Keywords:

Similarity-based prediction for channel mapping and user positioning

Similarity-based prediction for channel mapping and user positioning Luc Le Magoarou

Abstract-In a wireless network, gathering information at the base station about mobile users based only on uplink channel measurements is an interesting challenge. Indeed, accessing the users locations and predicting their downlink channels would be particularly useful in order to optimize the network efficiency. In this paper, a supervised machine learning approach addressing these tasks in an unified way is proposed. It relies on a labeled database that can be acquired in a simple way by the base station while operating. The proposed regression method can be seen as a computationally efficient two layers neural network initialized with a non-parametric estimator. It is illustrated on realistic channel data, both for the positioning and channel mapping tasks, achieving better results than previously proposed approaches, at a lower cost.

Index Terms-channel mapping, user positioning, neural networks, regression.

I. INTRODUCTION

W IRELESS networks are currently undergoing dramatic changes, driven by significant innovations in the physical layer. In particular, it has been recently proposed to use massive multiple input multiple output (massive MIMO) wireless systems [START_REF] Rusek | Scaling up mimo: Opportunities and challenges with very large arrays[END_REF], [START_REF] Larsson | Massive mimo for next generation wireless systems[END_REF], [START_REF] Lu | An overview of massive mimo: Benefits and challenges[END_REF] with a large number of antennas in the millimeter-wave band [START_REF] Rappaport | Millimeter wave mobile communications for 5g cellular: It will work![END_REF], [START_REF] Swindlehurst | Millimeter-wave massive mimo: the next wireless revolution?[END_REF], where a large bandwidth can be exploited.

On the other hand, machine learning techniques made possible by the growing available computing power have recently led to tremendous successes in various domains [START_REF] Lecun | Deep learning[END_REF], [START_REF] Goodfellow | Deep Learning[END_REF]. Machine learning holds promise for wireless communications (see [START_REF] Shea | An introduction to deep learning for the physical layer[END_REF], [START_REF] Wang | Deep learning for wireless physical layer: Opportunities and challenges[END_REF] for exhaustive surveys). In particular, it is possible to use uplink channel data that can be acquired easily in modern MIMO systems to predict quantities of interest. In this paper, the objective is to use the channel data to predict some target vector whose content depends on the considered task. The two tasks of interest are user positioning (in which the user's location is to be predicted) and channel mapping (in which the user's downlink channel is to be predicted). Contributions. This paper proposes a similarity-based approach to tackle these two problems. It is initialized with a non-parametric method but is fundamentally parametric. Indeed, it corresponds to a neural network whose structure at the initialization mimics nearest neighbors regression [START_REF] Stone | Consistent nonparametric regression[END_REF], [START_REF] Benedetti | On the nonparametric estimation of regression functions[END_REF], [START_REF] Tukey | Exploratory data analysis[END_REF]. Fine tuning by gradient descent within this structure allows to improve the prediction results. A theoretical justification of this simple method is proposed in the context of channel data. The introduced method is then empirically Luc Le Magoarou is with bcom, Rennes, France. Contact address: luc.lemagoarou@b-com.com. evaluated on realistic data and proves computationally efficient and accurate on the two considered tasks. Related work. User positioning and channel mapping using channel data have both been investigated. On the user positioning side, it has been proposed to tackle the problem with a convolutional neural network (CNN) operating directly on channel data [START_REF] Arnold | Novel massive mimo channel sounding data applied to deep learning-based indoor positioning[END_REF], or on channel data transformed to the angular domain [START_REF] Vieira | Deep convolutional neural networks for massive mimo fingerprint-based positioning[END_REF]. It has also been proposed to use a classical non-parametric approach not relying on a neural network [START_REF] Savic | Fingerprinting-based positioning in distributed massive mimo systems[END_REF], operating on the received signal strengths (RSS) rather than on the channels directly. The proposed method takes advantage of the simplicity and low complexity of non-parametric approaches to guide the structure of a neural network operating on channels, which distinguishes it from all these methods. Regarding channel mapping, which is a more recent problem, it has been tackled with help of a fully connected neural network [START_REF] Alrabeiah | Deep learning for tdd and fdd massive mimo: Mapping channels in space and frequency[END_REF]. The proposed method adopts a totally different network structure, as well as a different cost function.

II. PROBLEM FORMULATION

The methods proposed in this paper apply to a wide variety of multi-user MIMO wideband systems, operating indifferently in time division duplex (TDD) or frequency division duplex (FDD), where the antennas at the base station are indifferently colocated or not (in which case it is a distributed MIMO system). Let us consider N base station antennas and S subcarriers, and denote h ∈ C N S the uplink channel vector between any given user and the base station and h n,s ∈ C the channel for the nth antenna on the sth subcarrier. Note that no index is introduced to denote to which specific user corresponds the channel, since the proposed method treats indifferently the channels from all users.

Based on an incoming estimated uplink channel h, the objective in this paper is to predict a target vector t. Depending on the considered application, the target vector t may be the location of the user [START_REF] Arnold | Novel massive mimo channel sounding data applied to deep learning-based indoor positioning[END_REF], its downlink channel [START_REF] Alrabeiah | Deep learning for tdd and fdd massive mimo: Mapping channels in space and frequency[END_REF] or any other quantity of interest. Let us denote f the prediction function that maps the incoming channel to the target estimate, so that the estimated target writes

t = f (h). (1)
In order to calibrate the prediction function f , let us assume a dataset is available containing L labeled samples made of a channel and the associated target:

{h i ; t i } L i=1 . (2)
Under this very general framework, two tasks are considered in this paper. User positioning. The objective of this task is to predict the position of the user, based only on the knowledge of its uplink channel. In that case the target t contains the coordinates of the user's position (either in 2D or 3D depending on the context).

Regarding the labeled data acquisition, it has to be obtained by an auxiliary sensor such as the GPS [START_REF] Savic | Fingerprinting-based positioning in distributed massive mimo systems[END_REF], either online or during an offline data collection phase.

Channel mapping. This task aims at eliminating the need for a downlink channel estimation phase. Indeed, in that case the target t contains the user's downlink channel, which has to be predicted from the knowledge of the uplink channel. Labeled data can be acquired during a phase where the base station sends downlink pilots, as explained in details in [START_REF] Alrabeiah | Deep learning for tdd and fdd massive mimo: Mapping channels in space and frequency[END_REF]. Channel mapping is especially interesting for FDD systems that use different frequencies for the uplink and downlink transmissions.

In its original formulation [START_REF] Alrabeiah | Deep learning for tdd and fdd massive mimo: Mapping channels in space and frequency[END_REF], channel mapping takes as input the channel on only a subset of the base station antennas, so as to lighten the computational cost. This setting is also considered in the experimental part of this paper. System operation. For the two considered tasks, the system operates in two phases. During the training phase, data are collected (using GPS or downlink pilots) and the prediction function is learned. During the exploitation phase, the learned prediction is used. For the proposed method, these two phases are not mutually exclusive in time. Indeed, after an initial training has been carried out, they can be intertwined and online learning can be used so as to adapt to changes in the environment or users distribution.

III. SIMILARITY-BASED PREDICTION

Let us now introduce the proposed prediction method, by unveiling the structure of the prediction function f . The method is based on the very simple rationale that similar channels lead to similar targets. The main novelty of this paper is to translate that rationale into a neural network structure instead of using a generic network structure, as was done previously [START_REF] Alrabeiah | Deep learning for tdd and fdd massive mimo: Mapping channels in space and frequency[END_REF], [START_REF] Vieira | Deep convolutional neural networks for massive mimo fingerprint-based positioning[END_REF], [START_REF] Arnold | Novel massive mimo channel sounding data applied to deep learning-based indoor positioning[END_REF].

A. The Nadaraya-Watson estimator

The similarity-based prediction method proposed here is inspired by the Nadaraya-Watson estimator [START_REF] Nadaraya | On estimating regression[END_REF], [START_REF] Watson | Smooth regression analysis[END_REF], which is a classical non-parametric regression method. It is based on an approximation of the joint density of inputs and targets by kernels located at the training points [START_REF] Rosenblatt | Remarks on some nonparametric estimates of a density function[END_REF], [START_REF] Parzen | On estimation of a probability density function and mode[END_REF]. Such an approximation leads to the conditional expectation of the target given the input taking the form

t = E [t|h] L i=1 K(h, h i )t i L j=1 K(h, h j ) , (3)
where K(•, •) is a kernel function measuring the similarity between two inputs. The estimate of the target is then a convex combination of targets corresponding to the training points. In order to perform well, such a non-parametric method requires a fine enough sampling of the channel space, which may require a very large number of training points L. However, for the studied problem, it is argued in section III-C that the particular nature of channel data allows to overcome this issue.

B. Neural network structure

Let us now propose a neural network structure whose initialization corresponds to the Nadaraya-Watson estimator, so as to view this non-parametric method as a parametric one, whose parameters can be optimized. To do so, a kernel measuring the similarity between channels using the complex inner product is used. Indeed, denoting I k (h) the set of indices corresponding to the k training channels that are most correlated to the current channel h, the used kernel takes the form

K(h, h i ) |h H i h| if i ∈ I k (h), 0 otherwise. ( 4
)
It amounts to take into account only the k training channels most correlated to the current one. The hyperparameter k is chosen to be small (no more than a few dozens), yielding sparsity that reduces the computational cost of the method. With this kernel, the Nadaraya-Watson estimator can be viewed as an instance of k-nearest neighbors (k-NN) regression. Instead of considering this estimator as static, it is possible to view it as the forward pass in a neural network, given in algorithm 1, where HT k (•) refers to the hard thresholding operator that keeps unchanged the k entries of greatest modulus of its input and sets all the others to zero. The sequence of linear and nonlinear operation can also be visualized on figure 1. The matrices D and P are the weights of the neural network that are to be optimized. Initializing them with

D = (h 1 , . . . , h L ) , P = (t 1 , . . . , t L ) , (5)
amounts to initialize the neural network as the Nadaraya-Watson estimator. These weights can then be optimized by gradient descent using back-propagation [START_REF] Rumelhart | Learning Internal Representations by Error Propagation[END_REF] to better fit the labeled dataset, according to some cost function. The precise form of the used cost functions are given in the experimental part of the paper since they depend on the considered application. This fine tuning phase helps because the distribution of the training samples may not be perfect for the ultimate goal, and the used channels can be noisy. Fine tuning is meant to correct these two potential defects.

Computational complexity. The proposed method is particularly computationally efficient. Indeed the complexity of the forward pass is dominated by the first step (multiplication by D H ), which costs O(N SL) arithmetic operations. The backward pass is even more efficient since it costs only O(N Sk) arithmetic operations, thanks to the sparsity obtained via the hard thresholding operation. Note that this complexity is much lower than the one of concurrent approaches. Moreover, if the number of training samples L is very large, it is also possible to keep only a subset of the training samples or to perform clustering to build matrices D and P in order to further reduce the complexity of the method, as is classically done for radial basis function networks (RBFN) [START_REF] Schwenker | Three learning phases for radial-basis-function networks[END_REF]. This is one advantage of viewing the method as parametric, leading to a forward pass costing O(N S L) arithmetic operations, where L is a fixed number independent of the number of training samples. This possibility is not explored in the current paper, but an interesting avenue for future developments. The complexity of the proposed approach is compared precisely to the one of concurrent approaches in the experimental part of the paper.

C. Why should it work?

Given the simplicity of the proposed method, it is legitimate to wonder why it should work well with channel data, although in other domains such as image or audio processing, this kind of local interpolation methods are outperformed by more elaborate deep learning methods [START_REF] Lecun | Deep learning[END_REF].

In order to answer this question, let us take a step back. Actually, data processing techniques are often based on the manifold assumption: Meaningful data (signals) lie near a low dimensional manifold, although their apparent dimension is much larger [START_REF] Carlsson | Topology and data[END_REF], [START_REF] Peyré | Manifold models for signals and images[END_REF] [7, Section 5.11.3] [25, Section 9.3]. The performance of local interpolation methods is heavily dependent on the dimension of the data manifold (the lower the better). In order to perform well for a reasonable number of training points, the manifold dimension should be low. Indeed, the number of required training points for a given accuracy grows exponentially with the manifold dimension [START_REF] Bickel | Local polynomial regression on unknown manifolds[END_REF], [START_REF] Kpotufe | k-nn regression adapts to local intrinsic dimension[END_REF].

What is the dimension of the channel data manifold? As previous work suggests, it is reasonable to assume the existence of a position to channel mapping [START_REF] Alrabeiah | Deep learning for tdd and fdd massive mimo: Mapping channels in space and frequency[END_REF], i.e. a deterministic function linking the position of the user with the corresponding channel. The set of possible positions can be modeled as a twodimensional manifold (neglecting the elevation dimension). If it is further assumed that the position to channel mapping is an homeomorphism (continuous bijection whose inverse is also continuous), then the channel vectors lie on a twodimensional manifold (simple application of the definition of a manifold). This conclusion is very encouraging since it means that despite the high apparent dimension of channel vectors (due to the large number of antennas and subcarriers), the very low dimension of the channel manifold should allow simple local interpolation methods to perform well with a reasonable number of training points.

Note that such a simple reasoning cannot be applied to image or audio data, and the data manifold in these domains is very likely to be much higher dimensional (e.g. a few dozens for images [START_REF] Lu | Image manifolds[END_REF]). In summary, the channel data is inherently much less complex than image or audio data, so that local methods can work well for channel data although much more computationally complex techniques have to be used in these domains (such as deep learning).

IV. EXPERIMENTS

In this section, the proposed similarity-based approach is illustrated on the channel mapping and user positioning tasks. Implementation details. The method is implemented with help of the PyTorch library [START_REF] Paszke | Pytorch: An imperative style, high-performance deep learning library[END_REF], so that gradients are computed automatically. The optimization is done with minibatch gradient descent (size of the minibatches depending on the application) using the Adam optimization algorithm [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF]. Note that complex weights and inputs are handled classically by stacking the real and imaginary parts so that the neural network treats only real numbers. Moreover, for the fine tuning, the column of D corresponding to the current training sample is excluded in order to avoid trivial solutions during training (using for prediction a channel which is exactly the current training sample).

A. Channel mapping

The channel mapping application is investigated with help of the DeepMIMO dataset [START_REF] Alkhateeb | DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications[END_REF], which is itself based on the ray-tracing simulator Wireless InSite by Remcom [START_REF]Remcom, wireless insite[END_REF]. In order to ease comparisons with prior art, the considered setting is exactly the same as in [START_REF] Alrabeiah | Deep learning for tdd and fdd massive mimo: Mapping channels in space and frequency[END_REF], namely multipath channels (5 paths) obtained from an indoor environment consisting in a 10 m × 10 m room, with N = 64 antennas on the ceiling and S = 16 subcarriers, an uplink frequency of 2.4 GHz and a downlink frequency of 2.5 GHz (see [START_REF] Alrabeiah | Deep learning for tdd and fdd massive mimo: Mapping channels in space and frequency[END_REF] for a more detailed description). The labeled dataset consists of L uplink channels for which only 8 randomly picked antennas are considered paired with the associated downlink channel (on all 64 antennas) corresponding to users randomly located in the room, with L varying in our experiment. The cost used in order to fine tune the neural network of figure 1 (for 100 epochs) is the opposite of the downlink spectral efficiency averaged over subcarriers obtained when using the estimated downlink channel as precoder, taking the form

-E 1 K K k=1 log 2 1 + |h H D,k ĥD,k | 2 ĥD,k 2 2 , (6)
where h D,k is the downlink channel on the kth subcarrier, ĥD,k is its estimation given by the neural network, and the expectation being in practice estimated by averaging over minibatches of 1000 channels. The hard thresholding parameter is fixed to k = 5.

Results. Figure 2 summarizes the obtained results for L ∈ {1000, 3000, 6000, 12000, 30000, 60000}, showing the spectral efficiency (opposite of the cost ( 6)) averaged over 1000 randomly picked test channels. Results are shown for the initialization (blue dots) and after fine tuning by gradient descent for L ∈ {3000, 6000, 12000} (red crosses). An upper bound (obtained using the true channel as precoder) is also shown (black dashed line). It is interesting to notice that only a reasonable amount of training data is required to get very close to the upper bound with the proposed method. Indeed, the obtained results are within 6% of the upper bound without fine tuning and within 4% with fine tuning, with only 12000 training points. This performance is superior to the previously proposed method [16, Figure 7], which obtained results within 9% of the upper bound with 120000 training samples (ten times more). Moreover, the proposed method yields a much lighter network (forward pass costing around 1e6 arithmetic operations) compared to the fully connected neural network of [START_REF] Alrabeiah | Deep learning for tdd and fdd massive mimo: Mapping channels in space and frequency[END_REF] (around 3e7 arithmetic operations). In summary, the proposed method yields better results with fewer training samples, at a lower cost. This experiment shows the great potential of the similarity-based approach for the channel mapping problem. However, the number of required training samples may still seem large in regard of the quite small area considered in this experiment. This issue should be taken into account in future work. Note that this issue is less significant in the next experiment regarding the user positioning task.

B. User positioning

The user positioning task is investigated with help of a dataset originally intended for the IEEE Communication Theory Workshop (CTW) 2020 data competition [START_REF]Ctw 2020 data competition[END_REF], built with a channel sounder described in [START_REF] Arnold | Novel massive mimo channel sounding data applied to deep learning-based indoor positioning[END_REF]. The considered base station comprises N = 56 antennas and communicates over K = 924 subcarriers at the central frequency of 1.27 GHz. The labeled dataset consists of L = 4979 channels h with the corresponding location p that are measured in an area of approximately 1 km 2 in the streets of Stuttgart, Germany. It is divided into 4096 channels used for training and 883 channels used as validation data. Dimensionality reduction. Channels described in the previous paragraph are of very high dimension. Indeed, they can be seen as 56 × 924 complex matrices. Using directly the method of algorithm 1 on these channels would result in a very high computational complexity. In order to reduce the computational burden, the dimensionality of input channels is reduced by computing the left singular vector corresponding to the largest singular value and using it as input for algorithm 1. This results in inputs of size N = 56 complex numbers (112 real numbers) only. The cost used to fine tune the network of figure 1 (for 50 epochs) corresponds to the average localization error, taking the form

E [ p -p 2 ] (7)
where p is the true position and p its estimation given by the neural network, and the expectation being in practice estimated by averaging over minibatches of 100 channels. The hard thresholding parameter k is varied between 2 and 16.

Baselines. The proposed method is compared to two baselines taking the same inputs. The first one is an instance of extreme learning machine (ELM) as was proposed for user positioning [START_REF] Decurninge | Csi-based outdoor localization for massive mimo: Experiments with a learning approach[END_REF], with a random layer of 50000 neurons and a rectified linear activation (ReLu) [START_REF] Goodfellow | Deep Learning[END_REF]Chapter 6]. The second one is a simple multilayer perceptron (MLP) [START_REF] Goodfellow | Deep Learning[END_REF]Chapter 6] comprising two layers made of 112 neurons with a ReLu non-linearity followed by a last linear one with three neurons (outputting location estimates). It is trained for 200 epochs with the Adam optimization algorithm [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF].

Results. Figure 3 summarizes the obtained results for a varying number of neighbors (corresponding to the parameter k of the hard thresholding operator) and for the two baselines. First of all, note that the proposed method leads to better positioning results than the baselines (the MLP leading to approximately 70 m and the ELM to approximately 80 m average errors). Then, it is interesting to notice that the number of neighbors has only a modest influence on the localization error, provided it is chosen not too small (larger than 4). Second, fine tuning shows beneficial for the localization task (except for k = 1, where the fine tuning decreases a lot the positioning accuracy). Indeed, the initialization gives errors around 52 m while fine tuning allows to attain errors around 42 m (approximately 20% better). Keeping in mind that the area on which localization is sought is of 1 km 2 , such results are pretty accurate and encouraging. Moreover, note that most of the time the localization error is much smaller than the average (the median is around 20 m for k = 10). Note that these results are the first to be reported for this outdoor localization task. Concurrent parametric methods such as convolutional neural networks were only applied to indoor localization for now [START_REF] Arnold | Novel massive mimo channel sounding data applied to deep learning-based indoor positioning[END_REF].

V. CONCLUSION

In this paper, a generic similarity-based neural network was introduced in order to operate on channel data, for the channel mapping and user positioning tasks. It was theoretically motivated relying on the manifold hypothesis. Moreover, the proposed method was empirically validated on realistic data for the two aforementioned tasks. In the future, it would be very interesting to investigate further the sample complexity of the method, in order to optimize the training of such systems. Moreover, one can also envision using several heuristics aimed at improving the training of such similarity-based neural networks [START_REF] Schwenker | Three learning phases for radial-basis-function networks[END_REF].

Algorithm 1 1 Fig. 1 :

 111 Fig. 1: Feedforward representation of the proposed method.

Fig. 2 :

 2 Fig. 2: Channel mapping results.

Fig. 3 :

 3 Fig. 3: User positioning results.


Helena Bonciani Nadeer

Fabrice Allain
email: fabrice.allain@univ-lille.fr

Agnès Denys

The Emerging Roles of Heparan Sulfate 3-O-Sulfotransferases in Cancer

Keywords: heparan sulfate, sulfotransferase, cancer, epigenetic regulation, immune escape

Alteration in the expression of heparan sulfate (HS)-modifying enzymes has been frequently observed in cancer. Consequently, dysregulation of the HS biosynthetic machinery results in dramatic changes in the HS structure, thereby impacting a range of pivotal cellular processes involved in tumorigenesis and cancer progression including proliferation, migration, apoptosis, and immune escape. HS 3-O-sulfotransferases (HS3STs) catalyse the maturation step of glucosaminyl 3-O-sulfation within HS chains. Although seven HS3ST isozymes have been described in human, 3-O-sulfation is a rare modification and only a few biological processes have been described to be influenced by 3-O-sulfated HS. An aberrant expression of HS3STs has been reported in a variety of cancers. Thus, it was suggested that changes in the expression of these enzymes as a result of tumorigenesis or tumor growth may critically influence cancer cell behavior. In accordance with this assumption, a number of studies have documented the epigenetic repression of HS3ST2 and HS3ST3A in many cancers. However, the situation is not so clear, and there is accumulating evidence that HS3ST2, HS3ST3A, HS3ST3B, and HS3ST4 may also act as tumor-promoting enzymes in a number of cancer cells depending on their phenotypes and molecular signatures. In this mini-review, we focus on the recent insights regarding the abnormal expression of HS3STs in cancer and discuss the functional consequences on tumor cell behavior. In term of clinical outcome, further investigations are needed to explore the potential value of HS3STs and/or their 3-O-sulfated products as targets for therapeutic strategies in cancer treatment.

INTRODUCTION

Heparan sulfate (HS) is an anionic and linear polysaccharide, which is covalently attached to core proteins to form HS proteoglycans (HSPG). These molecules are present within the extracellular matrix (ECM) and at the surface of virtually all cells. While the core protein primarily determines the localization of HSPG, HS chains are involved in the binding of a large number of proteins, including growth factors, cytokines, proteases, lipoproteins, and ECM components. HS-protein interactions have multiple effects ranging from simple immobilization to protection against degradation, conformational change, stabilization of receptor-ligand complexes, or protein oligomerization [START_REF] Afratis | Syndecans -key regulators of cell signaling and biological functions[END_REF][START_REF] Esko | Order out of chaos: assembly of ligand binding sites in heparan sulphate[END_REF][START_REF] Sarrazin | Heparan sulfate proteoglycans[END_REF][START_REF] Turnbull | Heparan sulfate: decoding a dynamic multifunctional cell regulator[END_REF][START_REF] Zhang | Glycosaminoglycan (GAG) biosynthesis and GAG-binding proteins[END_REF]. Via these interactions, HS does not only regulate physiological processes, such as in embryogenesis, angiogenesis, blood coagulation and inflammation, but are also implicated in many pathologies, including cancer, infectious diseases, and neurodegenerative disorders [START_REF] Lanzi | Heparan sulfate mimetics in cancer therapy: the challenge to define structural determinants and the relevance of targets for optimal activity[END_REF][START_REF] Maïza | The role of heparan sulfates in protein aggregation and their potential impact on neurodegeneration[END_REF][START_REF] Mohamed | Heparin mimetics: their therapeutic potential[END_REF][START_REF] Nagarajan | Heparan sulfate and heparan sulfate proteoglycans in cancer initiation and progression[END_REF][START_REF] Sasisekharan | Roles of heparan sulphate glycosaminoglycans in cancer[END_REF].

Structural determinants in HS are derived from enzymatic modifications of the glycan backbone, which is formed by polymerization of the repeat unit consisting of D-glucuronic acid (GlcUA) and N-acetylated D-glucosamine (GlcNAc). In the classical model of biosynthesis, the native polysaccharide is first subject to partial N-deacetylation/N-sulfation of GlcNAc residues. This modification provides the substrate for next modifications, including epimerization of some GlcUA into Liduronic acid (IdoUA), 2-O-sulfation of uronic acids (mainly IdoUA), and 6-O and/or 3-O-sulfations of GlcN residues (Figure 1). HS-protein interactions are primarily driven by complementarity between positively charged amino acid residues in the ligand and sulfate groups in the HS sequence. However, protein binding to HS does not only rely on the overall degree of sulfation. Instead, a concept has emerged whereby optimal binding depends on the spatial arrangement of sulfate groups in given HS sequences [START_REF] Esko | Order out of chaos: assembly of ligand binding sites in heparan sulphate[END_REF][START_REF] Turnbull | Heparan sulfate: decoding a dynamic multifunctional cell regulator[END_REF][START_REF] Zhang | Glycosaminoglycan (GAG) biosynthesis and GAG-binding proteins[END_REF][START_REF] Carlsson | Heparin/heparan sulfate biosynthesis[END_REF][START_REF] Thacker | Heparan sulfate 3-O-sulfation: a rare modification in search of a function[END_REF].

HS3STs represent the largest family of HS-modifying enzymes, and yet the reaction of 3-O-sulfation is the rarest maturation step, when compared to other sulfations. Seven HS3STs have been characterized in human, for which the expression is dependent on cell type and tissue environment (Table 1). HS3ST-mediated 3-O-sulfation leads to at least two distinct forms of 3-O-sulfated motifs. HS3ST1 and HS3ST5 participate in the generation of anticoagulant-active HS/heparin sequences for antithrombin-III, while HS3ST2, HS3ST3A, HS3ST3B, HS3ST4, and HS3ST6 were described to provide the HS-binding motifs for the glycoprotein gD of herpes simplex virus-1 (HSV-1) [START_REF] Mochizuki | Tetrasulfated disaccharide unit in heparan sulfate: enzymatic formation and tissue distribution[END_REF][START_REF] Shworak | Multiple isoforms of heparan sulfate D-glucosaminyl 3-Osulfotransferase -isolation, characterization, and expression of human cDNAs and identification of distinct genomic loci[END_REF][START_REF] Mochizuki | Characterization of a heparan sulfate 3-O-sulfotransferase-5, an enzyme synthesizing a tetrasulfated disaccharide[END_REF][START_REF] Xia | Heparan sulfate 3-O-sulfotransferase isoform 5 generates both an antithrombin-binding site and an entry receptor for herpes simplex virus type 1[END_REF][START_REF] Xu | Characterization of heparan sulphate 3-O-sulphotransferase isoform 6 and its role in assisting the entry of herpes simplex virus type 1[END_REF][START_REF] Liu | Purification of heparan sulfate D-glucosaminyl 3-O-sulfotransferase[END_REF][START_REF] O'donnell | A role for heparan sulfate 3-O sulfotransferase isoform 2 in herpes simplex virus type 1 entry and spread[END_REF][START_REF] Shukla | A novel role for 3-O-sulfated heparan sulfate in herpes simplex virus 1 entry[END_REF][START_REF] Tiwari | A role for 3-O sulfotransferase isoform-4 in assisting HSV-1 entry and spread[END_REF][START_REF] Wang | Synthesis of 3-Osulfated oligosaccharides to understand the relationship between structures and functions of heparan sulfate[END_REF][START_REF] Zhang | The effect of precursor structures on the action of glucosaminyl 3-O-sulfotransferase-1 and the biosynthesis of anticoagulant heparan sulfate[END_REF]. To date, only a few ligands are known to selectively interact with 3-O-sulfated motifs, whereas hundreds of HS-binding proteins have been identified. Consequently, little is known concerning the functions of 3-Osulfated HS in biological processes, apart from their roles in anticoagulant properties of HS/heparin and entry of HSV-1 into host cells [START_REF] Thacker | Heparan sulfate 3-O-sulfation: a rare modification in search of a function[END_REF][START_REF] Kaltenbach | Sulfotransferase and heparanase: remodeling engines in promoting virus infection and disease development[END_REF][START_REF] Thacker | Expanding the 3-O-sulfate proteome -Enhanced binding of neuropilin-1 to 3-O-sulfated heparan sulfate modulates its activity[END_REF].

Expression of the genes encoding HS-modifying enzymes is frequently dysregulated in cancer and other diseases [START_REF] Kaltenbach | Sulfotransferase and heparanase: remodeling engines in promoting virus infection and disease development[END_REF][START_REF] Ropero | Epigenetic loss of the familial tumor-suppressor gene exostosin-1 (EXT1) disrupts heparan sulfate synthesis in cancer cells[END_REF][START_REF] Sanderson | Enzymatic remodeling of heparan sulfate proteoglycans within the tumor microenvironment: growth regulation and the prospect of new cancer therapies[END_REF]. An aberrant expression of HS3STs has been reported in various cancers, suggesting that these enzymes and their 3-Osulfated products may be involved in tumorigenesis and cancer progression. However, these reports reveal either anti-oncogenic or tumor-promoting effects (Table 1), and the mechanisms and consequences of HS3ST dysregulation in cancer still remain obscure (15-23, 26-28, 32).

ANTI-ONCOGENIC PROPERTIES OF HS3STs

In cancer cells, hypermethylation of CpG islands in gene promoters has been associated with the loss of expression of some susceptible genes, including tumor suppressor genes, and genes encoding products involved in DNA repair and apoptosis [START_REF] Ropero | Epigenetic loss of the familial tumor-suppressor gene exostosin-1 (EXT1) disrupts heparan sulfate synthesis in cancer cells[END_REF][START_REF] Gronbaek | Epigenetic changes in cancer[END_REF][START_REF] Vucic | Epigenetics of cancer progression[END_REF]. In the attempt to identify novel silenced genes in breast cancer, Miyamoto et al. [START_REF] Miyamoto | Methylation-associated silencing of heparan sulfate D-glucosaminyl 3-Osulfotransferase-2 (3-OST-2) in human breast, colon, lung and pancreatic cancers[END_REF] found that the 5' region of the HS3ST2 gene was hypermethylated in tumor tissue but not in surrounding non-cancerous tissue. As a consequence, the expression level of HS3ST2 was markedly reduced in the cancer sample compared with the matched normal counterpart. Then, they demonstrated that HS3ST2 was not expressed in cell lines representative of the different molecular breast cancer subgroups [START_REF] Subik | The expression patterns of ER, PR, HER2, CK5/6, EGFR, Ki-67 and AR by immunohistochemical analysis in breast cancer cell lines[END_REF]. Reversing methylation restored the expression of the enzyme, confirming the silencing effect of gene methylation. Moreover, HS3ST2 gene hypermethylation was detected in the majority of primary breast cancer samples analysed, and also in human colon, lung and pancreatic cancers [START_REF] Miyamoto | Methylation-associated silencing of heparan sulfate D-glucosaminyl 3-Osulfotransferase-2 (3-OST-2) in human breast, colon, lung and pancreatic cancers[END_REF]. Following this work, many clinical studies have been published examining the relationships between aberrant methylation of the HS3ST2 gene and tumorigenesis. Hypermethylation was found at high frequency in gastric, breast, colorectal, prostate and cervix cancers, as well as in hematological neoplasms [START_REF] Bernal | Reprimo as a potential biomarker for early detection in gastric cancer[END_REF][START_REF] Dietrich | Analysis of DNA methylation of multiple genes in microdissected cells from formalin-fixed and paraffin-embedded tissue[END_REF][START_REF] Gonzalo | Aberrant gene promoter methylation associated with sporadic multiple colorectal cancer[END_REF][START_REF] Hwang | Epigenetic inactivation of heparan sulfate (glucosamine) 3-O-sulfotransferase 2 in lung cancer and its role in tumorigenesis[END_REF][START_REF] Lim | Cervical dysplasia: assessing methylation status (Methylight) of CCNA1, DAPK1, HS3ST2, PAX1 and TFPI2 to improve diagnostic accuracy[END_REF][START_REF] Mahapatra | Global methylation profiling for risk prediction of prostate cancer[END_REF][START_REF] Martin-Subero | A comprehensive microarray-based DNA methylation study of 367 hematological neoplasms[END_REF][START_REF] Miyamoto | Methylation-associated silencing of heparan sulfate D-glucosaminyl 3-Osulfotransferase-2 (3-OST-2) in human breast, colon, lung and pancreatic cancers[END_REF][START_REF] Shivapurkar | Evaluation of candidate methylation markers to detect cervical neoplasia[END_REF]. In breast and cervix, hypermethylation of the HS3ST2 gene occurs early during malignant transformation, suggesting a correlation between HS3ST2 silencing and progression of the disease [START_REF] Dietrich | Analysis of DNA methylation of multiple genes in microdissected cells from formalin-fixed and paraffin-embedded tissue[END_REF][START_REF] Shivapurkar | Evaluation of candidate methylation markers to detect cervical neoplasia[END_REF]. Hwang et al. [START_REF] Hwang | Epigenetic inactivation of heparan sulfate (glucosamine) 3-O-sulfotransferase 2 in lung cancer and its role in tumorigenesis[END_REF] demonstrated that the exogenous reexpression of HS3ST2 was efficient to inhibit cell migration, invasion and proliferation in various lung cancer cell lines. However, they found that the tumor size was not significantly different between patients with HS3ST2 gene hypermethylation and those without, in spite of the anti-proliferative property of HS3ST2 observed in vitro. Hence, they emphasized the need of further investigations to validate HS3ST2 silencing as a prognostic/predictive biomarker [START_REF] Hwang | Epigenetic inactivation of heparan sulfate (glucosamine) 3-O-sulfotransferase 2 in lung cancer and its role in tumorigenesis[END_REF].

Besides HS3ST2, an analysis of the methylation status of other genes encoding HS sulfotransferases in chondrosarcoma showed hypermethylation in proximal regions of the HS3ST1 and HS3ST3A1 genes. Exposure to a demethylating agent restored their expression, confirming that aberrant methylation had affected their transcription. Moreover, re-expression of HS3ST3A reduced the proliferative and migratory properties of chondrosarcoma cells, suggesting that silencing of this enzyme may have contributed to tumor cell growth and invasiveness [START_REF] Bui | Epigenetics: methylation-associated repression of heparan sulfate 3-Osulfotransferase gene expression contributes to the invasive phenotype of H-EMCSS chondrosarcoma cells[END_REF]. In the following study, Mao et al. [START_REF] Mao | The heparan sulfate sulfotransferase 3-OST3A (HS3ST3A) is a novel tumor regulator and a prognostic marker in breast cancer[END_REF] demonstrated that the HS3ST3A1 gene is epigenetically repressed in breast cancer cell lines representative of the different molecular subgroups, except in the human epidermal growth factor receptor 2-positive (HER2+) cell lines. Re-expression of the enzyme in luminal Atype MCF-7 and triple negative MDA-MB-231 cell lines reduced cell proliferation in vitro and tumor growth in xenografted mice. Thus, the authors hypothesized that modification in HS structure may have hindered the interactions of growth factors with signalling receptors [START_REF] Mao | The heparan sulfate sulfotransferase 3-OST3A (HS3ST3A) is a novel tumor regulator and a prognostic marker in breast cancer[END_REF].

TUMOR-PROMOTING ACTIVITIES OF HS3STs

Albeit that epigenetic repression of the HS3ST2 gene was related to progression of many cancers, Vijaya Kumar et al. [START_REF] Vijaya-Kumar | HS3ST2 modulates breast cancer cell invasiveness via MAP kinase-and Tcf4 (Tcf7l2)-dependent regulation of protease and cadherin expression[END_REF] reported that its re-expression in MDA-MB-231 cells led to an increase in cell viability and invasion. Likewise, we reported that MDA-MB-231 cells carrying HS3ST2 expression displayed a significant increase in proliferation and survival [START_REF] Hellec | The heparan sulfate 3-O-sulfotransferases (HS3ST) 2, 3B and 4 enhance proliferation and survival in breast cancer MDA-MB-231 cells[END_REF]. The pro-invasive phenotype was however not observed in the MCF-7 cell line [START_REF] Vijaya-Kumar | HS3ST2 modulates breast cancer cell invasiveness via MAP kinase-and Tcf4 (Tcf7l2)-dependent regulation of protease and cadherin expression[END_REF], suggesting that the consequence of HS3ST expression could be different depending on the breast cancer phenotype. In line with this assumption, Mao et al. [START_REF] Mao | The heparan sulfate sulfotransferase 3-OST3A (HS3ST3A) is a novel tumor regulator and a prognostic marker in breast cancer[END_REF] described that HS3ST3A expression enhanced proliferation and survival of HER2+ SKBR3 cells, but not in MCF-7 cells. However, these authors reported that HS3ST3A was also anti-proliferative in MDA-MB-231 cells, meaning that this isozyme produced opposite effect than the one promoted by HS3ST2 in the same cell line [START_REF] Vijaya-Kumar | HS3ST2 modulates breast cancer cell invasiveness via MAP kinase-and Tcf4 (Tcf7l2)-dependent regulation of protease and cadherin expression[END_REF][START_REF] Hellec | The heparan sulfate 3-O-sulfotransferases (HS3ST) 2, 3B and 4 enhance proliferation and survival in breast cancer MDA-MB-231 cells[END_REF]. Although intriguing, we also found that overexpression of HS3ST3A did not have any effects on the proliferation of MDA-MB-231 cells. In contrast, forced expression of HS3ST3B and HS3ST4 had the same functional impact as observed in the case of HS3ST2 [START_REF] Hellec | The heparan sulfate 3-O-sulfotransferases (HS3ST) 2, 3B and 4 enhance proliferation and survival in breast cancer MDA-MB-231 cells[END_REF]. These results suggest that the impact of HS3ST expression in breast cancer cells could be also dependent on the type of isozyme. One explanation may be that each HS3ST exhibits subtle differences in their substrate requirement. On this assumption, HS3ST3A may have a restricted substrate specificity, making it incapable of synthesizing 3-O-sulfated HS with a tumor-promoting property in MDA-MB-231 cells. Conversely, HS3ST2, HS3ST3B, and HS3ST4 may exhibit a broader selectivity or share, at least in part, some common acceptors.

In pancreatic cancer cells, high level expression of HS3ST3B was reported to induce epithelial-mesenchymal transition (EMT) and to enhance cell invasiveness in vitro. Moreover, HS3ST3B overexpression was associated with an increased angiogenesis in graft-bearing mice, supporting the idea that HS3ST3B could favor pancreatic cancer progression [START_REF] Song | Heparan sulfate D-glucosaminyl 3-O sulfotransferase-3B1, a novel epithelial-mesenchymal transition inducer in pancreatic cancer[END_REF]. In the continuity of this study, Zhang et al. [START_REF] Zhang | Heparan sulfate Dglucosaminyl 3-O-sulfotransferase-3B1 (HS3ST3B1) promotes angiogenesis and proliferation by induction of VEGF in acute myeloid leukemia cells[END_REF] reported that high expression of HS3ST3B in U937 leukemia cells enhanced cell proliferation and survival, while its silencing had opposite effects. The advantage given by HS3ST3B was related to an increase in the production of vascular endothelial growth factor (VEGF) and activation of downstream signalling pathways. Next, the authors demonstrated that conditioned medium of HS3ST3B-expressing U937 cells had a promoting effect on angiogenesis, which was dependent on the secretion of VEGF. Finally, they confirmed that HS3ST3B effectively promoted leukemia cell proliferation and VEGF-dependent angiogenesis in xenografted mice [START_REF] Zhang | Heparan sulfate Dglucosaminyl 3-O-sulfotransferase-3B1 (HS3ST3B1) promotes angiogenesis and proliferation by induction of VEGF in acute myeloid leukemia cells[END_REF]. Most recently, a clinical study conducted in a cohort of lung cancer patients uncovered that HS3ST3B expression was upregulated in tumor biopsies compared to that in matched normal tissues [START_REF] Zhang | Heparan sulfate D-glucosamine 3-Osulfotransferase 3B1 is a novel regulator of transforming growth factorbeta-mediated epithelial-to-mesenchymal transition and regulated by miR-218 in non-small cell lung cancer[END_REF]. A high level expression of the enzyme was also observed in NSCLC cell lines. Silencing its expression reversed the mesenchymal phenotype, meaning that HS3ST3B is involved in the regulation of EMT in lung cancer cells in the same way as in pancreatic cells [START_REF] Zhang | Heparan sulfate D-glucosamine 3-Osulfotransferase 3B1 is a novel regulator of transforming growth factorbeta-mediated epithelial-to-mesenchymal transition and regulated by miR-218 in non-small cell lung cancer[END_REF][START_REF] Song | Heparan sulfate D-glucosaminyl 3-O sulfotransferase-3B1, a novel epithelial-mesenchymal transition inducer in pancreatic cancer[END_REF]. High expression level of TRF2 (telomere repeat binding factor 2), a protein normally involved in telomere protection, has been observed in various human cancers. Interestingly, the HS3ST4 gene was identified as a transcriptional target of TRF2, and increasing TRF2 level led to an up-regulation of HS3ST4 gene expression. Moreover, exogenous expression of either TRF2 or HS3ST4 in various tumor cell lines similarly resulted in increased tumor growth in xenografted mice, which suggests that the expression of this enzyme may be part of a pro-oncogenic pathway [START_REF] Biroccio | TRF2 inhibits a cell-extrinsic pathway through which natural killer cells eliminate cancer cells[END_REF].

HS3STs AND MODULATION OF SIGNALLING PATHWAYS

Consistent with a pro-invasive phenotype, Erk1/2 and β-catenin signalling was upregulated in HS3ST2-expressing cells in an HSdependent manner [START_REF] Vijaya-Kumar | HS3ST2 modulates breast cancer cell invasiveness via MAP kinase-and Tcf4 (Tcf7l2)-dependent regulation of protease and cadherin expression[END_REF]. As a consequence, the expression of several target genes involved in cancer cell invasiveness and survival was increased. High level expression of HS3ST3B in U937 leukemia cells was associated with activation of Notch-1, Erk1/2 and Akt signalling [START_REF] Zhang | Heparan sulfate Dglucosaminyl 3-O-sulfotransferase-3B1 (HS3ST3B1) promotes angiogenesis and proliferation by induction of VEGF in acute myeloid leukemia cells[END_REF], and more recently, the tumorpromoting effects of HS3ST2, HS3ST3B, and HS3ST4 were related to sustained activation of Src, Akt, and NF-κB, and upregulation of the anti-apoptotic proteins survivin and XIAP [START_REF] Hellec | The heparan sulfate 3-O-sulfotransferases (HS3ST) 2, 3B and 4 enhance proliferation and survival in breast cancer MDA-MB-231 cells[END_REF]. Importantly, all these signalling molecules have been well described to play a critical role in promoting tumor growth and resistance to apoptosis [START_REF] Altieri | Survivin and IAP proteins in cell-death mechanisms[END_REF][START_REF] Sever | Signal transduction in cancer[END_REF].

Most of the studies conducted with cancer cell lines reported that HS3ST overexpression resulted in an increase in the level of 3-O-sulfated motifs [START_REF] Vijaya-Kumar | HS3ST2 modulates breast cancer cell invasiveness via MAP kinase-and Tcf4 (Tcf7l2)-dependent regulation of protease and cadherin expression[END_REF][START_REF] Hellec | The heparan sulfate 3-O-sulfotransferases (HS3ST) 2, 3B and 4 enhance proliferation and survival in breast cancer MDA-MB-231 cells[END_REF][START_REF] Mao | The heparan sulfate sulfotransferase 3-OST3A (HS3ST3A) is a novel tumor regulator and a prognostic marker in breast cancer[END_REF]. Consequently, 3-Osulfation may have influenced ligand binding to cell surface HS, leading to an alteration of diverse signalling processes. Whether 3-O-sulfation can modulate ligand-receptor interactions was however unknown, until neuropilin-1 (Nrp1) was described as a preferential ligand for 3-O-sulfated HS [START_REF] Thacker | Expanding the 3-O-sulfate proteome -Enhanced binding of neuropilin-1 to 3-O-sulfated heparan sulfate modulates its activity[END_REF]. Initially described as a co-receptor for VEGFs and class 3 semaphorins in endothelial cells and neurons, there is now evidence that Nrp1 is also expressed in a number of cancer cells, wherein it regulates cell growth, migration, invasion, and immune escape, by interacting with a broad spectrum of growth factors [START_REF] Prud'homme | Neuropilins are multifunctional coreceptors involved in tumor initiation, growth, metastasis and immunity[END_REF][START_REF] Raimondi | Neuropilin signalling in vessels, neurons and tumours[END_REF][START_REF] West | Interactions of multiple heparin binding growth factors with neuropilin-1 and potentiation of the activity of fibroblast growth factor-2[END_REF]. Importantly, HS was reported to contribute to formation of a high-affinity complex incorporating Nrp1, VEGF, and cognate signalling receptors [START_REF] Sarabipour | VEGF-A121a binding to neuropilins -A concept revisited[END_REF][START_REF] Teran | Synergistic binding of vascular endothelial growth factor-A and its receptors to heparin selectively modulates complex affinity[END_REF]. Zhang et al. [START_REF] Zhang | Heparan sulfate Dglucosaminyl 3-O-sulfotransferase-3B1 (HS3ST3B1) promotes angiogenesis and proliferation by induction of VEGF in acute myeloid leukemia cells[END_REF] described that the tumorpromoting effect of HS3ST3B in leukemia cells was dependent on an autocrine activation of VEGF-dependent signalling pathways.

Thus, it may be suggested that 3-O-sulfation of HS has improved interplay between Nrp1, VEGF, and its receptors. Besides VEGF, transforming growth factor (TGF)-β has been also identified as a ligand of Nrp1. Interestingly, HS3ST3B was described as a regulator of TGF-β-mediated EMT in NSCLC cells [START_REF] Zhang | Heparan sulfate D-glucosamine 3-Osulfotransferase 3B1 is a novel regulator of transforming growth factorbeta-mediated epithelial-to-mesenchymal transition and regulated by miR-218 in non-small cell lung cancer[END_REF]. Though not mentioned in the study, these findings suggest a possible participation of Nrp1 and 3-O-sulfated HS in the response induced by TGF-β. Along the same lines, we demonstrated that silencing of Nrp1 in MDA-MB-231 cells reversed the advantage given by HS3ST3B [START_REF] Hellec | The pro-tumoral activity of heparan sulfate 3-O-sulfotransferase 3B (HS3ST3B) in breast cancer MDA-MB-231 cells is dependent on the expression of neuropilin-1[END_REF]. Hence, these findings raise the possibility that the tumor-promoting properties of HS3ST3B could be dependent on the formation of signalling complexes containing Nrp1.

Besides the roles attributed to HS moieties, HSPG core proteins have binding properties that engage them in specific interactions with proteins involved in signalling and cytoskeleton organization [START_REF] Afratis | Syndecans -key regulators of cell signaling and biological functions[END_REF][START_REF] Sarrazin | Heparan sulfate proteoglycans[END_REF]. A number of studies have reported that the expression of HSPG is dysregulated in many cancers, thus altering key biological processes involved in cell proliferation and survival [START_REF] Nagarajan | Heparan sulfate and heparan sulfate proteoglycans in cancer initiation and progression[END_REF]. However, there is no evidence that changes in the expression of a core protein can alter the sulfation patterns within HS chains [START_REF] Zako | Syndecan-1 and-4 synthesized simultaneously by mouse mammary gland epithelial cells bear heparan sulfate chains that are apparently structurally indistinguishable[END_REF]. Recently, Corti et al. [START_REF] Corti | N-terminal syndecan-2 domain selectively enhances 6-O heparan sulfate chains sulfation and promotes VEGFA165-dependent neovascularization[END_REF] reported that HS chains of syndecan-2 contained higher levels of 6-O and 3-O sulfations, which was related to an increase in formation of a signalling complex between syndecan-2, VEGF and its receptor in endothelial cells. These last findings demonstrate the existence of a regulatory mechanism wherein a core protein determines the sulfation pattern of its own HS chains. High level expression of syndecan-2 has been observed in many cancers [START_REF] Nagarajan | Heparan sulfate and heparan sulfate proteoglycans in cancer initiation and progression[END_REF]. This suggests that such a regulatory mechanism of HS sulfation may also occur in cancer cells. This could lead to the appearance of HSPG with specific HS chains, thereby enhancing the binding and functions of certain HS ligands. This assumption deserves additional works to identify the HSPG and their relevant ligands that interact with 3-O-sulfated HS in cancer cells.

HS3STs AND ESCAPE TO IMMUNE SURVEILLANCE

A body of evidence has accumulated over the past two decades indicating that HS3ST2 is epigenetically silenced in a wide range of cancers and tumor cell lines [START_REF] Bernal | Reprimo as a potential biomarker for early detection in gastric cancer[END_REF][START_REF] Dietrich | Analysis of DNA methylation of multiple genes in microdissected cells from formalin-fixed and paraffin-embedded tissue[END_REF][START_REF] Gonzalo | Aberrant gene promoter methylation associated with sporadic multiple colorectal cancer[END_REF][START_REF] Hwang | Epigenetic inactivation of heparan sulfate (glucosamine) 3-O-sulfotransferase 2 in lung cancer and its role in tumorigenesis[END_REF][START_REF] Lim | Cervical dysplasia: assessing methylation status (Methylight) of CCNA1, DAPK1, HS3ST2, PAX1 and TFPI2 to improve diagnostic accuracy[END_REF][START_REF] Mahapatra | Global methylation profiling for risk prediction of prostate cancer[END_REF][START_REF] Martin-Subero | A comprehensive microarray-based DNA methylation study of 367 hematological neoplasms[END_REF][START_REF] Miyamoto | Methylation-associated silencing of heparan sulfate D-glucosaminyl 3-Osulfotransferase-2 (3-OST-2) in human breast, colon, lung and pancreatic cancers[END_REF][START_REF] Shivapurkar | Evaluation of candidate methylation markers to detect cervical neoplasia[END_REF]. However, the authors did not address the possibility that another HS3ST could be expressed in place of HS3ST2. This assumption is supported by clinical studies showing that HS3ST3A and HS3ST3B were highly expressed in biopsies from patients with HER2+ breast cancer [START_REF] Mao | The heparan sulfate sulfotransferase 3-OST3A (HS3ST3A) is a novel tumor regulator and a prognostic marker in breast cancer[END_REF] and lung cancer biopsies [START_REF] Zhang | Heparan sulfate D-glucosamine 3-Osulfotransferase 3B1 is a novel regulator of transforming growth factorbeta-mediated epithelial-to-mesenchymal transition and regulated by miR-218 in non-small cell lung cancer[END_REF], respectively. These observations suggest that HS3STs can compensate each other for loss of their expression depending on the molecular signature and tissue environment of cancer cells.

During cancer progression, developing tumor cells are exposed to pro-inflammatory mediators that enhance immune anti-tumoral response. In order to evade this immune pressure, tumor cells can change their intrinsic features, thereby resulting in the emergence of cellular variants with increased activation of pro-oncogenic pathways, and less immunogenic phenotype [START_REF] Dunn | Cancer immunoediting: from immunosurveillance to tumor escape[END_REF]. It is of note that upregulation of the expression of HS3ST3B has been observed in many cell types exposed to inflammatory stimuli [START_REF] Zhang | Heparan sulfate D-glucosamine 3-Osulfotransferase 3B1 is a novel regulator of transforming growth factorbeta-mediated epithelial-to-mesenchymal transition and regulated by miR-218 in non-small cell lung cancer[END_REF][START_REF] Krenn | Glycanogenomics: a qPCR-approach to investigate biological glycan function[END_REF][START_REF] Martinez | Macrophage polarization alters the expression and sulfation pattern of glycosaminoglycans[END_REF][START_REF] Sikora | Regulation of the expression of heparan sulfate 3-O-sulfotransferase 3B (HS3ST3B) by inflammatory stimuli in human monocytes[END_REF][START_REF] Sikora | Tumour-necrosis factor-α induces heparan sulfate 6-O-endosulfatase 1 (Sulf-1) expression in fibroblasts[END_REF]. On the other hand, a progressive upregulation of TRF2 was observed during progression of colon cancer. Increased expression of TRF2 was associated with an abnormal expression of HS3ST4, which in turn led to inhibition of NK cell activation and recruitment. The same effects were observed with cancer cells carrying an exogenous expression of HS3ST4, suggesting that the isozyme may be involved in a mechanism of immune escape [START_REF] Biroccio | TRF2 inhibits a cell-extrinsic pathway through which natural killer cells eliminate cancer cells[END_REF]. In line with these findings, we reported that HS3ST-transfected MDA-MB-231 cells were more resistant to apoptosis induced by death receptors ligands or NK cells in vitro [START_REF] Hellec | The heparan sulfate 3-O-sulfotransferases (HS3ST) 2, 3B and 4 enhance proliferation and survival in breast cancer MDA-MB-231 cells[END_REF]. The functions of certain NK cell receptors can be modulated through interactions in cis with HS on NK cells themselves or in trans with HS on target cells. Disruption of cis-interactions releases NK receptors and enhances NK cell functional response. Interestingly, silencing of HS3ST3B in NK cells was found to down-regulate the cis-interactions between HS and the NK receptors KIR2DL4 and NKp46, meaning that the functions of these receptors can be regulated through interactions with 3-O-sulfated HS [START_REF] Brusilovsky | Genome-wide siRNA screen reveals a new cellular partner of NK cell receptor KIR2DL4: heparan sulfate directly modulates KIR2DL4-mediated responses[END_REF][START_REF] Brusilovsky | Regulation of natural cytotoxicity receptors by heparan sulfate proteoglycans in -cis: A lesson from NKp44[END_REF]. It is thus tempting to speculate that 3-O-sulfation in cancer cells may allow cell surface HS to engage in trans interactions with NK cell receptors. Accordingly, upregulation of the expression of certain HS3STs, such as HS3ST3B or HS3ST4, may be a mechanism that permits cancer cells to impact NK cell activation and to escape their elimination. On that assumption, the tumor-promoting properties of HS3STs may rely not only on alteration of intrinsic processes in cancer cells but also on a non-cell autonomous mechanism bypassing immune surveillance.

CONCLUSIONS AND PROSPECTS

The regulation of HS biosynthesis is still poorly understood, and whether other factors can influence specific HS sulfation in a given cell type remains largely unknown. In this respect, we demonstrated that HS3ST3B is a Golgi-resident enzyme, while HS3ST2 is specifically addressed to the plasma membrane. This suggests that different subcellular location of HS3STs may be a regulatory mechanism to produce distinct 3-O-sulfated motifs [START_REF] Delos | Heparan sulfate 3-O-sulfotransferase 2 (HS3ST2) displays an unexpected subcellular localization in the plasma membrane[END_REF]. Initially, HS3STs have been divided into two groups, based on their contribution to the synthesis of anticoagulant-active sequences and binding motifs for HSV-1 gD protein. However, there is recent evidence that the situation is not so simple, and a better characterization of the catalytic activity of HS3STs and its regulation is required to define more precisely the biological functions of each isozyme [START_REF] Wang | Synthesis of 3-Osulfated oligosaccharides to understand the relationship between structures and functions of heparan sulfate[END_REF][START_REF] Kaltenbach | Sulfotransferase and heparanase: remodeling engines in promoting virus infection and disease development[END_REF][START_REF] Gesteira | Structural basis of oligosaccharide processing by glycosaminoglycan sulfotransferases[END_REF][START_REF] Meneghetti | Insights into the role of 3-O-sulfotransferase in heparan sulfate biosynthesis[END_REF]. It is also of note that most of the effects attributed to HS3STs in cancer are arising from in vitro experiments. These findings need to be regarded with caution, because interference with other metabolic processes can have a dramatic impact on cell behavior, without being linked necessarily to changes in HS sulfation [START_REF] Guo | Drosophila heparan sulfate 3-O sulfotransferase B null mutant is viable and exhibits no defects in Notch signaling[END_REF][START_REF] Viola | MDA-MB-231 breast cancer cell viability, motility and matrix adhesion are regulated by a complex interplay of heparan sulfate, chondroitin-/dermatan sulfate and hyaluronan biosynthesis[END_REF]. All of this suggests that alteration in the expression of HS3STs in cancer cells may have diverse functional impacts, which could explain the different action of a particular isoform in a given cell type. Accordingly, the roles of HS3STs in cancer need to be further explored to evaluate the potential of these enzymes as targets for therapeutic strategies in cancer treatment.

Up to now, a lot of attention has been focused on HS mimetics [START_REF] Lanzi | Heparan sulfate mimetics in cancer therapy: the challenge to define structural determinants and the relevance of targets for optimal activity[END_REF][START_REF] Lindahl | Heparan sulfate-protein interactions: a concept for drug design?[END_REF]. A typical example is the synthesis of the pentasaccharide that binds antithrombin III [START_REF] Petitou | A synthetic antithrombin III binding pentasaccharide is now a drug! what comes next?[END_REF]. However, it is still difficult to synthesize oligosaccharides with complex sulfation patterns. A seducing alternative is the use of a chemoenzymatic approach, in which controlled sulfation could be achieved by recombinant enzymes [START_REF] Kaltenbach | Sulfotransferase and heparanase: remodeling engines in promoting virus infection and disease development[END_REF][START_REF] Chen | Enzymatic redesigning of biologically active heparan sulfate[END_REF][START_REF] Kuberan | Enzymatic synthesis of antithrombin III-binding heparan sulfate pentasaccharide[END_REF]. To date, most of the HS3STs have been cloned and used to prepare 3-Osulfated oligosaccharides [START_REF] Mochizuki | Tetrasulfated disaccharide unit in heparan sulfate: enzymatic formation and tissue distribution[END_REF][START_REF] Wang | Synthesis of 3-Osulfated oligosaccharides to understand the relationship between structures and functions of heparan sulfate[END_REF][START_REF] Zhang | The effect of precursor structures on the action of glucosaminyl 3-O-sulfotransferase-1 and the biosynthesis of anticoagulant heparan sulfate[END_REF][START_REF] Thacker | Expanding the 3-O-sulfate proteome -Enhanced binding of neuropilin-1 to 3-O-sulfated heparan sulfate modulates its activity[END_REF]. Some of them have proven to be effective as anticoagulant agents [START_REF] Li | Increased soluble heterologous expression of a rat brain 3-O-sulfotransferase 1 -A key enzyme for heparin biosynthesis[END_REF] and inhibitors of HSV-1 entry [START_REF] Copeland | Using a 3-O-sulfated heparin octasaccharide to inhibit the entry of herpes simplex virus type 1[END_REF]. On the other hand, targeting HS3STs directly to hinder the reaction of 3-O-sulfation may be a challenging endeavor. Byrne et al. [START_REF] Byrne | New tools for carbohydrate sulfation analysis: heparan sulfate 2-O-sulfotransferase (HS2ST) is a target for small-molecule protein kinase inhibitors[END_REF] reported that HS2ST was a target for a variety of cell-permeable small molecules, including kinase inhibitors. These findings suggest that such molecules could be redesigned for specific inhibition of HS sulfotransferases. On this assumption, designing specific HS3ST inhibitors via high-throughput screening of bio-active agents might be a future strategy to control HS 3-O-sulfation in cancer cells. In conclusion, a better understanding of the functions of HS3STs in cancer cells may provide opportunities to use these HS-modifying enzymes as molecular targets to improve therapeutic strategies.

FIGURE 1 |

 1 FIGURE 1 | Schematic representation of HS modifications. The HS polysaccharide is linked by the tetrasaccharide linker [Xyl-Gal-Gal-GlcUA] to a specific serine residue within the HSPG core protein. Elongation of the chain is achieved through the alternative addition of GlcUA and GlcNac residues by the polymerases EXT1/EXT2. (A) The disaccharide units [GlcUA-GlcNAc] are modified by the actions of sulfotransferases and epimerase. The sites of modification by N-deacetylases/N-sulfotransferases (NDST), C5-epimerase, HS 2-O-sulfotransferase (HS2ST), HS 6-O-sulfotransferases (HS6STs), and HS 3-O-sulfotransferases (HS3STs) are indicated. (B) HS modifications do not go to completion, resulting in domains with high, intermediate, and low levels of sulfation, enabling the generation of many HS structures and potential ligand-binding sites. NS, N-sulfo group on GlcN residue; 2S, 2-O-sulfo group on uronic acid residue; 3S and 6S, 3-O and 6-O-sulfo groups on GlcN residue.

TABLE 1 |

 1 Tissue expression of human HS3STs.

	Enzyme	Expression in normal tissues	Potential role in cancer
	HS3ST1	cerebellum (high), spleen (high),	/
		cerebral cortex, kidney, lung,
		stomach, small intestine, colon,
		testis, liver, heart, pancreas, placenta
		(13, 14)
	HS3ST2	cerebral cortex (high), cerebellum,	anti-oncogenic (15-23)
		placenta, spleen, lung, stomach,	pro-tumoral (24, 25)
		small intestine, colon, testis (13, 14)
	HS3ST3A	liver, placenta, spleen, stomach, small	anti-oncogenic (26, 27);
		intestine, colon, testis, heart, lung,	pro-tumoral (27)
		kidney, pancreas (13, 14)
	HS3ST3B	liver (high), placenta (high), spleen	pro-tumoral (25, 28-31)
		(high), stomach, small intestine,
		colon, testis, skeletal muscle, heart,
		lung, kidney, pancreas (13, 14)
	HS3ST4	cerebral cortex (high), cerebellum,	pro-tumoral (25, 32)
		stomach, spleen, testis (13, 14)
	HS3ST5	skeletal muscle (high), placenta,	/
		cerebral cortex, cerebellum, small
		intestine, colon (13, 33, 34)
	HS3ST6	liver, kidney (35)	/

Frontiers in Oncology | www.frontiersin.org

June 2019 | Volume 9 | Article 507

ACKNOWLEDGMENTS

Authors would like to thank Kenji Uchimura for critical reading of the manuscript. This work was conducted in the framework of the GDR GAG (GDR 3739 CNRS).

FUNDING

This work was supported by the University of Lille, France and by the Centre National de la Recherche Scientifique (CNRS).

AUTHOR CONTRIBUTIONS

AD and FA: conception, design, writing, and review of the manuscript.

Conflict of Interest Statement:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.