Glycoproteomics-based signatures for tumor subtyping and clinical outcome prediction of high-grade serous ovarian cancer

Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort study

BACKGROUND

Ovarian cancer (OC) ranks as the most lethal gynaecological malignancy worldwide, with early diagnosis being crucial yet challenging. Current diagnostic methods like transvaginal ultrasound and blood biomarkers show limited sensitivity/specificity. This study aimed to identify and validate serum metabolic biomarkers for OC diagnosis using the largest cohort reported to date.

METHODS

We constructed a large-scale OC-associated cohort of 1432 subjects, including 662 OC, 563 benign ovarian disease, and 207 healthy control subjects, across retrospective (n = 1073) and set-aside validation (n = 359) cohorts. Serum metabolic fingerprints (SMFs) were recorded using nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI-MS). A diagnostic panel was developed through machine learning of SMFs in the discovery cohort and validated in independent verification and set-aside validation cohorts. The identified metabolic biomarkers were further validated using liquid chromatography MS and their biological functions were assessed in OC cell lines.

FINDINGS

We identified a metabolic biomarker panel including glucose, histidine, pyrrole-2-carboxylic acid, and dihydrothymine. This panel achieved consistent areas under the curve (AUCs) of 0.87-0.89 for distinguishing between malignant and benign ovarian masses across all cohorts, and improved to AUCs of 0.95-0.99 when combined with risk of ovarian malignancy algorithm (ROMA). In vitro validation provided initial biological context for the metabolic alterations observed in our diagnostic panel.

INTERPRETATION

Our study established a reliable serum metabolic biomarker panel for OC diagnosis with potential clinical translations. The NELDI-MS based approach offers advantages of fast analytical speed (∼30 s/sample) and low cost (∼2-3 dollars/sample), making it suitable for large-scale clinical applications.

FUNDING

MOST (2021YFA0910100), NSFC (82421001, 823B2050, 824B2059, and 82173077), Medical-Engineering Joint Funds of Shanghai Jiao Tong University (YG2021GD02, YG2024ZD07, and YG2023ZD08), Shanghai Science and Technology Committee Project (23JC1403000), Shanghai Institutions of Higher Learning (2021-01-07-00-02-E00083), Shanghai Jiao Tong University Inner Mongolia Research Institute (2022XYJG0001-01-16), Sichuan Provincial Department of Science and Technology (2024YFHZ0176), Innovation Research Plan by the Shanghai Municipal Education Commission (ZXWF082101), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210700), Basic-Clinical Collaborative Innovation Project from Shanghai Immune Therapy Institute, Guangdong Basic and Applied Basic Research Foundation (2024A1515013255).

Deciphering the Metabolic Impact and Clinical Relevance of N-Glycosylation in Colorectal Cancer through Comprehensive Glycoproteomic Profiling

Colorectal cancer (CRC) progression is driven by complex metabolic alterations, including aberrant N-glycosylation patterns that critically influence tumor development. However, the metabolic and functional roles of N-glycosylation in CRC remain poorly understood. Herein, comprehensive proteomic and N-linked intact glycoproteomics analyses are performed on 45 CRC tumors, and normal adjacent tissues (NATs) are matched, identifying 7125 intact N-glycopeptides from 704 glycoproteins. Through analysis of glycoform expression profiles and structural characteristics, a glycosylation site-protein function association network is constructed to uncover metabolic dysregulation driven by N-glycosylation in CRC. Moreover, an arithmetic model is developed that integrates N-glycan expression patterns, which effectively distinguishes tumors from NATs, reflecting metabolic reprogramming in cancer. These findings identify Chloride Channel Accessory 1 (CLCA1) and Olfactomedin 4 (OLFM4) as potential metabolic biomarkers for CRC diagnosis. Immunohistochemistry and Cox regression analyses validated the prognostic power of these markers. Notably, the critical role of specific N-glycosylation at N196 of Adipocyte plasma membrane-associated protein (APMAP) is highlighted, a key player in tumor metabolism and CRC progression, providing a potential target for therapeutic intervention. These findings offer valuable insights into the metabolic roles of N-glycosylation in CRC, advancing biomarker discovery, enhancing metabolic-based diagnostic precision, and improving personalized treatment strategies targeting cancer metabolism.

SiaQuant Unveils Serum α2,3/α2,6 Sialylation Heterogeneities and Predicts Neoadjuvant Chemotherapy Response in Locally Advanced Cervical Cancer

Sialylation significantly influences tumor progression, invasion, and metastasis. Accurately characterizing sialylated N-glycopeptides (SGPs), particularly the linkage-specific analysis of α2,3,α2,6 sialic acids, remains a challenging yet crucial task in glycoproteomics. Notably, to date, there is a notable lack of detailed studies on α2,3/α2,6 sialylation in serum. Here, we present SiaQuant, an integrated strategy that employs liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), capitalizing on the distinctive separation of characteristic isomeric glycan fragments in ion mobility to accurately analyze serum α2,3/α2,6 sialylation patterns. It first provides proteome-wide insights into α2,3/α2,6 sialylation in serum, revealing three-dimensional heterogeneities across N-glycans, N-glycosites, and N-glycoproteins. Additionally, SiaQuant identifies potential candidate biomarkers for neoadjuvant chemotherapy (NACT) response in locally advanced cervical cancer (LACC), where the elevated level of α2,3/α2,6 sialylation was found to be associated with NACT resistance. In summary, SiaQuant offers the most comprehensive site- and linkage-specific N-glycosylation profiling of serum and shows great potential in clinical usage.

Temporal resolved multi-proteomic analysis enabled the systematic characterization of N-glycosylation pattern changes during Jurkat T cell activation

Protein glycosylation plays essential roles in regulating innate and adaptive immune response. Previous studies only focused on individual protein-glycan interactions or specific glycoform changes during T cell activation, yet the systematic characterization of protein glycosylation alterations remains insufficiently elucidated. To address these limitations, we conducted temporally resolved quantitative analysis of glycoforms, site-specific glycans, glycoproteins, and glycosylation enzymes in activated Jurkat T cells, and successfully portrayed the dynamic landscape of protein glycosylation during Jurkat T cell activation. We found the heterogeneity and number of significantly upregulated glycopeptides increased along with activation. For most glycopeptides, their alteration patterns did not correlate with the abundance of their glycoprotein substrates. However, functional molecules including CD69, CD28, and PTPRC demonstrated co-upregulation at both the protein and glycosylation levels. Correlation analysis between glycopeptides and glycotransferases indicated that sialylated or fucosylated peptides were well correlated with enzymes involved in glycan branching and capping. Comparative analysis of global peptides, glycopeptides, and phosphopeptides revealed their distinctive changing patterns along Jurkat T cell activation, and only glycosylation demonstrated a steady increase trend with a large proportion of upregulated glycopeptides. Collectively, this integrated multi-proteomics characterization of activated Jurkat T cells provided insights for the development of novel therapeutic strategy targeting glycosylation.

Demystifying EV heterogeneity: emerging microfluidic technologies for isolation and multiplexed profiling of extracellular vesicles

Extracellular vesicles (EVs) are heterogeneous lipid containers carrying complex molecular cargoes, including proteins, nucleic acids, glycans, etc. These vesicles are closely associated with specific physiological characteristics, which makes them invaluable in the detection and monitoring of various diseases. However, traditional isolation methods are often labour-intensive, inefficient, and time-consuming. In addition, single biomarker analyses are no longer accurate enough to meet diagnostic needs. Routine isolation and molecular analysis of high-purity EVs in clinical applications is even more challenging. In this review, we discuss a promising solution, microfluidic-based techniques, that combine efficient isolation and multiplex detection of EVs, to further demystify EV heterogeneity. These microfluidic-based EV multiplexing platforms will hopefully facilitate development of liquid biopsies and offer promising opportunities for personalised therapy.

Sonication-assisted protein extraction improves proteomic detection of membrane-bound and DNA-binding proteins from tumor tissues

Deep-scale, mass spectrometry-based proteomic studies by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) program involves tissue lysis using urea buffer before data acquisition via mass spectrometry for quantitative global proteomic and phosphoproteomic analysis. This is described in a 2018 protocol1. Here we report an update to this initial protocol by implementing a sonication step into urea-based tissue lysis. Similar to the initial CPTAC protocol, we identified >12,000 proteins and >25,000 phosphopeptides in a tandem mass tag (TMT) set containing both nonsonicated and sonicated tumor tissues from patient-derived xenograft mouse models. An improvement in the detection of membrane-bound and DNA-binding proteins was observed by including the sonication. We also offer recommendations for optimal sonication conditions such as the buffer composition, timing of sonication cycle, instrumentation settings and a troubleshooting section for potential users. Additionally, the protocol is equally applicable to other biological specimens.

Integrative Quantitative Analysis of Platelet Proteome and Site-Specific Glycoproteome Reveals Diagnostic Potential of Platelet Glycoproteins for Liver Cancer

The role of peripheral blood platelets as indicators of cancer progression is increasingly recognized, and the significance of abnormal glycosylation in platelet function and related disorders is gaining attention. However, the potential of platelets as a source of protein site-specific glycosylation for cancer diagnosis remains underexplored. In this study, we proposed a general pipeline that integrates quantitative proteomics with site-specific glycoproteomics, allowing for an in-depth investigation of the platelet glycoproteome. With this pipeline, we generated a data set comprising 3,466 proteins with qualitative information, 3,199 proteins with quantitative information, 3,419 site-specific glycans with qualitative information and 3,377 site-specific glycans with quantitative information from peripheral blood platelets of hepatocellular carcinoma (HCC) patients, metastatic liver cancer (mLC) patients, and healthy controls. The integrated analysis revealed significant changes in platelet protein N-glycosylation in liver cancer patients. Further systems biology analysis and lectin pull-down-coupled ELISA assays in independent clinical samples confirmed two N-glycoproteins with specific glycan types, complement C3 (C3) with oligomannose modification and integrin β-3 (ITGB3) with sialylation, as potential biomarkers distinguishing liver cancer patients from healthy individuals, without differentiating between HCC and mLC patient group. These findings highlight the potential of platelet protein glycosylation as biomarkers.

Quantitative Site-Specific Glycoproteomics Reveals Glyco-Signatures for Breast Cancer Diagnosis

Intact glycopeptide characterization by mass spectrometry has proven to be a versatile tool for site-specific glycoproteomics analysis and biomarker screening. Here, we present a method using a new model of a Q-TOF instrument equipped with a Zeno trap for intact glycopeptide identification and demonstrate its ability to analyze large-cohort glycoproteomes. From 124 clinical serum samples of breast cancer, noncancerous diseases, and nondisease controls, a total of 6901 unique site-specific glycans on 807 glycosites of proteins were detected. Much more differences of glycoproteome were observed in breast diseases than the proteome. By employing machine learning, 15 site-specific glycans were determined as potential glyco-signatures in detecting breast cancer. The results demonstrate that our method provides a powerful tool in glycoproteomic studies.

Comparative Proteomics and N-Glycoproteomics Reveal the Effects of Different Plasma Protein Enrichment Technologies

Human plasma proteomic and glycoproteomic analyses have emerged as an alternate avenue to identify disease biomarkers and therapeutic approaches. However, the vast number of high-abundance proteins in plasma can cause mass spectrometry (MS) suppression, which makes it challenging to detect low-abundance proteins (LAP). Currently, immunoaffinity-based depletion methods and strategies involving nanomaterial protein coronas have been developed to remove high-abundance proteins (HAP) and enhance the depth of plasma protein identification. Despite these advancements, there is a lack of systematic comparison and evaluation of the qualitative and quantitative effects of different strategies on the human plasma proteome and glycoproteome. In this study, we evaluated the performance of four depletion methods including combinatorial peptide ligand libraries (CPLL), Top 2, Top 14, and the nanomaterial protein corona formed by magnetic nanoparticles (MN) in both plasma proteomics and N-glycoproteomics. Compared to the CPLL, Top 2, and Top 14 strategies, the MN approach significantly increased the number of identified peptides and proteins. However, it demonstrated a relatively lower efficacy in identifying intact N-glycopeptides and N-glycoproteins. In contrast, the immunoaffinity-based depletion methods are better suited to glycoproteomics due to higher identification numbers. We believe that this work provides valuable insights and options for various research objectives, as well as clinical applications.

Integrated proteomics and scRNA-seq analyses of ovarian cancer reveal molecular subtype-associated cell landscapes and immunotherapy targets

BACKGROUND

Epithelial ovarian cancer (EOC) represents the most lethal gynaecological malignancy, yet understanding the connections between its molecular subtypes and their therapeutic implications remains incomplete.

METHODS

We conducted mass spectrometry-based proteomics analyses of 154 EOC tumour samples and 29 normal fallopian tubes, and single-cell RNA sequencing (scRNA-seq) analyses of an additional eight EOC tumours to classify proteomic subtypes and assess their cellular ecosystems and clinical significance. The efficacy of identified therapeutic targets was evaluated in patient-derived xenograft (PDX) and orthotopic mouse models.

RESULTS

We identified four proteomic subtypes with distinct clinical relevance: malignant proliferative (C1), immune infiltrating (C2), Fallopian-like (C3) and differentiated (C4) subtypes. C2 subtype was characterized by lymphocyte infiltration, notably an increased presence of GZMK CD8+ T cells and phagocytosis-like MRC+ macrophages. Additionally, we identified CD40 as a specific prognostic factor for C2 subtype. The interaction between CD40+ phagocytosis-like macrophages and CD40RL+ IL17R CD4+ T cells was correlated with a favourable prognosis. Finally, we established a druggable landscape for non-immune EOC patients and verified a TYMP inhibitor as a promising therapeutic strategy.

CONCLUSIONS

Our study refines the current immune subtype for EOC, highlighting CD40 agonists as promising therapies for C2 subtype patients and targeting TYMP for non-immune patients.

An 11-gene glycosyltransferases-related model for the prognosis of patients with bladder urothelial carcinoma: development and validation based on TCGA and GEO datasets

Background

Bladder urothelial carcinoma (BLCA) is a highly heterogeneous cancer with a wide range of prognoses, ranging from low-grade non-muscle-invasive bladder cancer (NMIBC), which has a good prognosis but a high recurrence rate, to high-grade muscle-invasive bladder cancer (MIBC), which has a poor prognosis. Glycosylation dysregulation plays a significant role in cancer development. Therefore, this study aimed to investigate the role of glycosyltransferases (GT)-related genes in the prognosis of BLCA and to develop a prognostic model based on these genes to predict overall survival (OS) and assess its clinical application.

Methods

The Cancer Genome Atlas (TCGA)-BLCA dataset, comprising 411 tumor and 19 normal samples. The validation set, GSE13507 from the Gene Expression Omnibus (GEO) database, included 165 primary bladder cancer samples with survival data. Differentially expressed GT-related genes (DEGRGs) in BLCA were identified in the training set. Predictive DEGRGs were used to construct risk score models by univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. The predictive value of the models was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) analysis in the training and validation sets. A nomogram was developed and its performance was evaluated with calibration curves. In addition, the relationship between the risk score and the tumor immune microenvironment was explored, and tumor immune dysfunction score (TIDE) and immune signature scores were used to predict the response to immunotherapy in BLCA patients.

Results

Thirty-three DEGRGs were identified in the comparison of BLCA patients with control samples. A risk score model was constructed based on 11 of these genes (GYS2, GALNTL6, GLT8D2, PYGB, B3GALNT2, GALNT15, ST6GALNAC3, ST8SIA6, CHPF, ALG9 and B3GALT2). The model performed well in predicting 3-, 5-, and 7-year overall survival (OS), with areas under the curve (AUC) of 0.65, 0.67, and 0.68, respectively. In addition, patients in the high-risk group had significantly lower survival than those in the low-risk group, and there were significant differences in immune status between the two groups. Based on age, tumor stage, T stage, and risk score, a Nomogram was constructed to predict the probability of OS, and the results of the calibration curves showed that the model had high predictive accuracy. Further analysis showed that the rejection score and TIDE were higher in the high-risk group, while the GT-related pathway was significantly upregulated in the high-risk group.

Conclusions

The 11 GT-related genes identified were associated with OS in BLCA patients, suggesting that the model has potential predictive value. At the same time, further research is needed to explore its role in clinical practice.

Tools and techniques for quantitative glycoproteomic analysis

Recent advances in mass spectrometry (MS)-based methods have significantly expanded the capabilities for quantitative glycoproteomics, enabling highly sensitive and accurate quantitation of glycosylation at intact glycopeptide level. These developments have provided valuable insights into the roles of glycoproteins in various biological processes and diseases. In this short review, we summarize pertinent studies on quantitative techniques and tools for site-specific glycoproteomic analysis published over the past decade. We also highlight state-of-the-art MS-based software that facilitate multi-dimension quantification of the glycoproteome, targeted quantification of specific glycopeptides, and the analysis of glycopeptide isomers. Additionally, we discuss the potential applications of these technologies in clinical biomarker discovery and the functional characterization of glycoproteins in health and disease. The review concludes with a discussion of current challenges and future perspectives in the field, emphasizing the need for more precise, high-throughput and efficient methods to further advance quantitative glycoproteomics and its applications.

Quantification and Site-Specific Analysis of Co-occupied N- and O-Glycopeptides

Protein glycosylation is a complex post-translational modification that is generally classified as N- or O-linked. Site-specific analysis of glycopeptides is accomplished with a variety of fragmentation methods, depending on the type of glycosylation being investigated and the instrumentation available. For instance, collisional dissociation methods are frequently used for N-glycoproteomic analysis with the assumption that one N-sequon exists per tryptic peptide. Alternatively, electron-based methods are preferable for O-glycosite localization. However, the presence of simultaneously N- and O-glycosylated peptides could suggest the necessity of electron-based fragmentation methods for N-glycoproteomics, which is not commonly performed. Thus, we quantified the prevalence of N- and O-glycopeptides in mucins and other glycoproteins. A much higher frequency of co-occupancy within mucins was detected whereas only a negligible occurrence occurred within nonmucin glycoproteins. This was demonstrated from analyses of recombinant and/or purified proteins, as well as more complex samples. Where co-occupancy occurred, O-glycosites were frequently localized to the Ser/Thr within the N-sequon. Additionally, we found that O-glycans in close proximity to the occupied Asn were predominantly unelaborated core 1 structures, while those further away were more extended. Overall, we demonstrate electron-based methods are required for robust site-specific analysis of mucins, wherein co-occupancy is more prevalent. Conversely, collisional methods are generally sufficient for analyses of other types of glycoproteins.

The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

Lectin-modified drug delivery systems - Recent applications in the oncology field

Chemotherapy with cytotoxic drugs remains the core treatment for cancer but, due to the difficulty to find general and usable biochemical differences between cancer cells and normal cells, many of these drugs are associated with lack of specificity, resulting in side effects and collateral cytotoxicity that impair patients' adherence to therapy. Novel cancer treatments in which the cytotoxic effect is maximized while adverse effects are reduced can be implemented by developing targeted therapies that exploit the specific features of cancer cells, such as the typical expression of aberrant glycans. Modification of drug delivery systems with lectins is one of the strategies to implement targeted chemotherapies, as lectins are able to specifically recognize and bind to cancer-associated glycans expressed at the surface of cancer cells, guiding the drug treatment towards these cells and not affecting healthy ones. In this paper, recent advances on the development of lectin-modified drug delivery systems for targeted cancer treatments are thoroughly reviewed, with a focus on their properties and performance in diverse applications, as well as their main advantages and limitations. The synthesis and analytical characterization of the cited lectin-modified drug delivery systems is also briefly described. A comparison with free-drug treatments and with antibody-modified drug delivery systems is presented, emphasizing the advantages of lectin-modified drug delivery systems. Main constraints and potential challenges of lectin-modified drug delivery systems, including key difficulties for clinical translation of these systems, and the required developments in this area, are also signalled.

NEU4-mediated desialylation enhances the activation of the oncogenic receptors for the dissemination of ovarian carcinoma

Glycosylation profoundly influences the interactions between cancer cells and microenvironmental stromal cells during the peritoneal disseminated metastasis of ovarian carcinoma (OC), which is the major cause of cancer-related death. Although the characteristic cancer glycoconjugates are widely used as biomarkers for cancer diagnosis, our knowledge about cancer glycome remains quite fragmented due to the technique limitations in analyzing glycan chains with tremendous structural and functional heterogeneity. Given the dysregulated cancer glycome is defined by the altered glycosylation machinery, here we performed a systematic loss-of-function screen on 498 genes involved in glycosylation for key regulators of OC dissemination. We identified neuraminidase 4 (NEU4), an enzyme capable of hydrolyzing terminal sialic acid from glycoconjugates, as a vital peritoneal dissemination-promoting modifier of OC glycome. In human patients with high-grade serous OC (HGSOC), increased NEU4 was detected in the disseminated OC cells when compared with that in the primary tumor cells, which significantly correlated with the worse survival. Among three alternative splice-generated isoforms of human NEU4, we revealed that only the plasma membrane-localized NEU4 isoform 2 (NEU4-iso2) and intracellular isoform 3 promoted the peritoneal dissemination of OC by enhancing the cell motility and epithelial-mesenchymal transition. We also identified NEU4-iso2-regulated cell surface glycoproteome and found that NEU4-iso2 desialylated the epithelial growth factor receptor (EGFR), in particular at N196 residue, for the hyperactivation of EGFR and its downstream tumor-promoting signaling cascades. Our results provide new insights into how the OC glycome is dysregulated during OC progression and reveal a functionally important glycosite on EGFR for its abnormal activation in cancer.

Glycan diversity in ovarian cancer: Unraveling the immune interplay and therapeutic prospects

Ovarian cancer remains a formidable challenge in oncology due to its late-stage diagnosis and limited treatment options. Recent research has revealed the intricate interplay between glycan diversity and the immune microenvironment within ovarian tumors, shedding new light on potential therapeutic strategies. This review seeks to investigate the complex role of glycans in ovarian cancer and their impact on the immune response. Glycans, complex sugar molecules decorating cell surfaces and secreted proteins, have emerged as key regulators of immune surveillance in ovarian cancer. Aberrant glycosylation patterns can promote immune evasion by shielding tumor cells from immune recognition, enabling disease progression. Conversely, certain glycan structures can modulate the immune response, leading to either antitumor immunity or immune tolerance. Understanding the intricate relationship between glycan diversity and immune interactions in ovarian cancer holds promise for the development of innovative therapeutic approaches. Immunotherapies that target glycan-mediated immune evasion, such as glycan-based vaccines or checkpoint inhibitors, are under investigation. Additionally, glycan profiling may serve as a diagnostic tool for patient stratification and treatment selection. This review underscores the emerging importance of glycan diversity in ovarian cancer, emphasizing the potential for unraveling immune interplay and advancing tailored therapeutic prospects for this devastating disease.

Clinical glycoproteomics: methods and diseases

Glycoproteins, representing a significant proportion of posttranslational products, play pivotal roles in various biological processes, such as signal transduction and immune response. Abnormal glycosylation may lead to structural and functional changes of glycoprotein, which is closely related to the occurrence and development of various diseases. Consequently, exploring protein glycosylation can shed light on the mechanisms behind disease manifestation and pave the way for innovative diagnostic and therapeutic strategies. Nonetheless, the study of clinical glycoproteomics is fraught with challenges due to the low abundance and intricate structures of glycosylation. Recent advancements in mass spectrometry-based clinical glycoproteomics have improved our ability to identify abnormal glycoproteins in clinical samples. In this review, we aim to provide a comprehensive overview of the foundational principles and recent advancements in clinical glycoproteomic methodologies and applications. Furthermore, we discussed the typical characteristics, underlying functions, and mechanisms of glycoproteins in various diseases, such as brain diseases, cardiovascular diseases, cancers, kidney diseases, and metabolic diseases. Additionally, we highlighted potential avenues for future development in clinical glycoproteomics. These insights provided in this review will enhance the comprehension of clinical glycoproteomic methods and diseases and promote the elucidation of pathogenesis and the discovery of novel diagnostic biomarkers and therapeutic targets.

Multi-omic profiling of intraductal papillary neoplasms of the pancreas reveals distinct expression patterns and potential markers of progression

In order to advance our understanding of precancers in the pancreas, 69 pancreatic intraductal papillary neoplasms (IPNs), including 64 intraductal papillary mucinous neoplasms (IPMNs) and 5 intraductal oncocytic papillary neoplasms (IOPNs), 32 pancreatic cyst fluid samples, 104 invasive pancreatic ductal adenocarcinomas (PDACs), 43 normal adjacent tissues (NATs), and 76 macro-dissected normal pancreatic ducts (NDs) were analyzed by mass spectrometry. A total of 10,246 proteins and 22,284 glycopeptides were identified in all tissue samples, and 756 proteins with more than 1.5-fold increase in abundance in IPMNs relative to NDs were identified, 45% of which were also identified in cyst fluids. The over-expression of selected proteins was validated by immunolabeling. Proteins and glycoproteins overexpressed in IPMNs included those involved in glycan biosynthesis and the immune system. In addition, multiomics clustering identified two subtypes of IPMNs. This study provides a foundation for understanding tumor progression and targets for earlier detection and therapies.

Significance

This multilevel characterization of intraductal papillary neoplasms of the pancreas provides a foundation for understanding the changes in protein and glycoprotein expression during the progression from normal duct to intraductal papillary neoplasm, and to invasive pancreatic carcinoma, providing a foundation for informed approaches to earlier detection and treatment.

Quantification and site-specific analysis of co-occupied N- and O-glycopeptides

Protein glycosylation is a complex post-translational modification that is generally classified as N- or O-linked. Site-specific analysis of glycopeptides is accomplished with a variety of fragmentation methods, depending on the type of glycosylation being investigated and the instrumentation available. For instance, collisional dissociation methods are frequently used for N-glycoproteomic analysis with the assumption that one N-sequon exists per tryptic peptide. Alternatively, electron-based methods are indispensable for O-glycosite localization. However, the presence of simultaneously N- and O-glycosylated peptides could suggest the necessity of electron-based fragmentation methods for N-glycoproteomics, which is not commonly performed. Thus, we quantified the prevalence of N- and O-glycopeptides in mucins and other glycoproteins. A much higher frequency of co-occupancy within mucins was detected whereas only a negligible occurrence occurred within non-mucin glycoproteins. This was demonstrated from analyses of recombinant and/or purified proteins, as well as more complex samples. Where co-occupancy occurred, O-glycosites were frequently localized to the Ser/Thr within the N-sequon. Additionally, we found that O-glycans in close proximity to the occupied Asn were predominantly unelaborated core 1 structures, while those further away were more extended. Overall, we demonstrate electron-based methods are required for robust site-specific analysis of mucins, wherein co-occupancy is more prevalent. Conversely, collisional methods are generally sufficient for analyses of other types of glycoproteins.

A Fully Automated Online Enrichment and Separation System for Highly Reproducible and In-Depth Analysis of Intact Glycopeptide

A fully automated online enrichment and separation system for intact glycopeptides, named AutoGP, was developed in this study by integrating three different columns in a nano-LC system. Specifically, the peptide mixture from the enzymatic digestion of a complex biological sample was first loaded on a hydrophilic interaction chromatography (HILIC) column. The nonglycopeptides in the sample were washed off the column, and the glycopeptides retained by the HILIC column were eluted to a C18 trap column to achieve an automated glycopeptide enrichment. The enriched glycopeptides were further eluted to a C18 column for separation, and the separated glycopeptides were eventually analyzed by using an orbitrap mass spectrometer (MS). The optimal operating conditions for AutoGP were systemically studied, and the performance of the fully optimized AutoGP was compared with a conventional manual system used for glycopeptide analysis. The experimental evaluation shows that the total number of glycopeptides identified is at least 1.5-fold higher, and the median coefficient of variation for the analyses is at least 50% lower by using AutoGP, as compared to the results acquired by using the manual system. In addition, AutoGP can perform effective analysis even with a 1-μg sample amount, while a 10-μg sample at least will be needed by the manual system, implying an order of magnitude better sensitivity of AutoGP. All the experimental results have consistently proven that AutoGP can be used for much better characterization of intact glycopeptides.

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Glycan-specific molecularly imprinted polymers towards cancer diagnostics: merits, applications, and future perspectives

Aberrant glycans are a hallmark of cancer states. Notably, emerging evidence has demonstrated that the diagnosis of cancers with tumour-specific glycan patterns holds great potential to address unmet medical needs, especially in improving diagnostic sensitivity and selectivity. However, despite vast glycans having been identified as potent markers, glycan-based diagnostic methods remain largely limited in clinical practice. There are several reasons that prevent them from reaching the market, and the lack of anti-glycan antibodies is one of the most challenging hurdles. With the increasing need for accelerating the translational process, numerous efforts have been made to find antibody alternatives, such as lectins, boronic acids and aptamers. However, issues concerning affinity, selectivity, stability and versatility are yet to be fully addressed. Molecularly imprinted polymers (MIPs), synthetic antibody mimics with tailored cavities for target molecules, hold the potential to revolutionize this dismal progress. MIPs can bind a wide range of glycan markers, even those without specific antibodies. This capacity effectively broadens the clinical applicability of glycan-based diagnostics. Additionally, glycoform-resolved diagnosis can also be achieved through customization of MIPs, allowing for more precise diagnostic applications. In this review, we intent to introduce the current status of glycans as potential biomarkers and critically evaluate the challenges that hinder the development of in vitro diagnostic assays, with a particular focus on glycan-specific recognition entities. Moreover, we highlight the key role of MIPs in this area and provide examples of their successful use. Finally, we conclude the review with the remaining challenges, future outlook, and emerging opportunities.

Identification and characterization of intact glycopeptides in human urine

Glycoproteins in urine have the potential to provide a rich class of informative molecules for studying human health and disease. Despite this promise, the urine glycoproteome has been largely uncharacterized. Here, we present the analysis of glycoproteins in human urine using LC-MS/MS-based intact glycopeptide analysis, providing both the identification of protein glycosites and characterization of the glycan composition at specific glycosites. Gene enrichment analysis reveals differences in biological processes, cellular components, and molecular functions in the urine glycoproteome versus the urine proteome, as well as differences based on the major glycan class observed on proteins. Meta-heterogeneity of glycosylation is examined on proteins to determine the variation in glycosylation across multiple sites of a given protein with specific examples of individual sites differing from the glycosylation trends in the overall protein. Taken together, this dataset represents a potentially valuable resource as a baseline characterization of glycoproteins in human urine for future urine glycoproteomics studies.

Dielectrophoretic Capture of Cancer-Derived Small-Extracellular-Vesicle-Bound Janus Nanoparticles via Lectin-Glycan Interaction

Glycosylation is closely related to cellular metabolism and disease progression. In particular, glycan levels in cancer cells and tissues increase during cancer progression. This upregulation of glycosylation in cancer cells may provide a basis for the development of new biomarkers for the targeting and diagnosis of specific cancers. Here, they developed a detection technology for pancreatic cancer cell-derived small extracellular vesicles (PC-sEVs) based on lectin-glycan interactions. Lectins specific for sialic acids are conjugated to Janus nanoparticles to induce interactions with PC-sEVs in a dielectrophoretic (DEP) system. PC-sEVs are selectively bound to the lectin-conjugated Janus nanoparticles (lectin-JNPs) with an affinity comparable to that of conventionally used carbohydrate antigen 19-9 (CA19-9) antibodies. Furthermore, sEVs-bound Lectin-JNPs (sEVs-Lec-JNPs) are manipulated between two electrodes to which an AC signal is applied for DEP capture. In addition, the proposed DEP system can be used to trap the sEVs-Lec-JNP on the electrodes. Their results, which are confirmed by lectin-JNPs using the proposed DEP system followed by target gene analysis, provide a basis for the development of a new early diagnostic marker based on the glycan characteristics of PC-sEVs. In turn, these novel detection methods could overcome the shortcomings of commercially available pancreatic cancer detection techniques.

Detection of Pancreatic Ductal Adenocarcinoma-Associated Proteins in Serum

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types, partly because it is frequently identified at an advanced stage, when surgery is no longer feasible. Therefore, early detection using minimally invasive methods such as blood tests may improve outcomes. However, studies to discover molecular signatures for the early detection of PDAC using blood tests have only been marginally successful. In the current study, a quantitative glycoproteomic approach via data-independent acquisition mass spectrometry was utilized to detect glycoproteins in 29 patient-matched PDAC tissues and sera. A total of 892 N-linked glycopeptides originating from 141 glycoproteins had PDAC-associated changes beyond normal variation. We further evaluated the specificity of these serum-detectable glycoproteins by comparing their abundance in 53 independent PDAC patient sera and 65 cancer-free controls. The PDAC tissue-associated glycoproteins we have identified represent an inventory of serum-detectable PDAC-associated glycoproteins as candidate biomarkers that can be potentially used for the detection of PDAC using blood tests.

Cyst fluid glycoproteins accurately distinguishing malignancies of pancreatic cystic neoplasm

Pancreatic cystic neoplasms (PCNs) are recognized as precursor lesions of pancreatic cancer, with a marked increase in prevalence. Early detection of malignant PCNs is crucial for improving prognosis; however, current diagnostic methods are insufficient for accurately identifying malignant PCNs. Here, we utilized mass spectrometry (MS)-based glycosite- and glycoform-specific glycoproteomics, combined with proteomics, to explore potential cyst fluid diagnostic biomarkers for PCN. The glycoproteomic and proteomic landscape of pancreatic cyst fluid samples from PCN patients was comprehensively investigated, and its characteristics during the malignant transformation of PCN were analyzed. Under the criteria of screening specific cyst fluid biomarkers for the diagnosis of PCN, a group of cyst fluid glycoprotein biomarkers was identified. Through parallel reaction monitoring (PRM)-based targeted glycoproteomic analysis, we validated these chosen glycoprotein biomarkers in a second cohort, ultimately confirming N-glycosylated PHKB (Asn-935, H5N2F0S0; Asn-935, H4N4F0S0; Asn-935, H5N4F0S0), CEACAM5 (Asn-197, H5N4F0S0) and ATP6V0A4 (Asn-367, H6N4F0S0) as promising diagnostic biomarkers for distinguishing malignant PCNs. These glycoprotein biomarkers exhibited robust performance, with an area under the curve ranging from 0.771 to 0.948. In conclusion, we successfully established and conducted MS-based glycoproteomic analysis to identify novel cyst fluid glycoprotein biomarkers for PCN. These findings hold significant clinical implications, providing valuable insights for PCN decision-making, and potentially offering therapeutic targets for PCN treatment.

Proteogenomic data and resources for pan-cancer analysis

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

CRISPR-Cas9 identifies growth-related subtypes of glioblastoma with therapeutical significance through cell line knockdown

BACKGROUND

Glioblastoma (GBM) is a type of highly malignant brain tumor that is known for its significant intratumoral heterogeneity, meaning that there can be a high degree of variability within the tumor tissue. Despite the identification of several subtypes of GBM in recent years, there remains to explore a classification based on genes related to proliferation and growth.

METHODS

The growth-related genes of GBM were identified by CRISPR-Cas9 and univariate Cox regression analysis. The expression of these genes in the Cancer Genome Atlas cohort (TCGA) was used to construct growth-related genes subtypes (GGSs) via consensus clustering. Validation of this subtyping was performed using the nearest template prediction (NTP) algorithm in two independent Gene Expression Omnibus (GEO) cohorts and the ZZ cohort. Additionally, copy number variations, biological functions, and potential drugs were analyzed for each of the different subtypes separately.

RESULTS

Our research established multicenter-validated GGSs. GGS1 exhibits the poorest prognosis, with the highest frequency of chr 7 gain & chr 10 loss, and the lowest frequency of chr 19 & 20 co-gain. Additionally, GGS1 displays the highest expression of EGFR. Furthermore, it is significantly enriched in metabolic, stemness, proliferation, and signaling pathways. Besides we showed that Foretinib may be a potential therapeutic agent for GGS1, the worst prognostic subtype, through data screening and in vitro experiments. GGS2 has a moderate prognosis, with a slightly higher proportion of chr 7 gain & chr 10 loss, and the highest proportion of chr 19 & 20 co-gain. The prognosis of GGS3 is the best, with the least chr 7 gain & 10 loss and EGFR expression.

CONCLUSIONS

These results enhance our understanding of the heterogeneity of GBM and offer insights for stratified management and precise treatment of GBM patients.

Prognostic Value of Lymph Node Parameters in Elderly Patients With Stage III Serous Ovarian Cancer Based on Competing Risk Model

OBJECTIVES

Competing risk models were used in this study. The purpose of this study was to assess the predictive usefulness of lymph node characteristics in elderly patients with stage III serous ovarian cancer.

METHODS

We conducted a retrospective analysis on 148,598 patients from 2010 to 2016 using the surveillance, epidemiology, and end results database. Lymph node characteristics were collected and examined, including the number of lymph nodes retrieved the number of lymph nodes examined (ELN) and the number of positive lymph nodes (PN). Using competing risk models, we evaluated the connection between these variables and overall survival (OS) and disease-specific survival (DSS).

RESULTS

This study included a total of 3457 ovarian cancer patients. Multivariate analysis using the COX proportional hazards model found that ELN>22 was an independent predictive factor for both OS (hazard ratio [HR] [95% CI]=0.688 [0.553 to 0.856], P <0.05) and DSS (HR [95% CI]=0.65 [0.512 to 0.826], P <0.001), PN>8 was identified as a significant risk factor for both OS (HR [95% CI]=0.908 [0.688 to 1.199], P =0.497) and DSS (HR [95% CI]=0.926 [0.684 to 1.254], P =0.62). Subsequently, using the competing risk model, ELN>22 was found to be an independent protective factor for DSS (HR [95% CI]=0.738 [0.574 to 0.949], P =0.018), while PN>8 was identified as a risk factor for DSS (HR [95% CI]=0.999 [0.731 to 1.366], P =1).

CONCLUSIONS

Our findings demonstrate the robustness of the competing risk model to evaluate the results of the COX proportional hazards model analysis.

Gene expression networks involved in multiple cellular programs coexist in individual hepatocellular cancer cells

The gene expression networks of a single cell can be used to reveal cell type- and condition-specific patterns that account for cell states, cell identity, and its responses to environmental changes. We applied single cell sequencing datasets to define mRNA patterns and visualized potential cellular capacities among hepatocellular cancer cells. The expressing numbers and levels of genes were highly heterogenous among the cancer cells. The cellular characteristics were dependent strongly on the expressing numbers and levels of genes, especially oncogenes and anti-oncogenes, in an individual cancer cell. The transcriptional activations of oncogenes and anti-oncogenes were strongly linked to inherent multiple cellular programs, some of which oppose and contend against other processes, in a cancer cell. The gene expression networks of multiple cellular programs proliferation, differentiation, apoptosis, autophagy, epithelial-mesenchymal transition, ATP production, and neurogenesis coexisted in an individual cancer cell. The findings give rise a hypothesis that a cancer cell expresses balanced combinations of genes and undergoes a given biological process by rapidly transmuting gene expressing networks.

Tumor metabolism rewiring in epithelial ovarian cancer

The mortality rate of epithelial ovarian cancer (EOC) remains the first in malignant tumors of the female reproductive system. The characteristics of rapid proliferation, extensive implanted metastasis, and treatment resistance of cancer cells require an extensive metabolism rewiring during the progression of cancer development. EOC cells satisfy their rapid proliferation through the rewiring of perception, uptake, utilization, and regulation of glucose, lipids, and amino acids. Further, complete implanted metastasis by acquiring a superior advantage in microenvironment nutrients competing. Lastly, success evolves under the treatment stress of chemotherapy and targets therapy. Understanding the above metabolic characteristics of EOCs helps to find new methods of its treatment.

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

Deciphering the immune heterogeneity dominated by natural killer cells with prognostic and therapeutic implications in hepatocellular carcinoma

Belonging to type 1 innate lymphoid cells (ILC1), natural killer (NK) cells play an important role not only in fighting microbial infections but also in anti-tumor response. Hepatocellular carcinoma (HCC) represents an inflammation-related malignancy and NK cells are enriched in the liver, making them an essential component of the HCC immune microenvironment. In this study, we performed single-cell RNA-sequencing (scRNA-seq) analysis to identify the NK cell marker genes (NKGs) and uncovered 80 prognosis-related ones by the TCGA-LIHC dataset. Based on prognostic NKGs, HCC patients were categorized into two subtypes with distinct clinical outcomes. Subsequently, we conducted LASSO-COX and stepwise regression analysis on prognostic NKGs to establish a five-gene (UBB, CIRBP, GZMH, NUDC, and NCL) prognostic signature-NKscore. Different mutation statuses of the two risk groups stratified by NKscore were comprehensively characterized. Besides, the established NKscore-integrated nomogram presented enhanced predictive performance. Single sample gene set enrichment analysis (ssGSEA) analysis was used to uncover the landscape of the tumor immune microenvironment (TIME) and the high-NKscore risk group was characterized with an immune-exhausted phenotype while the low-NKscore risk group held relatively strong anti-cancer immunity. T cell receptor (TCR) repertoire, tumor inflammation signature (TIS), and Immunophenoscore (IPS) analyses revealed differences in immunotherapy sensitivity between the two NKscore risk groups. Taken together, we developed a novel NK cell-related signature to predict the prognosis and immunotherapy efficacy for HCC patients.

Integrative Proteomics and N-Glycoproteomics Analyses of Rheumatoid Arthritis Synovium Reveal Immune-Associated Glycopeptides

Rheumatoid arthritis (RA) is a typical autoimmune disease characterized by synovial inflammation, synovial tissue hyperplasia, and destruction of bone and cartilage. Protein glycosylation plays key roles in the pathogenesis of RA but in-depth glycoproteomics analysis of synovial tissues is still lacking. Here, by using a strategy to quantify intact N-glycopeptides, we identified 1260 intact N-glycopeptides from 481 N-glycosites on 334 glycoproteins in RA synovium. Bioinformatics analysis revealed that the hyper-glycosylated proteins in RA were closely linked to immune responses. By using DNASTAR software, we identified 20 N-glycopeptides whose prototype peptides were highly immunogenic. We next calculated the enrichment scores of nine types of immune cells using specific gene sets from public single-cell transcriptomics data of RA and revealed that the N-glycosylation levels at some sites, such as IGSF10_N2147, MOXD2P_N404, and PTCH2_N812, were significantly correlated with the enrichment scores of certain immune cell types. Furthermore, we showed that aberrant N-glycosylation in the RA synovium was related to increased expression of glycosylation enzymes. Collectively, this work presents, for the first time, the N-glycoproteome of RA synovium and describes immune-associated glycosylation, providing novel insights into RA pathogenesis.

Roles of glycosylation at the cancer cell surface: opportunities for large scale glycoproteomics

Cell surface glycosylation has a variety of functions, and its dysregulation in cancer contributes to impaired signaling, metastasis and the evasion of the immune responses. Recently, a number of glycosyltransferases that lead to altered glycosylation have been linked to reduced anti-tumor immune responses: B3GNT3, which is implicated in PD-L1 glycosylation in triple negative breast cancer, FUT8, through fucosylation of B7H3, and B3GNT2, which confers cancer resistance to T cell cytotoxicity. Given the increased appreciation of the relevance of protein glycosylation, there is a critical need for the development of methods that allow for an unbiased interrogation of cell surface glycosylation status. Here we provide an overview of the broad changes in glycosylation at the surface of cancer cell and describe selected examples of receptors with aberrant glycosylation leading to functional changes, with emphasis on immune checkpoint inhibitors, growth-promoting and growth-arresting receptors. Finally, we posit that the field of glycoproteomics has matured to an extent where large-scale profiling of intact glycopeptides from the cell surface is feasible and is poised for discovery of new actionable targets against cancer.

Integrated glycoproteomic characterization of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC), a common form of RCC, is responsible for the high mortality rate of kidney cancer. Dysregulations of glycoproteins have been shown to associate with ccRCC. However, the molecular mechanism has not been well characterized. Here, a comprehensive glycoproteomic analysis is conducted using 103 tumors and 80 paired normal adjacent tissues. Altered glycosylation enzymes and corresponding protein glycosylation are observed, while two of the major ccRCC mutations, BAP1 and PBRM1, show distinct glycosylation profiles. Additionally, inter-tumor heterogeneity and cross-correlation between glycosylation and phosphorylation are observed. The relation of glycoproteomic features to genomic, transcriptomic, proteomic, and phosphoproteomic changes shows the role of glycosylation in ccRCC development with potential for therapeutic interventions. This study reports a large-scale tandem mass tag (TMT)-based quantitative glycoproteomic analysis of ccRCC that can serve as a valuable resource for the community.

Altered Glycosylation in Progression and Management of Bladder Cancer

Bladder cancer (BC) is the 10th most common malignancy worldwide, with an estimated 573,000 new cases and 213,000 deaths in 2020. Available therapeutic approaches are still unable to reduce the incidence of BC metastasis and the high mortality rates of BC patients. Therefore, there is a need to deepen our understanding of the molecular mechanisms underlying BC progression to develop new diagnostic and therapeutic tools. One such mechanism is protein glycosylation. Numerous studies reported changes in glycan biosynthesis during neoplastic transformation, resulting in the appearance of the so-called tumor-associated carbohydrate antigens (TACAs) on the cell surface. TACAs affect a wide range of key biological processes, including tumor cell survival and proliferation, invasion and metastasis, induction of chronic inflammation, angiogenesis, immune evasion, and insensitivity to apoptosis. The purpose of this review is to summarize the current information on how altered glycosylation of bladder cancer cells promotes disease progression and to present the potential use of glycans for diagnostic and therapeutic purposes.

Critical considerations in N-glycoproteomics

N-Glycoproteomics, the system-wide study of glycans asparagine-linked to protein carriers, holds a unique and still largely untapped potential to provide deep insights into the complexity and dynamics of the heterogeneous N-glycoproteome. Despite the advent of innovative analytical and informatics tools aiding the analysis, N-glycoproteomics remains challenging and consequently largely restricted to specialised laboratories. Aiming to stimulate discussions of method harmonisation, data standardisation and reporting guidelines to make N-glycoproteomics more reproducible and accessible to the community, we here discuss critical considerations related to the design and execution of N-glycoproteomics experiments and highlight good practices in N-glycopeptide data collection, analysis, interpretation and sharing. Giving the rapid maturation and, expectedly, a wide-spread implementation of N-glycoproteomics capabilities across the community in future years, this piece aims to point out common pitfalls, to encourage good data sharing and documentation practices, and to highlight practical solutions and strategies to enhance the insight into the N-glycoproteome.

The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection

Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.

Glycomics-Assisted Glycoproteomics Enables Deep and Unbiased N-Glycoproteome Profiling of Complex Biological Specimens

Mass spectrometry-driven glycomics and glycoproteomics, the system-wide profiling of detached glycans and intact glycopeptides from biological samples, respectively, are powerful approaches to interrogate the heterogenous glycoproteome. Efforts to develop integrated workflows employing both glycomics and glycoproteomics have been invested since the concerted application of these complementary approaches enables a deeper exploration of the glycoproteome. This protocol paper outlines, step-by-step, an integrated -omics technology, the "glycomics-assisted glycoproteomics" method, that first establishes the N-glycan fine structures and their quantitative distribution pattern of protein extracts via porous graphitized carbon-LC-MS/MS. The N-glycome information is then used to augment and guide the challenging reversed-phase LC-MS/MS-based profiling of intact N-glycopeptides from the same protein samples. Experimental details and considerations relating to the sample preparation and the N-glycomics and N-glycoproteomics data collection, analysis, and integration are discussed. Benefits of the glycomics-assisted glycoproteomics method, which can be readily applied to both simple and complex biological specimens such as protein extracts from cells, tissues, and bodily fluids (e.g., serum), include quantitative information of the protein carriers and site(s) of glycosylation, site occupancy, and the site-specific glycan structures directly from biological samples. The glycomics-assisted glycoproteomics method therefore facilitates a comprehensive view of the complexity and dynamics of the heterogenous glycoproteome.

High-throughput site-specific N-glycoproteomics reveals glyco-signatures for liver disease diagnosis

The glycoproteome has emerged as a prominent target for screening biomarkers, as altered glycosylation is a hallmark of cancer cells. In this work, we incorporated tandem mass tag labeling into quantitative glycoproteomics by developing a chemical labeling-assisted complementary dissociation method for the multiplexed analysis of intact N-glycopeptides. Benefiting from the complementary nature of two different mass spectrometry dissociation methods for identification and multiplex labeling for quantification of intact N-glycopeptides, we conducted the most comprehensive site-specific and subclass-specific N-glycosylation profiling of human serum immunoglobulin G (IgG) to date. By analysing the serum of 90 human patients with varying severities of liver diseases, as well as healthy controls, we identified that the combination of IgG1-H3N5F1 and IgG4-H4N3 can be used for distinguishing between different stages of liver diseases. Finally, we used targeted parallel reaction monitoring to successfully validate the expression changes of glycosylation in liver diseases in a different sample cohort that included 45 serum samples.

Mass Spectrometry-Based Glycoproteomic Workflows for Cancer Biomarker Discovery

Glycosylation has a clear role in cancer initiation and progression, with numerous studies identifying distinct glycan features or specific glycoproteoforms associated with cancer. Common findings include that aggressive cancers tend to have higher expression levels of enzymes that regulate glycosylation as well as glycoproteins with greater levels of complexity, increased branching, and enhanced chain length1. Research in cancer glycoproteomics over the last 50-plus years has mainly focused on technology development used to observe global changes in glycosylation. Efforts have also been made to connect glycans to their protein carriers as well as to delineate the role of these modifications in intracellular signaling and subsequent cell function. This review discusses currently available techniques utilizing mass spectrometry-based technologies used to study glycosylation and highlights areas for future advancement.

Recent advances in the use of legume lectins for the diagnosis and treatment of breast cancer

Poor lifestyle choices and genetic predisposition are factors that increase the number of cancer cases, one example being breast cancer, the third most diagnosed type of malignancy. Currently, there is a demand for the development of new strategies to ensure early detection and treatment options that could contribute to the complete remission of breast tumors, which could lead to increased overall survival rates. In this context, the glycans observed at the surface of cancer cells are presented as efficient tumor cell markers. These carbohydrate structures can be recognized by lectins which can act as decoders of the glycocode. The application of plant lectins as tools for diagnosis/treatment of breast cancer encompasses the detection and sorting of glycans found in healthy and malignant cells. Here, we present an overview of the most recent studies in this field, demonstrating the potential of lectins as: mapping agents to detect differentially expressed glycans in breast cancer, as histochemistry/cytochemistry analysis agents, in lectin arrays, immobilized in chromatographic matrices, in drug delivery, and as biosensing agents. In addition, we describe lectins that present antiproliferative effects by themselves and/or in conjunction with other drugs in a synergistic effect.

pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level

Large-scale intact glycopeptide identification has been advanced by software tools. However, tools for quantitative analysis remain lagging behind, which hinders exploring the differential site-specific glycosylation. Here, we report pGlycoQuant, a generic tool for both primary and tandem mass spectrometry-based intact glycopeptide quantitation. pGlycoQuant advances in glycopeptide matching through applying a deep learning model that reduces missing values by 19-89% compared with Byologic, MSFragger-Glyco, Skyline, and Proteome Discoverer, as well as a Match In Run algorithm for more glycopeptide coverage, greatly expanding the quantitative function of several widely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. Further application of pGlycoQuant to the N-glycoproteomic study in three different metastatic HCC cell lines quantifies 6435 intact N-glycopeptides and, together with in vitro molecular biology experiments, illustrates site 979-core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies.

Glycoproteomics Landscape of Asymptomatic and Symptomatic Human Alzheimer's Disease Brain

Molecular changes in the brain of individuals afflicted with Alzheimer's disease (AD) are an intense area of study. Little is known about the role of protein abundance and posttranslational modifications in AD progression and treatment, in particular large-scale intact N-linked glycoproteomics analysis. To elucidate the N-glycoproteome landscape, we developed an approach based on multi-lectin affinity enrichment, hydrophilic interaction chromatography, and LC-MS-based glycoproteomics. We analyzed brain tissue from 10 persons with no cognitive impairment or AD, 10 with asymptomatic AD, and 10 with symptomatic AD, detecting over 300 glycoproteins and 1900 glycoforms across the samples. The majority of glycoproteins have N-glycans that are high-mannosidic or complex chains that are fucosylated and bisected. The Man5 N-glycan was found to occur most frequently at >20% of the total glycoforms. Unlike the glycoproteomes of other tissues, sialylation is a minor feature of the brain N-glycoproteome, occurring at <9% among the glycoforms. We observed AD-associated differences in the number of antennae, frequency of fucosylation, bisection, and other monosaccharides at individual glycosylation sites among samples from our three groups. Further analysis revealed glycosylation differences in subcellular compartments across disease stage, including glycoproteins in the lysosome frequently modified with paucimannosidic glycans. These results illustrate the N-glycoproteomics landscape across the spectrum of AD clinical and pathologic severity and will facilitate a deeper understanding of progression and treatment development.

Aberrant N-glycolylneuraminic acid in breast MCF-7 cancer cells and cancer stem cells

N-Glycolylneuraminic acid (Neu5Gc) is not normally detected in humans because humans lack the hydroxylase enzyme that converts cytidine-5'-monophosphate-N-acetylneuraminic acid (CMP-Neu5Ac) to CMP-Neu5Gc; thus, any Neu5Gc appearing in the human body is aberrant. Neu5Gc has been observed in human cancer cells and tissues. Moreover, antibodies against Neu5Gc have been detected in healthy humans, which are obstacles to clinical xenotransplantation and stem cell therapies. Thus, the study of Neu5Gc in humans has important pathological and clinical relevance. Here, we report the N-glycoproteomics characterization of aberrant Neu5Gc in breast MCF-7 cancer cells and cancer stem cells (CSCs) at the molecular level of intact N-glycopeptides, including comprehensive information (peptide backbones, N-glycosites, N-glycan monosaccharide compositions, and linkage structures) based on a target-decoy theoretical database search strategy and a spectrum-level false discovery rate (FDR) control ≤1%. The existence of Neu5Gc on N-glycan moieties was further confirmed according to its characteristic oxonium fragment ions in the MS/MS spectra of either m/z 308.09816 (Neu5Gc) or 290.08759 (Neu5Gc-H2O). The results are an important addition to previously reported Neu5Ac data and can be further validated with targeted MS methods such as multiple and parallel reaction monitoring and biochemical methods such as immunoassays. This MS-based N-glycoproteomics method can be extended to the discovery and characterization of putative aberrant Neu5Gc in other biological and clinical systems.

Methods to improve quantitative glycoprotein coverage from bottom-up LC-MS data

Advances in mass spectrometry instrumentation, methods development, and bioinformatics have greatly improved the ease and accuracy of site-specific, quantitative glycoproteomics analysis. Data-dependent acquisition is the most popular method for identification and quantification of glycopeptides; however, complete coverage of glycosylation site glycoforms remains elusive with this method. Targeted acquisition methods improve the precision and accuracy of quantification, but at the cost of throughput and discoverability. Data-independent acquisition (DIA) holds great promise for more complete and highly quantitative site-specific glycoproteomics analysis, while maintaining the ability to discover novel glycopeptides without prior knowledge. We review additional features that can be used to increase selectivity and coverage to the DIA workflow: retention time modeling, which would simplify the interpretation of complex tandem mass spectra, and ion mobility separation, which would maximize the sampling of all precursors at a giving chromatographic retention time. The instrumentation and bioinformatics to incorporate these features into glycoproteomics analysis exist. These improvements in quantitative, site-specific analysis will enable researchers to assess glycosylation similarity in related biological systems, answering new questions about the interplay between glycosylation state and biological function.

Lectin Drug Conjugates Targeting High Mannose N-Glycans

Cancer-associated alterations to glycosylation have been shown to aid cancer development and progression. An increased abundance of high mannose N-glycans has been observed in several cancers. Here, we describe the preparation of lectin drug conjugates (LDCs) that permit toxin delivery to cancer cells presenting high mannose N-glycans. Additionally, we demonstrate that cancer cells presenting low levels of high mannose N-glycans can be rendered sensitive to the LDCs by co-treatment with a type I mannosidase inhibitor. Our findings establish that an increased abundance of high mannose N-glycans in the glycocalyx of cancer cells can be leveraged to enable toxin delivery.

Landscape of prognosis and immunotherapy responsiveness under tumor glycosylation-related lncRNA patterns in breast cancer

Aberrant glycosylation, a post-translational modification of proteins, is regarded to engage in tumorigenesis and malignant progression of breast cancer (BC). The altered expression of glycosyltransferases causes abnormal glycan biosynthesis changes, which can serve as diagnostic hallmarks in BC. This study attempts to establish a predictive signature based on glycosyltransferase-related lncRNAs (GT-lncRNAs) in BC prognosis and response to immune checkpoint inhibitors (ICIs) treatment. We firstly screened out characterized glycosyltransferase-related genes (GTGs) through NMF and WGCNA analysis and identified GT-lncRNAs through co-expression analysis. By using the coefficients of 8 GT-lncRNAs, a risk score was calculated and its median value divided BC patients into high- and low-risk groups. The analyses unraveled that patients in the high-risk group had shorter survival and the risk score was an independent predictor of BC prognosis. Besides, the predictive efficacy of our risk score was higher than other published models. Moreover, ESTIMATE analysis, immunophenoscore (IPS), and SubMAP analysis showed that the risk score could stratify patients with distinct immune infiltration, and patients in the high-risk group might benefit more from ICIs treatment. Finally, the vitro assay showed that MIR4435-2HG might promote the proliferation and migration of BC cells, facilitate the polarization of M1 into M2 macrophages, enhance the migration of macrophages and increase the PD-1/PD-L1/CTLA4 expression. Collectively, our well-constructed prognostic signature with GT-lncRNAs had the ability to identify two subtypes with different survival state and responses to immune therapy, which will provide reliable tools for predicting BC outcomes and making rational follow-up strategies.