N-glycan signatures identified in tumor interstitial fluid and serum of breast cancer patients: association with tumor biology and clinical outcome

Ultrasensitive Optical Detection and Elimination of Residual Microtumors with a Postoperative Implantable Hydrogel Sensor for Preventing Cancer Recurrence

In vivo optical imaging of trace biomarkers in residual microtumors holds significant promise for cancer prognosis but poses a formidable challenge. Here, a novel hydrogel sensor is designed for ultrasensitive and specific imaging of the elusive biomarker. This hydrogel sensor seamlessly integrates a molecular beacon nanoprobe with fibroblasts, offering both high tissue retention capability and an impressive signal-to-noise ratio for imaging. Signal amplification is accomplished through exonuclease I-mediated biomarker recycling. The resulting hydrogel sensor sensitively detects the biomarker carcinoembryonic antigen with a detection limit of 1.8 pg mL-1 in test tubes. Moreover, it successfully identifies residual cancer nodules with a median diameter of less than 2 mm in mice bearing partially removed primary triple-negative breast carcinomas (4T1). Notably, this hydrogel sensor is proven effective for the sensitive diagnosis of invasive tumors in post-surgical mice with infiltrating 4T1 cells, leveraging the role of fibroblasts in locally enriching tumor cells. Furthermore, the residual microtumor is rapidly photothermal ablation by polydopamine-based nanoprobe under the guidance of visualization, achieving ≈100% suppression of tumor recurrence and lung metastasis. This work offers a promising alternative strategy for visually detecting residual microtumors, potentially enhancing the prognosis of cancer patients following surgical interventions.

Influence of Desialylation on the Drug Binding Affinity of Human Alpha-1-Acid Glycoprotein Assessed by Microscale Thermophoresis

Human serum alpha-1-acid glycoprotein (AAG) is an acute-phase plasma protein involved in the binding and transport of many drugs, especially basic and lipophilic substances. The sialic acid groups that terminate the N-glycan chains of AAG have been reported to change in response to numerous health conditions and may have an impact on the binding of drugs to AAG. In this study, we quantified the binding between native and desialylated AAG and seven drugs from different pharmacotherapeutic groups (carvedilol, diltiazem, dipyridamole, imipramine, lidocaine, propranolol, vinblastine) using microscale thermophoresis (MST). This method was chosen due to its robustness and high sensitivity, allowing precise quantification of molecular interactions based on the thermophoretic movement of fluorescent molecules. Detailed glycan analysis of native and desialylated AAG showed over 98% reduction in sialic acid content for the enzymatically desialylated AAG. The MST results indicate that desialylation generally alters the binding affinity between AAG and drugs, leading to either an increase or decrease in Kd values, probably due to conformational changes of AAG caused by the different sialic acid content. This effect is also reflected in an increased denaturation temperature of desialylated AAG. Our findings indicate that desialylation impacts free drug concentrations differently, depending on the binding affinity of the drug with AAG relative to human serum albumin (HSA). For drugs such as dipyridamole, lidocaine, and carvedilol, which have a higher affinity for AAG, desialylation significantly changes free drug concentrations. In contrast, drugs such as propranolol, imipramine, and vinblastine, which have a strong albumin binding, show only minimal changes. It is noteworthy that the free drug concentration of dipyridamole is particularly sensitive to changes in AAG concentration and glycosylation, with a decrease of up to 15% being observed, underscoring the need for dosage adjustments in personalized medicine.

Serum-based Comprehensive N-Glycans Profiling Analysis in Different Gastric Disease Stages by Porous Graphitic Carbon Liquid Chromatography-Mass Spectrometry Associated With Potential Marker Discovery

BACKGROUND/AIM

N-glycans are potential serum biomarkers due to their aberrant structure and abundance alteration during disease progression. Few studies have been associated with relative quantitative N-glycans profiling during different gastric disease stages. In this study, we conducted an investigation on the profiling of N-glycans in patients with gastric disease, as well as in healthy controls.

MATERIALS AND METHODS

In this study, the porous graphitization carbon chromatography-high resolution Fourier transform mass spectrometry (PGC-FTMS) method was applied to assess comprehensive N-glycans profiling in patients at different stages of gastric disease, including gastritis, atrophic gastritis, gastric ulcer, gastric polyps, and gastric cancer.

RESULTS

A total of 45 N-glycans (relative abundance >0.1%) were detected, and 9 N-glycans were found to be potential biomarkers for gastric disease detection. Along with the progression of gastric disease, the abundance of sialylated N-glycans increased, while that of core-fucosylated N-glycans decreased. Multivariate statistical analysis demonstrated that N-glycans profiling between gastritis and healthy controls had significant differences. The characteristic N-glycans distinguished gastric cancer from healthy controls, which had strong clinical diagnostic value.

CONCLUSION

The relative quantitative profile of N-glycans in different gastric disease stages was revealed and serum N-glycans are proposed for distinguishing gastric disease stages in clinical application.

Highly-sensitive label-free deep profiling of N-glycans released from biomedically-relevant samples

Alterations of protein glycosylation can serve as sensitive and specific disease biomarkers. Labeling procedures for improved separation and detectability of oligosaccharides have several drawbacks, including incomplete derivatization, side-products, noticeable desialylation/defucosylation, sample loss, and interference with downstream analyses. Here, we develop a label-free workflow based on high sensitivity capillary zone electrophoresis-mass spectrometry (CZE-MS) for profiling of native underivatized released N-glycans. Our workflow provides a >45-fold increase in signal intensity compared to the conventional CZE-MS approaches used for N-glycan analysis. Qualitative and quantitative N-glycan profiling of purified human serum IgG, bovine serum fetuin, bovine pancreas ribonuclease B, blood-derived extracellular vesicle isolates, and total plasma results in the detection of >250, >400, >150, >310, and >520 N-glycans, respectively, using injected amounts equivalent to <25 ng of model protein and nL-levels of plasma-derived samples. Compared to reported results for biological samples of similar amounts and complexity, the number of identified N-glycans is increased up to ~15-fold, enabling highly sensitive analysis of sample amounts as low as sub-0.2 nL of plasma volume equivalents. Furthermore, highly sialylated N-glycans are identified and structurally characterized, and untreated sialic acid-linkage isomers are resolved in a single CZE-MS analysis.

Identification and validation of IgG N-glycosylation biomarkers of esophageal carcinoma

Introduction

Altered Immunoglobulin G (IgG) N-glycosylation is associated with aging, inflammation, and diseases status, while its effect on esophageal squamous cell carcinoma (ESCC) remains unknown. As far as we know, this is the first study to explore and validate the association of IgG N-glycosylation and the carcinogenesis progression of ESCC, providing innovative biomarkers for the predictive identification and targeted prevention of ESCC.

Methods

In total, 496 individuals of ESCC (n=114), precancerosis (n=187) and controls (n=195) from the discovery population (n=348) and validation population (n=148) were recruited in the study. IgG N-glycosylation profile was analyzed and an ESCC-related glycan score was composed by a stepwise ordinal logistic model in the discovery population. The receiver operating characteristic (ROC) curve with the bootstrapping procedure was used to assess the performance of the glycan score.

Results

In the discovery population, the adjusted OR of GP20 (digalactosylated monosialylated biantennary with core and antennary fucose), IGP33 (the ratio of all fucosylated monosyalilated and disialylated structures), IGP44 (the proportion of high mannose glycan structures in total neutral IgG glycans), IGP58 (the percentage of all fucosylated structures in total neutral IgG glycans), IGP75 (the incidence of bisecting GlcNAc in all fucosylated digalactosylated structures in total neutral IgG glycans), and the glycan score are 4.03 (95% CI: 3.03-5.36, P&lt;0.001), 0.69 (95% CI: 0.55-0.87, P&lt;0.001), 0.56 (95% CI: 0.45-0.69, P&lt;0.001), 0.52 (95% CI: 0.41-0.65, P&lt;0.001), 7.17 (95% CI: 4.77-10.79, P&lt;0.001), and 2.86 (95% CI: 2.33-3.53, P&lt;0.001), respectively. Individuals in the highest tertile of the glycan score own an increased risk (OR: 11.41), compared with those in the lowest. The average multi-class AUC are 0.822 (95% CI: 0.786-0.849). Findings are verified in the validation population, with an average AUC of 0.807 (95% CI: 0.758-0.864).

Discussion

Our study demonstrated that IgG N-glycans and the proposed glycan score appear to be promising predictive markers for ESCC, contributing to the early prevention of esophageal cancer. From the perspective of biological mechanism, IgG fucosylation and mannosylation might involve in the carcinogenesis progression of ESCC, and provide potential therapeutic targets for personalized interventions of cancer progression.

Device-Controlled Microcondensation for Spatially Confined On-Tissue Digests in MALDI Imaging of N-Glycans

On-tissue enzymatic digestion is a prerequisite for MALDI mass spectrometry imaging (MSI) and spatialomic analysis of tissue proteins and their N-glycan conjugates. Despite the more widely accepted importance of N-glycans as diagnostic and prognostic biomarkers of many diseases and their potential as pharmacodynamic markers, the crucial sample preparation step, namely on-tissue digestion with enzymes like PNGaseF, is currently mainly carried out by specialized laboratories using home-built incubation arrangements, e.g., petri dishes placed in an incubator. Standardized spatially confined enzyme digests, however, require precise control and possible regulation of humidity and temperature, as high humidity increases the risk of analyte dislocation and low humidity compromises enzyme function. Here, a digestion device that controls humidity by cyclic ventilation and heating of the slide holder and the chamber lid was designed to enable controlled micro-condensation on the slide and to stabilize and monitor the digestion process. The device presented here may help with standardization in MSI. Using sagittal mouse brain sections and xenografted human U87 glioblastoma cells in CD1 nu/nu mouse brain, a device-controlled workflow for MALDI MSI of N-glycans was developed.

MS-based glycomics: An analytical tool to assess nervous system diseases

Neurological diseases affect millions of peopleochemistryorldwide and are continuously increasing due to the globe's aging population. Such diseases affect the nervous system and are characterized by a progressive decline in brain function and progressive cognitive impairment, decreasing the quality of life for those with the disease as well as for their families and loved ones. The increased burden of nervous system diseases demands a deeper insight into the biomolecular mechanisms at work during disease development in order to improve clinical diagnosis and drug design. Recently, evidence has related glycosylation to nervous system diseases. Glycosylation is a vital post-translational modification that mediates many biological functions, and aberrant glycosylation has been associated with a variety of diseases. Thus, the investigation of glycosylation in neurological diseases could provide novel biomarkers and information for disease pathology. During the last decades, many techniques have been developed for facilitation of reliable and efficient glycomic analysis. Among these, mass spectrometry (MS) is considered the most powerful tool for glycan analysis due to its high resolution, high sensitivity, and the ability to acquire adequate structural information for glycan identification. Along with MS, a variety of approaches and strategies are employed to enhance the MS-based identification and quantitation of glycans in neurological samples. Here, we review the advanced glycomic tools used in nervous system disease studies, including separation techniques prior to MS, fragmentation techniques in MS, and corresponding strategies. The glycan markers in common clinical nervous system diseases discovered by utilizing such MS-based glycomic tools are also summarized and discussed.

High-Throughput Glycomic Methods

Glycomics aims to identify the structure and function of the glycome, the complete set of oligosaccharides (glycans), produced in a given cell or organism, as well as to identify genes and other factors that govern glycosylation. This challenging endeavor requires highly robust, sensitive, and potentially automatable analytical technologies for the analysis of hundreds or thousands of glycomes in a timely manner (termed high-throughput glycomics). This review provides a historic overview as well as highlights recent developments and challenges of glycomic profiling by the most prominent high-throughput glycomic approaches, with N-glycosylation analysis as the focal point. It describes the current state-of-the-art regarding levels of characterization and most widely used technologies, selected applications of high-throughput glycomics in deciphering glycosylation process in healthy and disease states, as well as future perspectives.

Computational gene expression analysis reveals distinct molecular subgroups of T-cell prolymphocytic leukemia

T-cell prolymphocytic leukemia (T-PLL) is a rare blood cancer with poor prognosis. Overexpression of the proto-oncogene TCL1A and missense mutations of the tumor suppressor ATM are putative main drivers of T-PLL development, but so far only little is known about the existence of T-PLL gene expression subtypes. We performed an in-depth computational reanalysis of 68 gene expression profiles of one of the largest currently existing T-PLL patient cohorts. Hierarchical clustering combined with bootstrapping revealed three robust T-PLL gene expression subgroups. Additional comparative analyses revealed similarities and differences of these subgroups at the level of individual genes, signaling and metabolic pathways, and associated gene regulatory networks. Differences were mainly reflected at the transcriptomic level, whereas gene copy number profiles of the three subgroups were much more similar to each other, except for few characteristic differences like duplications of parts of the chromosomes 7, 8, 14, and 22. At the network level, most of the 41 predicted potential major regulators showed subgroup-specific expression levels that differed at least in comparison to one other subgroup. Functional annotations suggest that these regulators contribute to differences between the subgroups by altering processes like immune responses, angiogenesis, cellular respiration, cell proliferation, apoptosis, or migration. Most of these regulators are known from other cancers and several of them have been reported in relation to leukemia (e.g. AHSP, CXCL8, CXCR2, ELANE, FFAR2, G0S2, GIMAP2, IL1RN, LCN2, MBTD1, PPP1R15A). The existence of the three revealed T-PLL subgroups was further validated by a classification of T-PLL patients from two other smaller cohorts. Overall, our study contributes to an improved stratification of T-PLL and the observed subgroup-specific molecular characteristics could help to develop urgently needed targeted treatment strategies.

Recent advances and trends in sample preparation and chemical modification for glycan analysis

Growing significance of glycosylation in protein functions has accelerated the development of methodologies for detection, identification, and characterization of protein glycosylation. In the past decade, glycobiology research has been advanced by innovative techniques with further progression in the post-genome era. Although significant technical progress has been made in terms of analytical throughput, comprehensiveness, and sensitivity, most methods for glycosylation analysis still require laborious and time-consuming sample preparation tasks. Additionally, sample preparation methods that are focused on specific glycan(s) require an in-depth understanding of various issues in glycobiology. In this review, modern sample preparation and chemical modification methods for the structural and quantitative glycan analyses together with the challenges and advantages of recent sample preparation methods are summarized. The techniques presented herein can facilitate the exploration of biomarkers, understanding of unknown glycan functions, and development of biopharmaceuticals.

Serum N-glycan profiles differ for various breast cancer subtypes

Breast cancer is the most prevalent cancer in women. Early detection of this disease improves survival and therefore population screenings, based on mammography, are performed. However, the sensitivity of this screening modality is not optimal and new screening methods, such as blood tests, are being explored. Most of the analyses that aim for early detection focus on proteins in the bloodstream. In this study, the biomarker potential of total serum N-glycosylation analysis was explored with regard to detection of breast cancer. In an age-matched case-control setup serum protein N-glycan profiles from 145 breast cancer patients were compared to those from 171 healthy individuals. N-glycans were enzymatically released, chemically derivatized to preserve linkage-specificity of sialic acids and characterized by high resolution mass spectrometry. Logistic regression analysis was used to evaluate associations of specific N-glycan structures as well as N-glycosylation traits with breast cancer. In a case-control comparison three associations were found, namely a lower level of a two triantennary glycans and a higher level of one tetraantennary glycan in cancer patients. Of note, various other N-glycomic signatures that had previously been reported were not replicated in the current cohort. It was further evaluated whether the lack of replication of breast cancer N-glycomic signatures could be partly explained by the heterogenous character of the disease since the studies performed so far were based on cohorts that included diverging subtypes in different numbers. It was found that serum N-glycan profiles differed for the various cancer subtypes that were analyzed in this study.

High-Sensitivity Glycan Profiling of Blood-Derived Immunoglobulin G, Plasma, and Extracellular Vesicle Isolates with Capillary Zone Electrophoresis-Mass Spectrometry

We developed a highly sensitive method for profiling of N-glycans released from proteins based on capillary zone electrophoresis coupled to electrospray ionization mass spectrometry (CZE-ESI-MS) and applied the technique to glycan analysis of plasma and blood-derived isolates. The combination of dopant-enriched nitrogen (DEN)-gas introduced into the nanoelectrospray microenvironment with optimized ionization, desolvation, and CZE-MS conditions improved the detection sensitivity up to ∼100-fold, as directly compared to the conventional mode of instrument operation through peak intensity measurements. Analyses without supplemental pressure increased the resolution ∼7-fold in the separation of closely related and isobaric glycans. The developed method was evaluated for qualitative and quantitative glycan profiling of three types of blood isolates: plasma, total serum immunoglobulin G (IgG), and total plasma extracellular vesicles (EVs). The comparative glycan analysis of IgG and EV isolates and total plasma was conducted for the first time and resulted in detection of >200, >400, and >500 N-glycans for injected sample amounts equivalent to <500 nL of blood. Structural CZE-MS² analysis resulted in the identification of highly diverse glycans, assignment of α-2,6-linked sialic acids, and differentiation of positional isomers. Unmatched depth of N-glycan profiling was achieved compared to previously reported methods for the analysis of minute amounts of similar complexity blood isolates.

High-throughput proteomics of breast cancer interstitial fluid: identification of tumor subtype-specific serologically relevant biomarkers

Despite significant advancements in breast cancer (BC) research, clinicians lack robust serological protein markers for accurate diagnostics and tumor stratification. Tumor interstitial fluid (TIF) accumulates aberrantly externalized proteins within the local tumor space, which can potentially gain access to the circulatory system. As such, TIF may represent a valuable starting point for identifying relevant tumor-specific serological biomarkers. The aim of the study was to perform comprehensive proteomic profiling of TIF to identify proteins associated with BC tumor status and subtype. A liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of 35 TIFs of three main subtypes: luminal (19), Her2 (4), and triple-negative (TNBC) (12) resulted in the identification of > 8800 proteins. Unsupervised hierarchical clustering segregated the TIF proteome into two major clusters, luminal and TNBC/Her2 subgroups. High-grade tumors enriched with tumor infiltrating lymphocytes (TILs) were also stratified from low-grade tumors. A consensus analysis approach, including differential abundance analysis, selection operator regression, and random forest returned a minimal set of 24 proteins associated with BC subtypes, receptor status, and TIL scoring. Among them, a panel of 10 proteins, AGR3, BCAM, CELSR1, MIEN1, NAT1, PIP4K2B, SEC23B, THTPA, TMEM51, and ULBP2, was found to stratify the tumor subtype-specific TIFs. In particular, upregulation of BCAM and CELSR1 differentiates luminal subtypes, while upregulation of MIEN1 differentiates Her2 subtypes. Immunohistochemistry analysis showed a direct correlation between protein abundance in TIFs and intratumor expression levels for all 10 proteins. Sensitivity and specificity were estimated for this protein panel by using an independent, comprehensive breast tumor proteome dataset. The results of this analysis strongly support our data, with eight of the proteins potentially representing biomarkers for stratification of BC subtypes. Five of the most representative proteomics databases currently available were also used to estimate the potential for these selected proteins to serve as putative serological markers.

Automating a Process Convolution Approach to Account for Spatial Information in Imaging Mass Spectrometry Data

In the age of big data, imaging techniques such as imaging mass spectrometry (IMS) stand out due to the combination of data size and spatial referencing. However, the data analytic tools readily accessible to investigators often ignore the spatial information or provide results with vague interpretations. We focus on imaging techniques like IMS that collect data along a regular grid and develop methods to automate the process of modeling spatially-referenced imaging data using a process convolution (PC) approach. The PC approach provides a flexible framework to model spatially-referenced geostatistical data, but to make it computationally efficient requires identification of model parameters. We perform simulation studies to define optimal methods for specifying PC parameters and then test those methods using simulations that spike in real spatial information. In doing so, we demonstrate that our methods concurrently account for the spatial information and provide clear interpretations of covariate effects, while maximizing power and maintaining type I error rates near the nominal level. To make these methods accessible, we detail the imagingPC R package. Our approach provides a framework that is flexible and scalable to the level required by many imaging techniques.

CAncer bioMarker Prediction Pipeline (CAMPP)-A standardized framework for the analysis of quantitative biological data

With the improvement of -omics and next-generation sequencing (NGS) methodologies, along with the lowered cost of generating these types of data, the analysis of high-throughput biological data has become standard both for forming and testing biomedical hypotheses. Our knowledge of how to normalize datasets to remove latent undesirable variances has grown extensively, making for standardized data that are easily compared between studies. Here we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper (https://github.com/ELELAB/CAncer-bioMarker-Prediction-Pipeline -CAMPP) intended to aid bioinformatic software-users with data analyses. CAMPP is called from a terminal command line and is supported by a user-friendly manual. The pipeline may be run on a local computer and requires little or no knowledge of programming. To avoid issues relating to R-package updates, a renv .lock file is provided to ensure R-package stability. Data-management includes missing value imputation, data normalization, and distributional checks. CAMPP performs (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis, and (VI) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist in streamlining bioinformatic analysis of quantitative biological data, whilst ensuring an appropriate bio-statistical framework.

Recent advances in glycoinformatic platforms for glycomics and glycoproteomics

Protein glycosylation is the most complex and prevalent post-translation modification in terms of the number of proteins modified and the diversity generated. To understand the functional roles of glycoproteins it is important to gain an insight into the repertoire of oligosaccharides present. The comparison and relative quantitation of glycoforms combined with site-specific identification and occupancy are necessary steps in this direction. Computational platforms have continued to mature assisting researchers with the interpretation of such glycomics and glycoproteomics data sets, but frequently support dedicated workflows and users rely on the manual interpretation of data to gain insights into the glycoproteome. The growth of site-specific knowledge has also led to the implementation of machine-learning algorithms to predict glycosylation which is now being integrated into glycoproteomics pipelines. This short review describes commercial and open-access databases and software with an emphasis on those that are actively maintained and designed to support current analytical workflows.

Comprehensive N-Glycome Profiling of Cells and Tissues for Breast Cancer Diagnosis

Aberrant protein glycosylation is observed in the progression of many types of diseases, including different cancers. In this study, we assess differential N-glycan patterns of human breast cancer cells and tissues by PGC-ESI-MS/MS. Compared with mammary epithelial cells, high-mannose glycans were significantly elevated in breast cancer cells. However, the alteration of N-glycans in tissues was more obvious than that in cells. Sixty-three kinds of different N-glycans were stably identified, and 38 types of them exhibited significant differences between para-carcinoma and breast cancer tissues. High-mannose glycans and core-fucosylated glycans were increased in the breast cancer tissues, while bisected glycans and sialylated glycans were decreased. Moreover, a total of 27 types of N-glycans displayed evident differences between benign breast tumor and breast cancer tissues, and most of them including bisected and sialylated glycans exhibited decreased relative abundances in cancer tissues. Overall, three high-mannose N-glycans (F0H6N2S0, F0H7N2S0, F0H8N2S0) exhibited significant diagnostic accuracy in both breast cancer cells and tissues, suggesting their potential role in biomarkers. Furthermore, a negative correlation between sialylated glycans and age of patients was identified. In conclusion, our results may be beneficial to understand the role that N-glycan plays on the progression of breast cancer and propose potential diagnostic biomarkers.

New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7.

The advent of Next-Generation Sequencing (NGS) technologies has been generating a massive amount of data which require continuous efforts in developing and maintain computational tool for data analyses. The Genomic Data Commons (GDC) Data Portal is a platform that contains different cancer genomic studies. Such platforms have often the primary focus on the data storage and they do not provide a comprehensive toolkit for analyses. To fulfil this urgent need, comprehensive but accessible computational protocols that do not renounce a robust statistical framework are thus required. In this context, we here present the new functions of the R/Bioconductor package TCGAbiolinks to improve the discovery of differentially expressed genes in cancer and tumor (sub)types, include the estimate of tumor purity and tumor infiltrations, use normal samples from other platforms and support more broadly other genomics datasets.

Clinical application of quantitative glycomics

INTRODUCTION

Aberrant glycosylation has been associated with many diseases. Decades of research activities have reported many reliable glycan biomarkers of different diseases which enable effective disease diagnostics and prognostics. However, none of the glycan markers have been approved for clinical diagnosis. Thus, a review of these studies is needed to guide the successful clinical translation. Area covered: In this review, we describe and discuss advances in analytical methods enabling clinical glycan biomarker discovery, focusing only on studies of released glycans. This review also summarizes the different glycobiomarkers identified for cancers, Alzheimer's disease, diabetes, hepatitis B and C, and other diseases. Expert commentary: Along with the development of techniques in quantitative glycomics, more glycans or glycan patterns have been reported as better potential biomarkers of different diseases and proved to have greater diagnostic/diagnostic sensitivity and specificity than existing markers. However, to successfully apply glycan markers in clinical diagnosis, more studies and verifications on large biological cohorts need to be performed. In addition, faster and more efficient glycomic strategies need to be developed to shorten the turnaround time. Thus, glycan biomarkers have an immense chance to be used in clinical prognosis and diagnosis of many diseases in the near future.

Cancer Cell Glycocalyx and Its Significance in Cancer Progression

Cancer is a malignant tumor that threatens the health of human beings, and has become the leading cause of death in urban and rural residents in China. The glycocalyx is a layer of multifunctional glycans that covers the surfaces of a variety of cells, including vascular endothelial cells, smooth muscle cells, stem cells, epithelial, osteocytes, as well as cancer cells. The glycosylation and syndecan of cancer cell glycocalyx are unique. However, heparan sulfate (HS), hyaluronic acid (HA), and syndecan are all closely associated with the processes of cancer progression, including cell migration and metastasis, tumor cell adhesion, tumorigenesis, and tumor growth. The possible underlying mechanisms may be the interruption of its barrier function, its radical role in growth factor storage, signaling, and mechanotransduction. In the later sections, we discuss glycocalyx targeting therapeutic approaches reported in animal and clinical experiments. The study concludes that cancer cells’ glycocalyx and its role in cancer progression are beginning to be known by more groups, and future studies should pay more attention to its mechanotransduction of interstitial flow-induced shear stress, seeking promising therapeutic targets with less toxicity but more specificity.