liver cancer 150 patients with HCC, 40 patients with cirrhosis and cell carcinoma (LSCC), 31 controls with vocal cord polyps (H). Shin, 2017 Case control Oral microbiome Head and neck cancer 72 tissue samples (normal, primary, metastatic) originating from the oral cavity, larynx, pharynx and lymph nodes of 34 HNSCC subjects Wang, 2017 Case control Oral microbiome Head and neck cancer 242 samples from 121 patients Hayes, 2018 Nested case-control Oral microbiome Head and neck cancer 129 incident patient cases of HNSCC and 254 matched controls Case control microbiome samples as well as 20 cancer adjacent normal controls (matched) and 20 oral tissue (uvula) from healthy individuals (non-matched controls) Lee, 2017 Case control Oral microbiome Oral cancer Normal (n = 127) Epithelial precursor lesion (n = 124) Cancer (n = 125) Mok, 2017 Case control Oral microbiome Oral cancer 9 normal, 9, oral potentially malignant disorders, 9 malignant lesions Wolf, 2017 Case control Oral microbiome Oral cancer 11 patients and 11 healthy controls Zhao, 2017 Case control Oral microbiome Oral cancer 80 samples Lim, 2018 Case control Oral microbiome Oral cancer normal healthy controls (n=20), high-risk individuals (n = 11) and OCC and OPC patients (n = 52) Perera, 2018 Case control Oral microbiome Oral cancer 25 OSCC cases and 27 FEP controls Yost, 2018 Case control Oral microbiome Oral cancer 15 samples, including four tumour sites from OSCC subjects, four tumour-adjacent sites from OSCC subjects, four sites from healthy patients who matched the locations of the samples Börnigen, 2017 Case control Oral microbiome Oral squamous cell carcinomas 121 oral cancer patients to 242 age-and gendermatched controls Schmidt, 2014 Case control Oral microbiome Oral cancer cases For study 1 (discovery cohort): 5 pat. with oral cancer (OC1) and 5 healthy controls (H1). For study 2 (confirmation cohort): 10 pat. with oral cancer (OC2), 1 pat. with carcinoma in situ (CIS), 8 pat. with pre-cancer stages (PRE) and 20 healthy controls (H2). Berkovitz, 2016 Case control Oral microbiome Oral cancer cases 60 subjects: 20 pat. (14 m. and 6 fem.) squamous cell cancer (FAC)+ (FAC2), 2 pat. with Fanconi Anaemia and benign oral lesion (FAB) + (FAB2), and 5 healthy controls (H). Homann, 2000 Case control Oral microbiome Oral cancer cases 326 volunteers: 26 pat. with a malignant tumor of the oral cavity (T), 64 alcoholics (A), 24 pat. seeking a dental examination or treatment (DE), 90 unemployed volunteers (UN) and 114 healthy volunteers (H). Marttila, 2013 Case control Oral microbiome Oral cancer cases 90 subjects: 30 pat. with oral squamous cell carcinoma (OSCC), 30 pat. with oral lichenoid disease (OLD) and 30 healthy controls (H). Guerrero-Preston, 2016 Oral microbiome Head and neck cancer cases 17 pat. with head and neck squamous 7 were HPV+ and 4 HPV-. 6 pat with an oral cavity squamous cell carcinoma (OCSCC), all HPV-. Wang, 2019 Case control Oral and lung microbiome Lung cancer 51 patients with primary bronchogenic carcinoma and 15 healthy controls Lu, 2016 Case control Oral (Tongue coat) microbiome Liver cancer 35 patients in the early stages of LC patients with Yang, 2019 Case control Oral microbiome colorectal cancer 231 incident CRC cases and 462 controls Flemer, 2018 Case control Oral microbiome Gut microbiome Colorectal cancer CRC (99 subjects), colorectal polyps (32) or controls (103) Han, 2014 Case control Oral microbiome Colorectal cancer cases 92 subjects: 47 pat. with colorectal cancer (22 with rectal cancer, 25 with colon cancer) (CRC), 45 healthy controls (H). (CRC) Han, 2016 Case control Oral microbiome Colorectal cancer, lung cancer and gastric cancer cases 386 subjects: 90 pat. with colorectal cancer (CRC), 96 pat. with lung cancer (LC), 100 pat. with gastric cancer (GC) and 100 healthy controls (H). Peters, 2017 Case control Oral microbiome Esophageal Cancer EAC cases: 81 EAC matched controls: 160 ESCC cases: 25 ESCC matched controls: 50 Chen, 2015 ( Case control Oral microbiome Esophageal cancer cases 235 subjects: 87 pat. with esophageal squamous cell carcinoma (ESCC), 63 subjects with dysplasia (DYS) and 85 healthy controls (H). Fan, 2018 Case control Oral microbiome Pancreatic cancer 361 incident adenocarcinoma of pancreas and 371 matched controls Fan, 2016 Prospective nested case control Oral microbiome Pancreatic cancer cases CPS II cohort: 170 cases with primary pancreatic adenocarcinoma (PAD1), 170 matched controls (H1). PLCO: 191 cases with primary pancreatic adenocarcinoma (PAD2), 201 matched controls (H2). Farrell, 2012 Case control Oral microbiome Pancreatic cancer cases For discovery phase: 10 pat. with pancreatic cancer (PC1) and 10 matched controls (H1). For independent validation phase: 28 pat. with pancreatic cancer (PC2), 28 (PC), 78 with other diseases (OD) and 22 healthy controls (H). Hu, 2015 Case control Oral microbiome Gastric cancer cases For tongue images, 74 pat. with gastric cancer (GC) and 72 healthy controls (H). For the samples, 34 pat. with gastric cancer, from who 16 had thin coatings (GCtn) and 18 had thick tongue coatings (GCtk), and 17 healthy controls (Hs). Banerjee, 2017 Case control Ovarian microbiome Ovarian cancer 99 ovarian cancer samples and 20 matched (tissue adjacent to the tumor deemed noncancerous by pathological analysis) and 20 unmatched control samples Gong, 2017 Pharyngeal Laryngeal 68 subjects with 2017 Case control microbiome tumor, peri-tumor, and nontumor tissues after radical prostatectomy Salava, 2016 Case control Skin microbiome Skin cancer 15 cutaneous melanomas and 17 benign melanocytic nevi Bučević Popović, 2018 Case control Urinary microbiome Bladder cancer 12 male patients diagnosed with bladder cancer, and from 11 healthy, age-matched individuals Bi, 2019 Case control Urinary microbiome Bladder cancer 29 bladder cancer patients and 26 non-cancer patients Case control (H) Healthy controls specimen brushing samples microbiome Tsay, 2018 Case control (LC) Lung cancer (NLC) Non-cancer diagnoses (H) Healthy controls Airway brushings Airway microbiome Lee, 2016 Cohort (LC) Lung cancer (BML) Begnin mass-like lesion Bronchoalveolar fluid Lung microbiome Carpagnano, 2014 Case control (NSCLC) Non-small cell lung cancer, (H) Healthy controls. Exhaled breath condensate (EBC) and bronchial brushing. Lung microbiome Cameron, 2017 Case control (LC) Lung cancer Sputum samples sputum microbiome common bile duct Chng, 2016 Case control (CCA) Cholangiocarcinoma (OVa) Opisthorchis viverrini associated Tissue samples of the liver, of the bile duct, bile fluid samples and gastric mucosa samples. Bile duct microbiome Hieken 2016 Case control (BBD-non-atypia) Benign breast disease without atypia, (IBC) Invasive breast cancer Breast tissue Breast tissue microbiome (H) Healthy controls Audirac-Chalifour, 2016 Case control (CC) Cervical cancer, (SIL) Squamous intraepithelial lesions (H) Healthy controls with no cervical lesions Cervical scraping swabs and fresh cell biopsies. Cervical microbiome Oh, 2015 Case control (CIN) Cervical intraepithelial neoplasia (H) Healthy controls. Cervical swab Cervical microbiome Seo, 2016 Case control (CIN) Cervical intraepithelial neoplasia Cervical swab Cervical microbiome Mannell, 1983 Case control (EC) Esophagus carcinoma (H) Healthy controls. Esophageal aspirates Esophageal microbiome Castaño-Rodríguez, 2017 Case control (GC) Gastric cancer Antral gastric biopsies gastric microbiome Li, 2017 Case control (GC) Gastric cancer Endoscopic gastric biopsies Gastric microbiome Yu, 2017 Case control (GC) Gastric cancer gastric tissue Gastric microbiome Yu, 2017 Case control (GC) Gastric cancer Paired non-malignant and tumor tissues Gastric microbiome (IM) intestinal metaplasia (GC) Gastric cancer Hu, 2018 Case control (SG) Superficial gastritis (GC) Gastric adenocarcinoma gastric wash samples Gastric microbiome Liu, 2019 Cohort (GC) Gastric cancer Gastric tissues Gastric microbiome Aviles-Jimenez, 2014 Case control (NAG) Non-atrophic gastritis, (IM) Intestinal metaplasia (GC) Intestinal-type of gastric cancer. Gastric samples Gastric microbiome Nasrollahzad eh, 2015 Case control (ESCC) Esophageal squamous cell carcinoma, (ED) Esophageal sqoamous dysplasia, (DC) Diseased controls, (H) Healthy controls. Gastric tissue Gastric microbiome Baxter, 2016 Case control (CRA) Adenomas (CRC) Carcinomas (H) Healthy Stool Gut microbiome Sinha, 2016 Case control (CRC) Colorectal cancer lyophilized feces Gut microbiome Amitay, 2017 Case control (CRC) Colorectal cancer Stool Gut microbiome Flemer, 2017 Case control (CRC) Colorectal cancer faecal and mucosal samples Gut microbiome Gao, 2017 Case control (CRC) Colorectal cancer Tissue samples including tumor and adjacent normal mucosal tissue Gut microbiome Yu, 2017 Case control (CRC) Colorectal cancer Stool Gut microbiome Allali, 2018 Case control (CRC) Colorectal cancer Stool Gut microbiome Burns, 2018 Case control (CRC) Colorectal cancer tumors and matched normal tissues Gut microbiome Hale, 2018 Case control (CRC) Colorectal cancer (MMR) Mismatch repair (dMMR) deficient MMR or (pMMR) proficient MMR Tumor and normal-adjacent tissue Gut microbiome Loke, 2018 Case control (CRC) colorectal cancer (H) Healthy controls paired tumor and unaffected (normal) surgical biopsy tissues Gut microbiome Mori, 2018 Case control (HP) hyperplastic polyps, (LRA) low-risk adenomas, (HRA) high-risk adenomas (ADK) adenocarcinomas Stool Gut microbiome (H) Healthy subjects Shah, 2018 Case control (CRC) tumor biopsies tumor biopsy and tumor-adjacent biopsy or fecal samples Gut microbiome Zhang, 2018 Case control (CRC) Colorectal cancer (A-CRA) Advanced colorectal adenoma Stool Gut microbiome Saito, 2019 Case control (CRC) Advanced colorectal carcinoma (CRA) Colorectal adenoma Colonoscopy aspirates Gut microbiome (PA) Polypoid adenomas Ahn, 2013 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Amiot, 2015 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Feng, 2015 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Fukugaiti, 2015 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Kasai, 2016 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Mira-Pascual, 2014 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Ohigashi, 2013 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome Zeller, 2014 Case control (CRC) Colorectal cancer (H) Healthy controls Fecal samples Gut microbiome samples were frozen and stored at -80°C until further use. Flemer, 2016 Case control (CRC) Colorectal cancer (H) Healthy controls colon tissue samples Gut microbiome collected during surgery/colonoscopy. All were placed in liquid nitrogen immediately and transported to the laboratory within 30 min of collection. phosphate solution. Sterile biopsy forceps (Olympus) were used for every single biopsy. Each biopsy specimen of the colonic mucosa was immediately inserted into a transport liver-enriched broth. unaffected control tissue from 65 subjects. Youssef, 2018 Case control (GIT) gastrointestinal tract (RN) rectal neoplasm (CN) colon neoplasm (GN) stomach neoplasm Stool Gut microbiome Goedert, 2015 Case control (BC) Breast cancer Fecal samples Gut microbiome Goedert, 2018 Case control (BC) Breast cancer Stool Gut microbiome Alanee, 2019 Cohort (PC) Prostate cancer Rectal swab and first voided urine after a prostate massage Gut and urinary microbiome Golombos, 2018 Case control (PC) Prostate Cancer (H) Controls Stool Gut microbiome Qi, 2019 Case control (GC) Gastric cancer Stool Gut microbiome Ren, 2019 Case control (HCC) hepatocellular carcinoma Stool Gut microbiome Gong, 2013 Case control (LSCC) Laryngeal squamous cell carcinoma, (H) Controls with vocal cord polyps. Tissue samples of the larynx Laryngeal microbiome Shin, 2017 Case control (HNSCC) Head and neck squamous cell carcinoma Tissues from the oral cavity, larynx-pharynx, and lymph nodes Oral microbiome Wang, 2017 Case control (HNSCC) Head and neck squamous cell carcinoma Tumor resection specimens Oral microbiome Hu, 2016 Case control (OSCC) oral squamous cell carcinoma (OLK) oral leukoplakia (H) healthy controls microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Tongue coat microbiome Oral microbiome Gut microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Oral microbiome Ovarian microbiome Pharyngeal microbiome Skin microbiome Urinary microbiome Urinary * * microbiome 1 1 1 1 2 1 1 1 1 3 1 1 2 1 3 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 Nonstimulated saliva Oral Banerjee, 2017 Case control (OCSCC/OPSCC) oropharyngeal squamous cell carcinomas Carcinomas taken from tongue, base of tongue, tonsil, floor of mouth, cheek and predominantly oropharynx Mok, 2017 Case control (C) Cancer (OPMD) Potentially malignant disorders (H) Normal Swab samples Wolf, 2017 Case control (OSCC) Oral squamous cell carcinomas (H) Healthy saliva samples Zhao, 2017 Case control (OSCC) Oral squamous cell carcinomas Lesion samples and anatomically matched normal samples Hsiao, 2018 Case control (OSCC) Oral squamous cell carcinomas (H) Healthy saliva samples cancers Perera, 2018 Case control (OSCC) oral squamous cell carcinoma (FEP) fibroepithelial polyp fresh biopsies Yost, 2018 Case control (OSCC) Oral squamous cell carcinoma tumor biopsy and tumor-adjacent biopsy Schmidt, 2014 Case control (OC) Oral cancer, (HC) Healthy controls. (CIS) Carcinoma in situ, (PRE) Pre-cancer stages Oral swab Berkovitz, 2016 Case control (OSCC) Oral squamous cell carcinoma. (H) Healthy controls Oral swab Fanconi Anaemia and benign oral lesion (H) Healthy controls. Homann, 2000 Case control (T) Malignant tumor of the oral cavity. (A) Alcoholics. (DE) Dental examination or treatment. (UN) Unemployed volunteers. (H) Healthy volunteers. Saliva disease. (H) Healthy controls. Guerrero-Preston, 2016 Cohort study (1 year follow-up of patients and controls nested in cohort study) (HNSCC) Head and neck squamous cell carcinoma, (HC) Healthy controls. (OPSCC) Oropharyngeal squamous cell carcinoma. (OCSCC) Oral cavity squamous cell carcinoma Saliva and tumor samples Lu, 2016 Case control (LC) liver carcinoma patients with cirrhosis (H) Healthy Tongue coat Flemer, 2018 Case control (CRC) colorectal cancer oral swabs, colonic mucosae and stool Han, 2014 Case control (CRC) Colorectal cancer. (H) Healthy controls Tongue coating samples and images. (H) Healthy controls Peters, 2017 Case control (EAC) esophageal adenocarcinoma and (ESCC) esophageal squamous cell carcinoma mouthwash samples (H) Healthy controls. Olson, 2017 (PDAC) pancreatic ductal adenocarcinoma (IPMN) intraductal papillary mucinous neoplasms (H) healthy controls Saliva samples Fan, 2016 Prospective nested case control (PAD) Primary pancreatic adenocarcinoma. (H) Matched controls. Oral mouthwash (CP) Chronic pancreatitis Torres, 2015 Case control (PC) Pancreatic cancer. (OD) Other diseases. (H) Healthy controls Saliva cancer, thin coatings, (GCtk) Gastric cancer, thick tongue coatings Banerjee, 2017 Case control (OC) Ovarian cancer Ovarian cancer samples Gong, 2017 Case control (LC) laryngeal carcinoma (VCP) vocal cord polyps swab samples Salava, 2016 Case control (M) Melanomas (MN) Melanocytic nevi Non-invasive swab specimens from melanocytic skin lesions Bučević Popović, 2018 Case control (BC) Bladder cancer Urine Bi, 2019 Case control (BC) Bladder cancer (H) Healthy control Urine NS: Non-significant NM: Not mention Actinobacteria Decreased in OC Actinobacteria Increased in HCC Actinobacteria Decreased in HNSCC Actinobacteria Increased in CRC Actinobacteria Decreased in CRC Bacteroidetes Increased in GC Bacteroidetes Increased in CRC Bacteroidetes class1 Decreased in CRC Bacteroidetes class 2 Increased in CRC Bacteroidetes Increased in OC Bacteroidetes Decreased in HNSCC Firmicutes Increased in GC Firmicutes Decreased in OC Firmicutes Increased in CRC Firmicutes Decreased in CRC Firmicutes class 1 Decreased in CRC Firmicutes class II Increased in CRC Firmicutes Increased in LC Firmicutes Decreased in PC Firmicutes Increased in HNSCC Firmicutes Decreased in HNSCC Fusobacteria Increased in GC Fusobacteria Increased in ECCA Planctomycetes Increased in ECCA Proteobacteria Decreased in CRC Proteobacteria Increased in CRC Proteobacteria Increased in HNSCC Proteobacteria Decreased in HNSCC Proteobacteria Decreased in GC Proteobacteria Decreased in PC Proteobacteria Increased in OC Spirochaetes Increased in GC TM7 Increased in LC TM7 Increased in OC *All studies in same direction CRC= Colorectal cancer ECCA= Extrahepatic cholangiocarcinoma GC= Gastric Cancer HCC= Hepato cellular carcinoma HNSCC= Head and neck squamous cell carcinoma LC= lung cancer OC= Oral cancer PC= Pancreatic cancer Taxonomic level -	pat. (LC) and 8 never 45-72 years. cancer °smokers breast disease and age: 49 years intraepithelial chronic diseases, drug status, menopausal status, adenocarcinoma NSAIDs, anti-weeks. squamous dysplasia Only individuals with control microbiome (pMMR): 63 (13) radiation in the 2 tumor location, stage adenoma patients, with prior diagnoses of prior to surgery tumor and 18 chemotherapy and/or 63.3 years [±SD: polyposis (FAP) and months; any antibiotic 49.67±8.56 habit cohort, smoking, alcohol, and including diabetes, or if (OCSCC) mean underwent prior index. 45 pat. and 47 °cases with diagnosis of hormones or antibiotics in functional profiles. Case control 131 healthy controls tumour sites and with oral squamous cell carcinoma was divided into a matched controls Case control microbiome carcinoma laryngeal cancer and	161 202 203 207 208 209

Class Cancer and Result Number of studies Microbiota body site

	Gao, 2015	Chen, 2012	Sinha, 2016	Gao, 2015	Alanee, 2019	Gao, 2015
	Gut	Gut	Gut	Gut	Gut & Urinary	Gut
	Decreased in CRC 1	Increased in CRC 1	Decreased in CRC 1	Decreased in CRC 1	Decreased in PRO 1	Decresed in CRC 1
	Alphaproteobacteria	Bacilli	Clostridia	Gammaproteobacteria	Lactobacilli	Sphingobacteria	*All studies in same direction	CRC= Colorectal cancer	PRO= Prostate cancer	Taxonomic level -

Order Cancer and Result Number of studies Microbiota body site

	Reference Microbiota body site	Chen, 2012 Gut	Youssef, 2018 Gut	Yang, 2019 Gut	Chen, 2012 Gut	Wu, 2013 Gut	Goedert, 2015 Gut	Chen, 2012 Gut	Ohigashi, 2013 Gut	Mira-Pascual, 2014; Zackular, 2014 Gut	Chen, 2012 Gut	Yang, 2019 Oral	Wu, 2013 Gut	Castaño-Rodríguez, 2017 Gut	Gastric Nasrollahzadeh, 2015 Chen, 2012; Wu, 2013 * Gut	Gut Wu, 2013 Guerrero-Preston, 2016 Oral	Gastric Nasrollahzadeh, 2015 Nasrollahzadeh, 2015 Gastric	Cavarretta, 2017 Goedert, 2015 Prostate Gut	Mira-Pascual, 2014 Ahn, 2013; Baxter, 2016; Burns, 2015; Flemer, 2018; Sinha, 2016; Zackular, 2014 Gut * Gut & Oral	Thomas, 2016 Aviles-Jimenez, 2014; Wang, 2016 Gut * Gastric	Youssef, 2018 Gut	Aviles-Jimenez, 2016 Bile duct	Saito, 2019 Gut	Chen, 2012; Chen, 2012 * Gut & Oral	Chen, 2012; Wu, 2013 * Gut	Chen, 2012 Gut	227 228
	Cancer and Result Number of studies Taxonomic level -Family Taxonomic level -	Decreased in CRC 1 Alcaligenaceae	Decreased in rectal cancer 1 Bifidobacteriaceae	Increased in CRC 1 Bifidobacteriaceae	Decreased in CRC 1 Bifidobacteriaceae	Increased in CRC 1 Campylobacteraceae	Increased in BC 1 Clostridiaceae	Increased in CRC 1 Coriobacteriaceae	Decreased in CRC 1 Enterobacteriaceae	Increased in CRC 2 Enterobacteriaceae	Increased in CRC 1 Erysipelotricaceae	Decreased in CRC 1 Erysipelotricaceae	Increased in CRC 1 Eubacteriaceae	Increased in GC 1 Fusobacteriaceae	Clostridiales Increased in ESCC 1 Increased in CRC 2 Fusobacteriaceae	clostridiales Family XI Incertae sedis Increased in CRC 1 Decreased in HNSCC 1 Gemellaceae	Erysipelotrichales Increased in ESCC 1 Decreased in ESCC 1 Helicobacteraceae	Lactobacillales Decreased in PRO 1 Decreased in BC 1 Lachnospiraceae	Methanobacteriales Increased in CRC 1 Decreased in CRC 6 Lachnospiraceae	Planctomycetes Decreased in CRC 1 Increased in GC 2 Lachnospiraceae	Decreased in colon cancer 1 Lactobacillaceae	*All studies in same direction 1 Increased ECCA Methylophilaceae	1 Decreased in CRC Moraxellaceae	CRC= Colorectal cancer 2 Increased in CRC Peptostreptococcaceae	2 Increased in CRC ECCA= Extrahepatic cholangiocarcinoma Porphyromonadaceae	1 Increased in CRC PRO= Prostate cancer Prevotellaceae

Family Cancer and Result Number of studies Microbiota body site Reference

	Pseudomonadaceae Increased in FAC 1 Oral Henrich, 2014 Reference Reference Chen, 2012 Burns, 2015 Rikenellaceae Decreased in CRC 1 Gut Li, 2017 Hieken 2016 Chen, 2015 Ruminococcaceae Decreased in CRC 5 Burns, 2015; Chen, 2012; Shah, 2018; Warren, 2013; Baxter, 2016 Ahn, 2013; Kasai, 2016; Vogtmann, 2016; Saito, 2019 Goedert, 2015 Gut Lim, 2018 Chen, 2015 Wu, 2013 Gut Chen, 2015 Farrell, 2012 Cavarretta, 2017 Prostate Hosgood, 2014 Perera, 2018 Yang, 2019 Oral Hu, 2016 Hu, 2016 Cavarretta, 2017 Prostate Alanee, 2019 Thomas, 2016 Weir, 2013 Banerjee, 2017 Chen, 2012 Amiot, 2015; Feng, 2015; Loke, 2018; Thomas, 2016 Gao, 2015 Burns, 2015; Loke, 2018 Cameron, 2017 Alanee, 2019; Liss, 2018 Thomas, 2016; Zeller, 2014; Gao, 2015 Bi, 2019 Lim, 2018; Wolf, 2017 * Gut Ruminococcaceae Increased in BC 1 Staphylococcaceae Increased in CRC 1 Staphylococcaceae Increased in PRO 1 Streptococcaceae Decreased in CRC 1 Streptococcaceae Decreased in PRO 1 *All studies in same direction BC= Breast cancer CRC= Colorectal cancer ECCA= Extrahepatic cholangiocarcinoma Taxonomic level -Genus Number of studies Microbiota body site Achromobacter 1 Gastric Acholeplasma 1 Oral Actinobacillus 1 Oral Actinobacillus 1 Oral Abiothrophia 1 Lung Abiothrophia 1 Oral Acetanaerobacterium 1 Gut & Urinary Acidaminobacter 1 Gut Acidocella 1 Gut Acidovorax 1 Gut Acinetobacterjunii 1 Lung Acinetobacter 3 * Gut Actinomyces 2 * Oral Taxonomic level -Genus Number of studies Microbiota body site Anaerotruncus 1 Gut Atopium 1 Breast tissue Atopobium 4 * Gut Atopobium 1 Oral Atopobium 1 Oral Atopobium 1 Oral Bacillus 1 Oral Bacillus 1 Gut Bacillus 1 Ovarian Bacteroides 4 Gut Bacteroides 2 Gut Bacteroides 2 Gut & Urinary Bifidobacterium 1 Urinary Cancer and Result Increased in GC Decreased in ESCC Increased in OC Decreased in ESCC Increased in LC Decreased in OC Decreased in PRO Increased in CRC Decreased in CRC Decreased in CRC Increased in LC Decreased in CRC Increased in OC Cancer and Result Increased in CRC Increased in IBC Increased in CRC Decreased in ESCC Increased in PC Increased in OC Increased in OC Decreased in CRC Increased in OVC Increased in CRC Decreased in CRC Increased in PRO Decreased in bladder cancer	Bi, 2019 Chen, 2012; Feng, 2015; Ohigashi, 2013; Zeller, 2014 FAC= Fanconi Anaemia and oral squamous cell cancer Actinomyces 1 Urinary Bifidobacterium 4 * Gut Increased in bladder cancer Zeller, 2014 Wang, 2017 Actinomyces 1 Oral Bilophila 1 Gut Blautia PRO= Prostate cancer GC= Gastric cancer, HNSCC= Head and neck squamous cell carcinoma Decreased in HNSCC Decreased in CRC Increased in CRC Decreases in CRC	Hu, 2018 Aggregatibacter 1 Gastric Increased in GC	Guerrero-Preston, 2016 Aggregatibacter 1 Oral Decreased in	HNSCC	Chen, 2015 Aggregatibacter 1 Oral Decreased in ESCC	Lim, 2018 Aggregatibacter 1 Oral Increased in OC	Fan, 2018 Aggregatibacter 1 Oral Increased in PC	Banerjee, 2017 Alcaligenes 1 Oral Increased in OC	Loke, 2018 Alistipes 1 Gut Increased in CRC	Hu, 2018 Alloprevotella 1 Gastric Increased in GC	Wu, 2013 Anaerococcus 1 Gut Increased in CRC	Chen, 2012 Anaerostipes 1 Gut Decreased in CRC

Table S2b -

 S2b Results from qPCR, specimen collection cards.

	Sample name Sample name GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (-80°C) GenSaver (-80°C) GenCollect (RT) GenSaver (-80°C) GenCollect (RT) GenSaver (-80°C) GenCollect (RT) GenSaver (-80°C) GenCollect (RT) GenSaver (-80°C) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT) GenSaver (RT) GenCollect (RT)	Bacterial DNA Bacterial DNA concentration (ng/μl) concentration 55.42 (ng/μl) 28.69 41.59 42.52 27.61 44.10 45.63 43.09 33.67 48.76 16.82 37.77 35.18 40.75 69.91 53.93 62.19 38.49 8.29 21.25 69.78 16.17 110.60 53.27 43.43 52.56 71.65 12.37 66.12 50.14 26.67 87.88 42.69 46.83 15.80 49.92 45.24 50.71 44.84 27.94 24.81 38.75 27.90 25.90 56.88 46.60 31.05 42.35 8.70 20.64 28.24 30.37 55.23 60.90 35.33 41.14 9.55 18.60 41.18 39.20 81.09 45.11 50.51 46.01 43.55 7.51 53.37	Bacterial Bacterial quantity/ slices quantity/ slices 4.27E+10 2.85E+10 5.70E+10 3.66E+10 3.12E+10 4.09E+10 5.11E+10 2.52E+10 3.70E+10 4.01E+10 1.79E+10 4.49E+10 1.97E+10 4.50E+10 4.96E+10 6.16E+10 5.26E+10 3.76E+10 2.47E+09 2.06E+10 3.88E+10 9.96E+09 8.80E+10 5.66E+10 2.59E+10 6.57E+10 3.75E+10 8.34E+09 5.11E+10 3.95E+10 1.24E+10 1.10E+11 1.97E+10 5.08E+10 8.20E+09 4.81E+10 1.59E+10 6.49E+10 2.18E+10 1.75E+10 1.42E+10 1.74E+10 1.77E+10 1.21E+10 3.95E+10 1.79E+10 1.73E+10 1.17E+10 4.24E+09 1.17E+10 1.06E+10 1.49E+10 3.31E+10 2.71E+10 3.91E+10 1.88E+10 4.77E+09 8.33E+09 3.49E+10 2.30E+10 1.39E+11 3.52E+10 9.27E+10 2.32E+10 4.40E+10 3.03E+09 1.08E+11	log(Bacterial log(Bacterial quantity/slices) quantity/slices) 10.63 10.76 10.46 10.49 10.56 10.71 10.61 10.57 10.40 10.25 10.60 10.30 10.65 10.70 10.65 10.72 10.79 9.39 10.58 10.59 10.31 10.94 10.00 10.41 10.75 10.57 10.82 10.71 9.92 10.09 10.60 10.29 11.04 9.91 10.71 10.20 10.68 10.34 10.81 10.15 10.24 10.25 10.24 10.60 10.08 10.24 10.25 9.63 10.07 10.03 10.07 10.52 10.17 10.59 10.43 9.68 10.28 10.54 9.92 11.14 10.36 10.97 10.55 10.64 10.37 9.48 11.03
	GenSaver (RT)	31.27	1.43E+10	10.15
	GenSaver (RT)	71.45	4.64E+10	10.67
	GenSaver (RT)	18.26	9.40E+09	9.97
	GenSaver (RT)	29.20	1.51E+10	10.18
	GenSaver (RT)	54.59	3.77E+10	10.58
	GenCollect 01 (-80°C)	15.68	1.73E+10	10.24
	GenCollect 02 (-80°C)	27.49	2.79E+10	10.44
	GenCollect 03 (-80°C)	44.97	5.52E+10	10.74
	GenCollect 04 (-80°C)	70.51	6.26E+10	10.80
			249

Table S2c -

 S2c Results from qPCR, FIT tubes.

	Sample name Sample name Sample name Sample name Sample name OC-Auto Sampling 05 (+30°C) Hemotrust 10 (-80°C) Specimen Collection Container A 15 (-80°C) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 06 (+30°C) Hemotrust 11 (-80°C) Specimen Collection Container A 16 (-80°C) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 07 (+30°C) Hemotrust 12 (-80°C) Specimen Collection Container A 17 (-80°C) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 08 (+30°C) Hemotrust 13 (-80°C) Specimen Collection Container A 18 (-80°C) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 09 (+30°C) Hemotrust 14 (-80°C) Specimen Collection Container A 19 (-80°C) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 10 (+30°C) Hemotrust 15 (-80°C) Specimen Collection Container A 01 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 11 (+30°C) Hemotrust 16 (-80°C) Specimen Collection Container A 02 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 12 (+30°C) Hemotrust 17 (-80°C) Specimen Collection Container A 03 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 13 (+30°C) Hemotrust 18 (-80°C) Specimen Collection Container A 04 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 14 (+30°C) Hemotrust 19 (-80°C) Specimen Collection Container A 05 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 15 (+30°C) Hemotrust 01 (RT) Specimen Collection Container A 06 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 16 (+30°C) Hemotrust 02 (RT) Specimen Collection Container A 07 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 17 (+30°C) Hemotrust 03 (RT) Specimen Collection Container A 08 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 18 (+30°C) Hemotrust 04 (RT) Specimen Collection Container A 09 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling 19 (+30°C) Hemotrust 05 (RT) Specimen Collection Container A 10 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling (screening ) 01 Hemotrust 06 (RT) Specimen Collection Container A 11 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling (screening ) 02 Hemotrust 07 (RT) Specimen Collection Container A 12 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling (screening ) 03 Hemotrust 08 (RT) Specimen Collection Container A 13 (RT) One-Step FOB (RT) OC-Auto Sampling (-80°C) OC-Auto Sampling (-80°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+4°C) OC-Auto Sampling (+30°C) OC-Auto Sampling (+30°C) OC-Auto Sampling (+30°C) OC-Auto Sampling (+30°C) Hemotrust 09 (-80°C) Specimen Collection Container A 14 (-80°C) One-Step FOB 19 (-80°C) Hemotrust 08 (-80°C) Specimen Collection Container A 13 (-80°C) One-Step FOB 18 (-80°C) Hemotrust 07 (-80°C) Specimen Collection Container A 12 (-80°C) One-Step FOB 17 (-80°C) Hemotrust 06 (-80°C) Specimen Collection Container A 11 (-80°C) One-Step FOB 16 (-80°C) Hemotrust 05 (-80°C) Specimen Collection Container A 10 (-80°C) One-Step FOB 15 (-80°C) Hemotrust 04 (-80°C) Specimen Collection Container A 09 (-80°C) One-Step FOB 14 (-80°C) Hemotrust 03 (-80°C) Specimen Collection Container A 08 (-80°C) One-Step FOB 13 (-80°C) Hemotrust 02 (-80°C) Specimen Collection Container A 07 (-80°C) One-Step FOB 12 (-80°C) Hemotrust 01 (-80°C) Specimen Collection Container A 06 (-80°C) One-Step FOB 11 (-80°C) OC-Auto Sampling (screening ) 19 Specimen Collection Container A 05 (-80°C) One-Step FOB 10 (-80°C) OC-Auto Sampling (screening ) 18 Specimen Collection Container A 04 (-80°C) One-Step FOB 09 (-80°C) OC-Auto Sampling (screening ) 17 Specimen Collection Container A 03 (-80°C) One-Step FOB 08 (-80°C) OC-Auto Sampling (screening ) 16 Specimen Collection Container A 02 (-80°C) One-Step FOB 07 (-80°C) OC-Auto Sampling (screening ) 15 Specimen Collection Container A 01 (-80°C) One-Step FOB 06 (-80°C) OC-Auto Sampling (screening ) 14 Hemotrust 19 (RT) One-Step FOB 05 (-80°C) OC-Auto Sampling (screening ) 13 Hemotrust 18 (RT) One-Step FOB 04 (-80°C) OC-Auto Sampling (screening ) 12 Hemotrust 17 (RT) One-Step FOB 03 (-80°C) OC-Auto Sampling (screening ) 11 Hemotrust 16 (RT) One-Step FOB 02 (-80°C) OC-Auto Sampling (screening ) 10 Hemotrust 15 (RT) One-Step FOB 01 (-80°C) OC-Auto Sampling (screening ) 09 Hemotrust 14 (RT) Specimen Collection Container A 19 (RT) OC-Auto Sampling (screening ) 08 Hemotrust 13 (RT) Specimen Collection Container A 18 (RT) OC-Auto Sampling (screening ) 07 Hemotrust 12 (RT) Specimen Collection Container A 17 (RT) OC-Auto Sampling (screening ) 06 Hemotrust 11 (RT) Specimen Collection Container A 16 (RT) OC-Auto Sampling (screening ) 05 Hemotrust 10 (RT) Specimen Collection Container A 15 (RT) OC-Auto Sampling (screening ) 04 Hemotrust 09 (RT) Specimen Collection Container A 14 (RT) One-Step FOB (RT)	Bacterial DNA Bacterial DNA concentration (ng/μl) Bacterial DNA concentration (ng/μl) Bacterial DNA concentration (ng/μl) Bacterial DNA concentration (ng/μl) concentration 2.61 3.40 1.72 -2.77 (ng/μl) 2.44 2.78 3.31 2.52 -1.99 5.27 5.17 5.79 2.00 1.27 4.69 3.49 2.50 2.08 -3.43 3.11 3.74 -0.68 1.79 -0.11 7.31 2.76 2.24 1.52 -0.97 4.44 3.08 5.49 1.41 0.41 5.38 3.65 1.95 2.35 5.57 5.66 3.23 3.66 0.94 -1.82 4.24 1.55 3.97 3.30 -1.72 3.00 4.11 1.68 0.89 -3.76 4.71 3.77 4.91 2.55 0.15 4.13 2.57 8.42 1.97 2.65 3.32 4.69 2.57 1.90 -4.11 1.34 5.14 4.55 0.89 -2.00 3.93 1.41 4.69 1.41 -1.07 7.07 4.26 3.31 2.41 0.78 3.21 4.18 4.44 1.29 3.66 4.39 4.73 5.18 2.49 3.23 3.27 4.15 3.06 3.42 3.71 4.69 4.13 3.66 3.74 2.51 2.28 5.22 5.07 3.86 2.84 4.04 1.19 5.34 3.10 2.60 3.40 0.27 2.53 4.45 1.74 4.79 2.95 1.86 -1.81 2.56 1.47 7.92 3.15 0.83 5.01 5.99 3.35 -5.18 3.88 2.62 -1.32 1.93 1.37 3.91 2.72 2.13 0.30 5.00 1.50 0.43 2.90 1.30 -1.57 2.55 2.19 2.94 4.09 1.41 0.94 1.94 1.06 0.76 1.17 3.94 4.58 2.84 4.21 4.58 4.05 2.06 -0.12 3.32 2.74 -0.87 2.67 2.15 3.20 2.70 -0.43 1.79 5.17 3.79 1.96 3.05 6.29 1.43 2.02 1.84 2.14 3.02 2.20 1.19 3.16 4.89 1.20 4.87	Bacterial Bacterial quantity/ mL Bacterial quantity/ mL Bacterial quantity/ mL Bacterial quantity/ mL quantity/ mL 1.26E+09 5.95E+09 2.17E+08 7.15E+08 1.04E+09 7.33E+08 4.80E+09 1.91E+09 5.87E+08 1.93E+09 1.67E+09 1.03E+10 1.55E+09 3.19E+09 2.84E+09 2.02E+09 7.15E+09 1.22E+09 7.21E+08 4.69E+08 3.35E+09 4.12E+08 1.05E+09 1.41E+09 3.46E+09 1.35E+09 3.32E+09 4.29E+08 1.16E+09 2.89E+09 1.33E+09 1.18E+10 1.53E+08 2.03E+09 2.07E+09 3.12E+09 3.52E+09 1.12E+09 8.00E+09 8.47E+08 2.27E+09 4.75E+09 2.22E+08 1.92E+09 2.73E+09 1.30E+09 6.41E+09 4.68E+08 3.44E+09 1.01E+09 7.88E+08 2.02E+09 4.66E+08 3.66E+08 1.32E+09 1.94E+09 5.93E+09 3.68E+08 5.94E+09 6.99E+09 9.67E+08 1.19E+10 5.64E+08 4.30E+09 1.70E+09 2.13E+09 2.19E+09 2.46E+08 1.61E+08 9.35E+07 2.21E+09 2.98E+09 1.85E+08 2.62E+09 1.75E+09 2.32E+08 3.58E+09 1.13E+08 1.83E+09 6.45E+09 8.73E+08 2.60E+09 6.92E+08 2.35E+09 6.95E+08 1.47E+09 3.82E+09 4.80E+08 7.04E+09 1.41E+09 4.75E+09 6.51E+08 9.66E+08 1.42E+09 6.19E+08 1.57E+09 1.44E+09 1.59E+09 2.81E+09 5.47E+07 4.66E+09 1.81E+09 5.93E+09 5.23E+08 1.39E+08 1.04E+09 3.33E+09 1.65E+09 1.18E+09 2.30E+09 3.42E+08 3.23E+08 2.07E+09 4.16E+08 6.27E+09 6.46E+07 6.99E+09 7.10E+09 1.43E+09 8.12E+09 4.82E+09 1.33E+09 2.83E+09 5.23E+09 7.40E+08 4.59E+09 3.43E+09 7.25E+08 4.94E+09 4.87E+09 1.15E+08 3.00E+08 4.69E+09 1.59E+09 5.25E+09 3.94E+09 1.06E+09 6.93E+09 3.39E+09 1.48E+09 2.34E+09 1.52E+09 4.59E+08 3.14E+09 7.79E+08 3.87E+08 1.34E+09 4.36E+08 3.11E+08 7.46E+09 1.61E+09 7.91E+08 3.03E+09 1.84E+08 8.44E+08 5.10E+09 7.31E+07 4.51E+09 4.33E+09 6.69E+08 3.68E+09 4.63E+09 1.51E+09 2.23E+09 1.89E+09 3.11E+08 5.52E+08 1.98E+09 6.81E+08 1.41E+09 4.70E+09 9.03E+08 1.43E+08 4.64E+08 6.21E+08 3.55E+09 2.67E+08 5.27E+08 8.45E+09 1.28E+08 3.72E+08 2.40E+09 2.97E+08 7.82E+08 2.67E+08 2.22E+08 2.77E+08 3.96E+09 3.13E+07 4.46E+09	log(Bacterial log(Bacterial quantity/ mL) log(Bacterial quantity/ mL) log(Bacterial quantity/ mL) log(Bacterial quantity/ mL) quantity/ mL) 9.10 9.77 8.34 8.85 8.87 9.68 9.28 8.77 9.02 9.22 10.01 9.19 9.50 9.29 9.31 9.85 9.09 8.86 9.45 9.53 8.61 9.02 9.15 8.67 9.13 9.52 8.63 9.06 9.54 9.12 10.07 8.19 9.31 9.46 9.49 9.55 9.05 9.90 9.32 9.36 9.68 8.35 9.28 8.93 9.11 9.81 8.67 9.54 9.44 8.90 9.31 8.67 8.56 9.01 9.29 9.77 8.57 9.77 9.12 8.99 10.08 8.75 9.63 9.84 9.33 9.34 8.39 8.21 9.23 9.34 9.47 8.27 9.42 7.97 8.37 9.55 8.05 9.26 9.24 8.94 9.41 8.84 9.37 9.81 9.17 9.58 8.68 9.85 8.84 8.62 9.80 7.81 9.84 9.32 9.85 9.15 9.91 8.51 9.68 9.12 9.45 8.53 9.72 8.87 9.66 9.36 9.53 8.86 9.69 9.07 9.69 8.06 8.48 9.22 9.67 9.20 9.72 9.52 9.60 9.02 9.84 9.02 9.53 9.17 9.37 8.14 9.18 8.66 9.50 8.72 8.89 8.59 9.13 9.77 8.64 8.49 9.87 9.26 9.21 8.90 9.48 9.67 8.27 8.93 9.71 7.74 7.86 9.65 9.64 9.45 8.83 9.57 9.67 9.20 9.18 9.35 9.28 9.16 8.49 8.74 9.30 9.20 8.83 9.15 9.67 8.79 8.96 8.15 8.67 9.15 8.79 9.55 8.43 8.99 8.72 9.93 8.11 8.81 8.57 9.38 8.47 9.68 8.89 8.43 8.35 9.15 8.44 9.60 7.50 9.65

Acknowledgements: Not applicable

Data availability: The datasets used and/or statistical analysis code for the current study are available from the authors (Marc Gunter, gunterm@students.iarc.fr) on reasonable request.

Acknowledgments

Disclaimer -Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization.

Funding: This work was supported by the International Agency for Research on Cancer, WHO, France and the National Cancer Institute, USA. Funding -This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors; this research was jointly funded by IARC and INRAE. Recognition of personal assistance -The authors of this paper would like to thank Christophe Lallemand from IARC and Laurence Brazeau from INRAE for their help with laboratory work.

Comparison of fecal sample collection methods for microbial analysis embedded within colorectal cancer screening programs

Semi Zouiouich (zouiouichs@students.iarc.fr) 1 , Mahendra Mariadassou (mahendra.mariadassou@inrae.fr) 2,3 , Olivier Rué (olivier.rue@inrae.fr) 2,3 , Emily Vogtmann (emily.vogtmann@nih.gov) 4 , Inge Huybrechts (huybrechtsi@iarc.fr) 1 , Gianluca Severi (gianluca.severi@gustaveroussy.fr) 4,5 , Marie-Christine Boutron-Ruault (mariechristine.boutron@gustaveroussy.fr) 4 , Carlo Senore (carlo.senore@cpo.it) 6 , Alessio Naccarati (alessio.naccarati@iigm.it) 7,8 , Giulio Mengozzi (giulio.mengozzi@unito.it) 9 , Zisis Kozlakidis (kozlakidisz@iarc.fr) 10 , Mazda Jenab (jenabm@iarc.fr) 1 , Rashmi Sinha (sinhar@exchange.nih.gov) 3 , Marc J. Gunter (gunterm@iarc.fr) 1* , Marion Leclerc (marion.leclerc@inrae.fr) 11* Affiliations: 1