Serum N-glycan analysis in breast cancer patients--Relation to tumour biology and clinical outcome

Bayesian inference of sample-specific coexpression networks

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression networks is a critical element of GRN inference, as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate coexpression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. Bayesian optimized networks obtained by assimilating omic data (BONOBO) is a scalable Bayesian model for deriving individual sample-specific coexpression matrices that recognizes variations in molecular interactions across individuals. For each sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific coexpression matrix constructed from all other samples in the data. Combining the sample-specific gene coexpression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific coexpression matrices, thus allowing the analysis of large data sets. We demonstrate BONOBO's utility in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, the prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other methods that have been used for sample-specific coexpression network inference and provides insight into individual differences in the drivers of biological processes.

O-GlycoIsoQuant: A Novel O-Glycome Quantitative Approach through Superbase Release and Isotopic Girard's P Labeling

Quantitative glycosylation analysis serves as an effective tool for detecting changes in glycosylation patterns in cancer and various diseases. However, compared with N-glycans, O-glycans present challenges in both qualitative and quantitative mass spectrometry analysis due to their low abundance, ease of peeling, lack of a universal enzyme, and difficult accessibility. To address this challenge, we developed O-GlycoIsoQuant, a novel O-glycome quantitative approach utilizing superbase release and isotopic Girard's P labeling. This method facilitates rapid and efficient nonreducing β-elimination to dissociate O-glycans from proteins using the organic superbase, 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU), combined with light and heavy isotopic Girard's reagent P (GP) labeling for relative quantification of O-glycans by mass spectrometry. Employing this method, labeled O-glycans exhibit a double peak with a mass difference of 5 Da, suitable for stable relative quantification. The O-GlycoIsoQuant method is characterized by its high labeling efficiency, excellent reproducibility (CV < 20%), and good linearity (R2 > 0.99), across a dynamic range spanning a 100-fold range. This method was applied to various complex sample types, including human serum, porcine spermatozoa, human saliva, and urinary extracellular vesicles, detecting 33, 39, 49, and 37 O-glycans, respectively, thereby demonstrating its broad applicability.

Medical Relevance, State-of-the-Art and Perspectives of "Sweet Metacode" in Liquid Biopsy Approaches

This review briefly introduces readers to an area where glycomics meets modern oncodiagnostics with a focus on the analysis of sialic acid (Neu5Ac)-terminated structures. We present the biochemical perspective of aberrant sialylation during tumourigenesis and its significance, as well as an analytical perspective on the detection of these structures using different approaches for diagnostic and therapeutic purposes. We also provide a comparison to other established liquid biopsy approaches, and we mathematically define an early-stage cancer based on the overall prognosis and effect of these approaches on the patient's quality of life. Finally, some barriers including regulations and quality of clinical validations data are discussed, and a perspective and major challenges in this area are summarised.

Bayesian Optimized sample-specific Networks Obtained By Omics data (BONOBO)

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring co-expression networks is a critical element of GRN inference as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate co-expression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. To address these concerns, we introduce BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data), a scalable Bayesian model for deriving individual sample-specific co-expression networks by recognizing variations in molecular interactions across individuals. For every sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific co-expression matrix constructed from all other samples in the data. Combining the sample-specific gene expression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices, thus making the method extremely scalable. We demonstrate the utility of BONOBO in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other sample-specific co-expression network inference methods and provides insight into individual differences in the drivers of biological processes.

Exploring multisite heterogeneity of human basal cell carcinoma proteome and transcriptome

Basal cell carcinoma (BCC) is the most common type of skin cancer. Due to multiple, potential underlying molecular tumor aberrations, clinical treatment protocols are not well-defined. This study presents multisite molecular heterogeneity profiles of human BCC based on RNA and proteome profiling. Three areas from lesions excised from 9 patients were analyzed. The focus was gene expression profiles based on proteome and RNA measurements of intra-tumor heterogeneity from the same patient and inter-tumor heterogeneity in nodular, infiltrative, and superficial BCC tumor subtypes from different patients. We observed significant overlap in intra- and inter-tumor variability of proteome and RNA expression profiles, showing significant multisite heterogeneity of protein expression in the BCC tumors. Inter-subtype analysis has also identified unique proteins for each BCC subtype. This profiling leads to a deeper understanding of BCC molecular heterogeneity and potentially contributes to developing new sampling tools for personalized diagnostics therapeutic approaches to BCC.

Role of glycosylation in breast cancer progression and metastasis: implications for miRNA, EMT and multidrug resistance

Breast cancer (BC) is one of the leading causes of death in women, globally. A variety of biological processes results in metastasis, a poorly understood pathological phenomenon, causing a high relapse rate. Glycosylation, microribonucleic acids (miRNAs) and epithelial to mesenchymal transition (EMT), have been shown to regulate this cascade where tumor cells detach from their primary site, enter the circulatory system and colonize distant sites. Integrated proteomics and glycomics approaches have been developed to probe the molecular mechanism regulating such metastasis. In this review, we describe specific aspects of glycosylation and its interrelation with miRNAs, EMT and multidrug resistance during BC progression and metastasis. We explore various approaches that determine the role of proteomes and glycosylation in BC diagnosis, therapy and drug discovery.

Not-So-Sweet Dreams: Plasma and IgG N-Glycome in the Severe Form of the Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a prevalent disease associated with increased risk for cardiovascular and metabolic diseases and shortened lifespan. The aim of this study was to explore the possibility of using N-glycome as a biomarker for the severe form of OSA. Seventy subjects who underwent a whole-night polysomnography/polygraphy and had apnea-hypopnea index (AHI) over 30 were compared to 23 controls (AHI under 5). Plasma samples were used to extract 39 glycan peaks using ultra-high-performance liquid chromatography (UPLC) and 27 IgG peaks using capillary gel electrophoresis (CGE). We also measured glycan age, a molecular proxy for biological aging. Three plasma and one IgG peaks were significant in a multivariate model controlling for the effects of age, sex, and body mass index. These included decreased GP24 (disialylated triantennary glycans as major structure) and GP28 (trigalactosylated, triantennary, disialylated, and trisialylated glycans), and increased GP32 (trisialylated triantennary glycan). Only one IgG glycan peak was significantly increased (P26), which contains biantennary digalactosylated glycans with core fucose. Patients with severe OSA exhibited accelerated biological aging, with a median of 6.9 years more than their chronological age (p < 0.001). Plasma N-glycome can be used as a biomarker for severe OSA.

Glycosylation Alterations in Cancer Cells, Prognostic Value of Glycan Biomarkers and Their Potential as Novel Therapeutic Targets in Breast Cancer

Although we are lately witnessing major improvements in breast cancer treatment and patient outcomes, there is still a significant proportion of patients not receiving efficient therapy. More precisely, patients with triple-negative breast cancer or any type of metastatic disease. Currently available prognostic and therapeutic biomarkers are not always applicable and oftentimes lack precision. The science of glycans is a relatively new scientific approach to better characterize malignant transformation and tumor progression. In this review, we summarize the most important information about glycosylation characteristics in breast cancer cells and how different glycoproteins and enzymes involved in glycosylation could serve as more precise biomarkers, as well as new therapeutic targets.

Phosphorylated Proteins from Serum: A Promising Potential Diagnostic Biomarker of Cancer

Cancer is a fatal disease worldwide. Each year ten million people are diagnosed around the world, and more than half of patients eventually die from it in many countries. A majority of cancer remains asymptomatic in the earlier stages, with specific symptoms appearing in the advanced stages when the chances of adequate treatment are low. Cancer screening is generally executed by different imaging techniques like ultrasonography (USG), mammography, CT-scan, and magnetic resonance imaging (MRI). Imaging techniques, however, fail to distinguish between cancerous and non-cancerous cells for early diagnosis. To confirm the imaging result, solid and liquid biopsies are done which have certain limitations such as invasive (in case of solid biopsy) or missed early diagnosis due to extremely low concentrations of circulating tumor DNA (in case of liquid biopsy). Therefore, it is essential to detect certain biomarkers by a noninvasive approach. One approach is a proteomic or glycoproteomic study which mostly identifies proteins and glycoproteins present in tissues and serum. Some of these studies are approved by the Food and Drug Administration (FDA). Another non-expensive and comparatively easier method to detect glycoprotein biomarkers is by ELISA, which uses lectins of diverse specificities. Several of the FDA approved proteins used as cancer biomarkers do not show optimal sensitivities for precise diagnosis of the diseases. In this regard, expression of phosphoproteins is associated with a more specific stage of a particular disease with high sensitivity and specificity. In this review, we discuss the expression of different serum phosphoproteins in various cancers. These phosphoproteins are detected either by phosphoprotein enrichment by immunoprecipitation using phosphospecific antibody and metal oxide affinity chromatography followed by LC-MS/MS or by 2D gel electrophoresis followed by MALDI-ToF/MS analysis. The updated knowledge on phosphorylated proteins in clinical samples from various cancer patients would help to develop these serum phophoproteins as potential diagnostic/prognostic biomarkers of cancer.

Nondestructive protein sampling with electroporation facilitates profiling of spatial differential protein expression in breast tumors in vivo

Excision tissue biopsy, while central to cancer treatment and precision medicine, presents risks to the patient and does not provide a sufficiently broad and faithful representation of the heterogeneity of solid tumors. Here we introduce e-biopsy—a novel concept for molecular profiling of solid tumors using molecular sampling with electroporation. As e-biopsy provides access to the molecular composition of a solid tumor by permeabilization of the cell membrane, it facilitates tumor diagnostics without tissue resection. Furthermore, thanks to its non tissue destructive characteristics, e-biopsy enables probing the solid tumor multiple times in several distinct locations in the same procedure, thereby enabling the spatial profiling of tumor molecular heterogeneity.We demonstrate e-biopsy in vivo, using the 4T1 breast cancer model in mice to assess its performance, as well as the inferred spatial differential protein expression. In particular, we show that proteomic profiles obtained via e-biopsy in vivo distinguish the tumors from healthy breast tissue and reflect spatial tumor differential protein expression. E-biopsy provides a completely new molecular sampling modality for solid tumors molecular cartography, providing information that potentially enables more rapid and sensitive detection at lesser risk, as well as more precise personalized medicine.

Serum N-glycomic profiling may provide potential signatures for surveillance of COVID-19

Disease development and progression are often associated with aberrant glycosylation, indicating that changes in biological fluid glycome may potentially serve as disease signatures. The corona virus disease-2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represents a significant threat to global human health. However, the effect of SARS-CoV-2 infection on the overall serum N-glycomic profile has been largely unexplored. Here, we extended our 96-well-plate-based high-throughput, high-sensitivity N-glycan profiling platform further with the aim of elucidating potential COVID-19-associated serum N-glycomic alterations. Use of this platform revealed both similarities and differences between the serum N-glycomic fingerprints of COVID-19 positive and control cohorts. Although there were no specific glycan peaks exclusively present or absent in COVID-19 positive cohort, this cohort showed significantly higher levels of glycans and variability. On the contrary, the overall N-glycomic profiles for healthy controls were well-contained within a narrow range. From the serum glycomic analysis, we were able to deduce changes in different glycan subclasses sharing certain structural features. Of significance was the hyperbranched and hypersialylated glycans and their derived glycan subclass traits. T-distributed stochastic neighbour embedding (tSNE) and hierarchical heatmap clustering analysis were performed to identify 13 serum glycomic variables that potentially distinguished the COVID-19 positive from healthy controls. Such serum N-glycomic changes described herein may indicate or correlate to the changes in serum glycoproteins upon COVID-19 infection. Furthermore, mapping the serum N-glycome following SARS-CoV-2 infection may help us better understand the disease and enable "Long-COVID" surveillance to capture the full spectrum of persistent symptoms.

High-Mannose N-Glycans as Malignant Progression Markers in Early-Stage Colorectal Cancer

The increase incidence of early colorectal cancer (T1 CRC) last years is mainly due to the introduction of population-based screening for CRC. T1 CRC staging based on histological criteria remains challenging and there is high variability among pathologists in the scoring of these criteria. It is crucial to unravel the biology behind the progression of adenoma into T1 CRC. Glycomic studies have reported extensively on alterations of the N-glycomic pattern in CRC; therefore, investigating these alterations may reveal new insights into the development of T1 CRC. We used matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging (MSI) to spatially profile the N-glycan species in a cohort of pT1 CRC using archival formalin-fixed and paraffin-embedded (FFPE) material. To generate structural information on the observed N-glycans, CE-ESI-MS/MS was used in conjunction with MALDI-MSI. Relative intensities and glycosylation traits were calculated based on a panel of 58 N-glycans. Our analysis showed pronounced differences between normal epithelium, dysplastic, and carcinoma regions. High-mannose-type N-glycans were higher in the dysplastic region than in carcinoma, which correlates to increased proliferation of the cells. We observed changes in the cancer invasive front, including higher expression of α2,3-linked sialic acids which followed the glycosylation pattern of the carcinoma region.

Construction of InstantPC derivatized glycan GU database: A foundation work for high-throughput and high-sensitivity glycomic analysis

Glycosylation is well-recognized as a critical quality attribute of biotherapeutics being routinely monitored to ensure desired product quality, safety, and efficacy. Additionally, as one of the most prominent and complex post-translational modifications, glycosylation plays a key role in disease manifestation. Changes in glycosylation may serve as a specific and sensitive biomarker for disease diagnostics and prognostics. However, the conventional 2-aminobenzamide based N-glycosylation analysis procedure is time-consuming and insensitive, with poor reproducibility. We have evaluated an innovative streamlined 96-well-plate-based platform utilizing InstantPC label for high-throughput, high-sensitivity glycan profiling, which is user-friendly, robust, and ready for automation. However, the limited availability of InstantPC labelled glycan standards has significantly hampered the applicability and transferability of this platform for expedited glycan structural profiling. To address this challenge, we have constructed a detailed InstantPC labelled glycan glucose unit database through analysis of human serum and a variety of other glycoproteins from various sources. Following preliminary hydrophilic interaction liquid chromatography with fluorescence detection separation and analysis, glycoproteins with complex glycan profiles were subjected to further fractionation by weak anion exchange hydrophilic interaction liquid chromatography and exoglycosidase sequential digestion for cross-validation of the glycan assignment. Hydrophilic interaction ultra-performance liquid chromatography coupled with electrospray ionization mass spectrometry was subsequently utilised for glycan fragmentation and accurate glycan mass confirmation. The constructed InstantPC glycan GU database is accurate and robust. It is believed that this database will enhance the application of the developed platform for high-throughput, high-sensitivity glycan profiling, and eventually advance glycan-based biopharmaceutical production and disease biomarker discovery.

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data

Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.

N-Glycomic Signature of Stage II Colorectal Cancer and Its Association With the Tumor Microenvironment

The choice for adjuvant chemotherapy in stage II colorectal cancer is controversial as many patients are cured by surgery alone and it is difficult to identify patients with high risk of recurrence of the disease. There is a need for better stratification of this group of patients. Mass spectrometry imaging could identify patients at risk. We report here the N-glycosylation signatures of the different cell populations in a group of stage II colorectal cancer tissue samples. The cancer cells, compared with normal epithelial cells, have increased levels of sialylation and high-mannose glycans, as well as decreased levels of fucosylation and highly branched N-glycans. When looking at the interface between cancer and its microenvironment, it seems that the cancer N-glycosylation signature spreads into the surrounding stroma at the invasive front of the tumor. This finding was more outspoken in patients with a worse outcome within this sample group.

A nomogram based on glycomic biomarkers in serum and clinicopathological characteristics for evaluating the risk of peritoneal metastasis in gastric cancer

Background

Peritoneal metastasis (PM) in gastric cancer (GC) remains an untreatable disease, and is difficult to diagnose preoperatively. Here, we aim to establish a novel prediction model.

Methods

The clinicopathologic characteristics of a cohort that included 86 non-metastatic GC patients and 43 PMGC patients from Zhongshan Hospital were retrospectively analysed to identify PM associated variables. Additionally, mass spectrometry and glycomic analysis were applied in the same cohort to find glycomic biomarkers in serum for the diagnosis of PM. A nomogram was established based on the associations between potential risk variables and PM.

Results

Overexpression of 4 N-glycans (H6N5L1E1: m/z 2620.93; H5N5F1E2: m/z 2650.98; H6N5E2, m/z 2666.96; H6N5L1E2, m/z 2940.08); weight loss ≥ 5 kg; tumour size ≥ 3 cm; signet ring cell or mucinous adenocarcinoma histology type; poor differentiation; diffuse or mixed Lauren classification; increased CA19-9, CA125, and CA724 levels; decreased lymphocyte count, haemoglobin, albumin, and pre-albumin levels were identified to be associated with PM. A nomogram that integrated with five independent risk factors (weight loss ≥ 5 kg, CA19-9 ≥ 37 U/mL, CA125 ≥ 35 U/mL, lymphocyte count < 2.0 * 10 ~ 9/L, and H5N5F1E2 expression ≥ 0.0017) achieved a good performance for diagnosis (AUC: 0.892, 95% CI 0.829–0.954). When 160 was set as the cut-off threshold value, the proposed nomogram represented a perfectly discriminating power for both sensitivity (0.97) and specificity (0.88).

Conclusions

The nomogram achieved an individualized assessment of the risk of PM in GC patients; thus, the nomogram could be used to assist clinical decision-making before surgery.

Breast cancer diagnosis by analysis of serum N-glycans using MALDI-TOF mass spectroscopy

Blood and serum N-glycans can be used as markers for cancer diagnosis, as alterations in protein glycosylation are associated with cancer pathogenesis and progression. We aimed to develop a platform for breast cancer (BrC) diagnosis based on serum N-glycan profiles using MALDI-TOF mass spectroscopy. Serum N-glycans from BrC patients and healthy volunteers were evaluated using NosQuest’s software “NosIDsys.” BrC-associated “NosID” N-glycan biomarkers were selected based on abundance and NosIDsys analysis, and their diagnostic potential was determined using NosIDsys and receiver operating characteristic curves. Results showed an efficient pattern recognition of invasive ductal carcinoma patients, with very high diagnostic performance [area under the curve (AUC): 0.93 and 95% confidence interval (CI): 0.917–0.947]. We achieved effective stage-specific differentiation of BrC patients from healthy controls with 82.3% specificity, 84.1% sensitivity, and 82.8% accuracy for stage 1 BrC and recognized hormone receptor-2 and lymph node invasion subtypes based on N-glycan profiles. Our novel technique supplements conventional diagnostic strategies for BrC detection and can be developed as an independent platform for BrC screening.

Molecular harvesting with electroporation for tissue profiling

Recent developments in personalized medicine are based on molecular measurement steps that guide personally adjusted medical decisions. A central approach to molecular profiling consists of measuring DNA, RNA, and/or proteins in tissue samples, most notably in and around tumors. This measurement yields molecular biomarkers that are potentially predictive of response and of tumor type. Current methods in cancer therapy mostly use tissue biopsy as the starting point of molecular profiling. Tissue biopsies involve a physical resection of a small tissue sample, leading to localized tissue injury, bleeding, inflammation and stress, as well as to an increased risk of metastasis. Here we developed a technology for harvesting biomolecules from tissues using electroporation. We show that tissue electroporation, achieved using a combination of high-voltage short pulses, 50 pulses 500 V cm-1, 30 µs, 1 Hz, with low-voltage long pulses 50 pulses 50 V cm-1, 10 ms, delivered at 1 Hz, allows for tissue-specific extraction of RNA and proteins. We specifically tested RNA and protein extraction from excised kidney and liver samples and from excised HepG2 tumors in mice. Further in vivo development of extraction methods based on electroporation can drive novel approaches to the molecular profiling of tumors and of tumor environment and to related diagnosis practices.

Exploring serum and immunoglobulin G N-glycome as diagnostic biomarkers for early detection of breast cancer in Ethiopian women

BACKGROUND

Alterations in protein glycosylation patterns have potentially been targeted for biomarker discovery in a wide range of diseases including cancer. Although there have been improvements in patient diagnosis and survival for breast cancer (BC), there is no clinically validated serum biomarker for its early diagnosis. Here, we profiled whole serum and purified Immunoglobulin G (IgG) fraction N-glycome towards identification of non-invasive glycan markers of BC.

METHODS

We employed a comprehensive glycomics approach by integrating glycoblotting-based glycan purification with MALDI-TOF/MS based quantitative analysis. Sera of BC patients belonging to stages I-IV and normal controls (NC) were collected from Ethiopian women during 2015-2016. IgG was purified by affinity chromatography using protein G spin plate and further subjected to glycoblotting for glycan release. Mass spectral data were further processed and evaluated rigorously, using various bioinformatics and statistical tools.

RESULTS

Out of 35 N-glycans that were significantly up-regulated in the sera of all BC patients compared to the NC, 17 complex type N-glycans showed profound expression abundance and diagnostic potential (AUC = 0.8-1) for the early stage (I and II) BC patients. Most of these glycans were core-fucosylated, multiply branched and sialylated structures, whose abundance has been strongly associated with greater invasive and metastatic potential of cancer. N-glycans quantified form IgG confirmed their abundance in BC patients, of which two core-fucosylated and agalactosylated glycans (m/z 1591, 1794) could specifically distinguish (AUC = 0.944 and 0.921, p ≤ 0.001) stage II patients from NC. Abundance of such structural features in IgG is associated with a decrease in its immunosuppressive potential towards tumor cells, which in part may correlate with the aggressive nature of BC commonly noticed in black population.

CONCLUSIONS

Our comprehensive study has addressed for the first time both whole serum and IgG N-glycosylation signatures of native black women suffering from BC and revealed novel glyco-biomarkers with marked overexpression and distinguishing ability at early stage patients. Further studies on direct identification of the intact glycoproteins using a glycoprteomics approach will provide a deeper understanding of specific biomarkers towards their clinical utility.

Sialic Acid Linkage Analysis Refines the Diagnosis of Ovarian Cancer

Epithelial ovarian cancer (EOC) is a rather rare but lethal disease that is usually diagnosed at an advanced stage; this is due to a lack of early diagnostic markers. At the time being, less than a quarter of patients are diagnosed when the tumor has not metastasized yet. In previous work, we demonstrated that antennarity, fucosylation, and sialylation increased in EOC patients and built a glycan-based score that was able to diagnose EOC better than CA125, the routine diagnostic marker, does. To date, little attention had been paid to the sialic acid linkages of N-glycans in the context of blood biomarker research. In this work, the sialic acid linkages of the serum glycome of ovarian cancer patients were investigated for the first time by MALDI-TOF-MS. To this end, we released N-glycans, derivatized sialic acids solely in a linkage-specific way and measured glycome profiles by MALDI-TOF mass spectrometry. A statistically significant decrease was observed between late stage patients and controls or early stage patients for high-mannose, hybrid-type, complex-type asialylated, bi, tri- and tetraantennary sialylated structures. A significant decrease of monosialylated monoantennary N-glycan structures was observed in early and late stage EOC when compared to healthy controls. Statistically significant increases were observed in early and late stage patients compared to controls for tri, tetraantennary fucosylated structures, afucosylated, and fucosylated triantennary structures taken as α-2,3-linked/α-2,6-linked sialic acid ratio. Moreover, all afucosylated and fucosylated structures taken as α-2,3-linked/α-2,6-linked sialic acid ratio and the α-2,3-linked/α-2,6-linked sialic acid ratio of all sialylated structures were increased significantly for early and late stage EOC patients when compared to healthy controls. Finally, ROC curves were built for the most significant glycan combinations and we were able to show that the serum glycome sialic acid ratio could enhance ovarian cancer diagnosis as sialic acid linkage modulations arise even in early stage ovarian cancer.

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.

Preparation of a thickness-controlled Mg-MOFs-based magnetic graphene composite as a novel hydrophilic matrix for the effective identification of the glycopeptide in the human urine

The highly effective analysis of glycopeptides from complex biological samples is an attractive and critical topic all the time. In this study, a novel thickness-controlled hydrophilic Mg-metal organic frameworks (Mg-MOFs) coating-functionalized magnetic graphene composite (MagG@Mg-MOFs-1C) was prepared for the capture of the glycopeptides. The as-synthesized composite exhibits an ultralow limit of detection (0.1 fmol μL-1), a perfect size-exclusion effect (HRP digests/BSA protein/HRP protein, 1 : 500 : 500, w/w/w), and a high binding capacity (150 mg g-1), satisfying reusability and high recovery in the recognition of glycopeptides due to its outstanding characteristics including strong magnetic property, large surface area (617 m2 g-1), plenty of affinity sites, and excellent hydrophilicity. Furthermore, the MagG@Mg-MOFs-1C composite was successfully applied to selectively enriched glycopeptides in human urine. More excitingly, 406 N-glycosylation peptides corresponding to 185 glycoproteins were identified in the urine of the bladder cancer patients, in which these identified glycoproteins include the potential biomarkers (α-2-macroglobulin, complement C4-B, and α-1-antitrypsin) for the bladder cancer. This study suggests that the hydrophilic porous MOFs-functionalized composite has a great potential in the large-scale characterization of the low-abundance biomolecules in urine, opening a new avenue for the rapid and convenient diagnosis of the disease.

Serum IgA1 shows increased levels of α2,6-linked sialic acid in breast cancer

The lectin Helix pomatia agglutinin (HPA) recognizes altered glycosylation in solid cancers and the identification of HPA binding partners in tumour tissue and serum is an important aim. Among the many HPA binding proteins, IgA1 has been reported to be the most abundant in liver metastases. In this study, the glycosylation of IgA1 was evaluated using serum samples from patients with breast cancer (BCa) and the utility of IgA1 glycosylation as a biomarker was assessed. Detailed mass spectrometric structural analysis showed an increase in disialo-biantennary N-linked glycans on IgA1 from BCa patients (p < 0.0001: non-core fucosylated; p = 0.0345: core fucosylated) and increased asialo-Thomsen-Friedenreich antigen (TF) and disialo-TF antigens in the O-linked glycan preparations from IgA1 of cancer patients compared with healthy control individuals. An increase in Sambucus nigra binding was observed, suggestive of increased α2,6-linked sialic acid on IgA1 in BCa. Logistic regression analysis showed HPA binding to IgA1 and tumour size to be significant independent predictors of distant metastases (χ ² 13.359; n = 114; p = 0.020) with positive and negative predictive values of 65.7% and 64.6%, respectively. Immunohistochemical analysis of tumour tissue samples showed IgA1 to be detectable in BCa tissue. This report provides a detailed analysis of serum IgA1 glycosylation in BCa and illustrates the potential utility of IgA1 glycosylation as a biomarker for BCa prognostication.

Glycosylation and metastases

The change of cellular glycosylation is one of the key events in malignant transformation and neoplastic progression, and tumor-related glycosylation alterations are promising targets in both tumor diagnosis and therapy. Both malignant transformation and neoplastic progression are the consequence of gene expression alterations and alterations in protein expression. Micro environmental factors such as extracellular matrix (ECM) also play an important role in their growth and metastasis. Tumor-associated glycans are important biomarker candidates for cancer diagnosis and prognosis, and analytical methods for their detection were developed recently. Glycoproteomics that use mass spectrometry for identification of cancer antigens and structural analysis of glycans play a key role in the investigation of changes of glycosylation during malignant transformation and tumor development and metastasis. Deep understanding of glycan remodeling in cancer and the role of glycosyltransferases that are involved in this process will require a detailed profiling of glycosylation patterns of tumor cells, and corresponding analytical methods for their detection were developed.

Salivary Glycopatterns as Potential Biomarkers for Screening of Early-Stage Breast Cancer

OBJECTIVE

We systematically investigated and assessed the alterations of salivary glycopatterns and possibility as biomarkers for diagnosis of early-stage breast cancer.

DESIGN

Alterations of salivary glycopatterns were probed using lectin microarrays and blotting analysis from 337 patients with breast benign cyst or tumor (BB) or breast cancer (I/II stage) and 110 healthy humans. Their diagnostic models were constructed by a logistic stepwise regression in the retrospective cohort. Then, the performance of the diagnostic models were assessed by ROC analysis in the validation cohort. Finally, a double-blind cohort was tested to confirm the application potential of the diagnostic models.

RESULTS

The diagnostic models were constructed based on 9 candidate lectins (e.g., PHA-E+L, BS-I, and NPA) that exhibited significant alterations of salivary glycopatterns, which achieved better diagnostic powers with an AUC value >0.750 (p<0.001) for the diagnosis of BB (AUC: 0.752, sensitivity: 0.600, and specificity: 0.835) and I stage breast cancer (AUC: 0.755, sensitivity: 0.733, and specificity: 0.742) in the validation cohort. The diagnostic model of I stage breast cancer exhibited a high accuracy of 0.902 in double-blind cohort.

CONCLUSIONS

This study could contribute to the screening for patients with early-stage breast cancer based on precise alterations of salivary glycopatterns.

Serum N-glycome alterations in breast cancer during multimodal treatment and follow-up

Using our recently developed high-throughput automated platform, N-glycans from all serum glycoproteins from patients with breast cancer were analysed at diagnosis, after neoadjuvant chemotherapy, surgery, radiotherapy and up to 3 years after surgery. Surprisingly, alterations in the serum N-glycome after chemotherapy were pro-inflammatory with an increase in glycan structures associated with cancer. Surgery, on the other hand, induced anti-inflammatory changes in the serum N-glycome, towards a noncancerous phenotype. At the time of first follow-up, glycosylation in patients with affected lymph nodes changed towards a malignant phenotype. C-reactive protein showed a different pattern, increasing after first line of neoadjuvant chemotherapy, then decreasing throughout treatment until 1 year after surgery. This may reflect a switch from acute to chronic inflammation, where chronic inflammation is reflected in the serum after the acute phase response subsides. In conclusion, we here present the first time-course serum N-glycome profiling of patients with breast cancer during and after treatment. We identify significant glycosylation changes with chemotherapy, surgery and follow-up, reflecting the host response to therapy and tumour removal.

Metallic Element Chelated Tag Labeling (MeCTL) for Quantitation of N-Glycans in MALDI-MS

N-glycosylation plays an important role in chief biological and pathological processes. Quantifying the N-glycan is important since glycan alterations are related to many diseases. In this study, we developed a novel N-glycan quantitation approach using metallic element chelated tag labeling (MeCTL) through reductive amination. The MeCTL strategy is of high labeling efficiency and accurate in quantitation with high reproducibility (CV < 17.03%) and good linearity (R² > 0.99) within 2 orders of magnitude of dynamic range. Additionally, it provides significant cross-ring fragmentation to distinguish N-glycan isomers. Furthermore, multiplex quantitation by chelation with several different rare earth elements can be achieved. At last, this strategy has been successfully used for evaluation of N-glycan changes in human serum associated with CRC, indicating its potential in clinical applications including disease N-glycome profiling and relative quantitation.

Epigenetic regulation of glycosylation and the impact on chemo-resistance in breast and ovarian cancer

Glycosylation is one of the most fundamental posttranslational modifications in cellular biology and has been shown to be epigenetically regulated. Understanding this process is important as epigenetic therapies such as those using DNA methyltransferase inhibitors are undergoing clinical trials for the treatment of ovarian and breast cancer. Previous work has demonstrated that altered glycosylation patterns are associated with aggressive disease in women presenting with breast and ovarian cancer. Moreover, the tumor microenvironment of hypoxia results in globally altered DNA methylation and is associated with aggressive cancer phenotypes and chemo-resistance, a feature integral to many cancers. There is sparse knowledge on the impact of these therapies on glycosylation. Moreover, little is known about the efficacy of DNA methyltransferase inhibitors in hypoxic tumors. In this review, we interrogate the impact that hypoxia and epigenetic regulation has on cancer cell glycosylation in relation to resultant tumor cell aggressiveness and chemo-resistance.

UniCarbKB: New database features for integrating glycan structure abundance, compositional glycoproteomics data, and disease associations

BACKGROUND

UniCarbKB aims to provide a resource for the representation of mammalian glycobiology knowledge by providing a curated database of structural and experimental data, supported by a web application that allows users to easily find and view richly annotated information. The database comprises two levels of annotation (i) global-specific data of oligosaccharides released and characterised from single purified glycoproteins and (ii) information pertaining to site-specific glycan heterogeneity. Additional, contextual information is provided including structural, bibliographic, and taxonomic information for each entry.

METHODS

Since the launch of UniCarbKB in 2012, we have continued to improve the organisation of our data model. Recently, we have extended our pipeline to collate structural and abundance changes of oligosaccharides in different human disease states and experimental models to extend our coverage of the human glycome.

RESULTS

In this manuscript, we demonstrate the capability of UniCarbKB to store and query relative glycan abundance data using a set of published colorectal and prostate cancer cell lines as examples. Furthermore, we outline our strategy for managing large-scale glycoproteomics data, site-specific and glycan compositional data, and how this information is adding value to UniCarbKB. Finally, we summarise our efforts to improve the efficient representation of disease terms and associated changes in glycan heterogeneity by integrating the Disease Ontology.

CONCLUSIONS

Updates and improvements to UniCarbKB have introduced unique features for storing and displaying glycosylation features of mammalian glycoproteins. The integration of site-specific glycosylation data obtained from large-scale glycoproteomics and introduction of cell line studies will improve the analysis of glycoproteins and entire glycomes.

GENERAL SIGNIFICANCE

Continuing advancements in analytical technologies and new data types are advancing disease-related glycomics. It is increasingly necessary to ensure all the data are comprehensively annotated. UniCarbKB was established with the mission of providing a resource for human glycobiology by capturing a wide range of data with corresponding annotations. This article is part of a Special Issue entitled "Glycans in personalised medicine" Guest Editor: Professor Gordan Lauc.

Serum N-glycan analysis in breast cancer patients--Relation to tumour biology and clinical outcome

Glycosylation and related processes play important roles in cancer development and progression, including metastasis. Several studies have shown that N-glycans have potential diagnostic value as cancer serum biomarkers. We have explored the significance of the abundance of particular serum N-glycan structures as important features of breast tumour biology by studying the serum glycome and tumour transcriptome (mRNA and miRNA) of 104 breast cancer patients. Integration of these types of molecular data allows us to study the relationship between serum glycans and transcripts representing functional pathways, such as metabolic pathways or DNA damage response. We identified tri antennary trigalactosylated trisialylated glycans in serum as being associated with lower levels of tumour transcripts involved in focal adhesion and integrin-mediated cell adhesion. These glycan structures were also linked to poor prognosis in patients with ER negative tumours. High abundance of simple monoantennary glycan structures were associated with increased survival, particularly in the basal-like subgroup. The presence of circulating tumour cells was found to be significantly associated with several serum glycome structures like bi and triantennary, di- and trigalactosylated, di- and trisialylated. The link between tumour miRNA expression levels and N-glycan production is also examined.