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

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022

The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.

High-throughput glycosaminoglycan extraction and UHPLC-MS/MS quantification in human biofluids

Glycosaminoglycans (GAGs) are linear, unbranched heteropolysaccharides whose structural complexity determines their function. Accurate quantification of GAGs in biofluids at high throughput is relevant for numerous biomedical applications. However, because of the structural variability of GAGs in biofluids, existing protocols require complex pre-analytical procedures, have limited throughput and lack accuracy. Here, we describe the extraction and quantification of GAGs by using ultra-high-performance liquid chromatography coupled with triple-quadrupole mass spectrometry (UHPLC-MS/MS). Designed for 96-well plates, this method enables the processing of up to 82 study samples per plate, with the remaining 14 wells used for calibrators and controls. Key steps include the enzymatic depolymerization of GAGs, their derivatization with 2-aminoacridone and their quantification via UHPLC-MS/MS. Each plate can be analyzed in a single UHPLC-MS/MS run, offering the quantitative and scalable analysis of 17 disaccharides from chondroitin sulfate, heparan sulfate and hyaluronic acid, with a level of precision and reproducibility sufficient for their use as biomarkers. The procedure from sample thawing to initiating the UHPLC-MS/MS run can be completed in ~1.5 d plus 15 min of MS runtime per sample, and it is structured to fit within ordinary working shifts, thus making it a valuable tool for clinical laboratories seeking high-throughput analysis of GAGs. The protocol requires expertise in UHPLC-MS/MS.

Nitrogen-modified reduced graphene oxide for serum enrichment of N-glycans and MALDI-TOF MS-based identification of HCC biomarkers

Protein N-glycosylation, as one of the most crucial post-translational modifications, plays a significant role in various biological processes. The structural alterations of N-glycans are closely associated with the onset and progression of numerous diseases. Therefore, the precise and specific identification of disease-related N-glycans in complex biological samples is invaluable for understanding their involvement in physiological and pathological processes, as well as for discovering clinical diagnostic biomarkers. However, protein N-glycosylation suffers from microscopic heterogeneity and low abundance in biological systems, leading to N-glycopeptide signals being overshadowed by those of their non-glycosylated counterparts during mass spectrometry (MS) analysis. Consequently, there is an urgent demand for the development of novel methods for highly efficient N-glycan enrichment. In this study, we introduced a novel hydrophilic nanomaterial, nitrogen-modified reduced graphene oxide (N-rGO), tailored for this purpose, which was formed by a condensation reaction between the amino groups of rGO and the carboxyl groups of Fmoc-Photo-Linker. Compared to other enrichment materials, N-rGO not only supports efficient N-glycans enrichment via hydrophilic interaction (HILIC), but also serves as an effective matrix for direct MALDI-TOF MS analysis combined with DHB, thereby avoiding sample loss during N-glycans release. 76 and 81 serum N-glycans were obtained from 3 healthy individuals and 3 hepatocellular carcinoma (HCC) patients. Notably, relative quantification of serum N-glycans between 20 patients and 20 healthy controls showed significant expression differences, such as H5N4F1S1, H6N5F1, H5N4S2, H5N4F2S1 and H5N5F1S1, indicating the potential of N-rGO for biomarker discovery.

Emerging methods and techniques for cancer biomarker discovery

Modern cancer research depends heavily on the identification and validation of biomarkers because they provide important information about the diagnosis, prognosis, and response to treatment of the cancer. This review will provide a comprehensive overview of cancer biomarkers, including their development phases and recent breakthroughs in transcriptomics and computational techniques for detecting these biomarkers. Blood-based biomarkers have great potential for non-invasive tumor dynamics and treatment response monitoring. These include circulating tumor DNA, exosomes, and microRNAs. Comprehensive molecular profiles are provided by multi-omic technologies, which combine proteomics, metabolomics, and genomes to support the identification of biomarkers and the targeting of therapeutic interventions. Genetic changes are detected by next-generation sequencing, and patterns of protein expression are found by protein arrays and mass spectrometry. Tumor heterogeneity and clonal evolution can be understood using metabolic profiling and single-cell studies. It is projected that the use of several biomarkers-genetic, protein, mRNA, microRNA, and DNA profiles, among others-will rise, enabling multi-biomarker analysis and improving individualised treatment plans. Biomarker identification and patient outcome prediction are further improved by developments in AI algorithms and imaging techniques. Robust biomarker validation and reproducibility require cooperation between industry, academia, and doctors. Biomarkers can provide individualized care, meet unmet clinical needs, and enhance patient outcomes despite some obstacles. Precision medicine will continue to take shape as scientific research advances and the integration of biomarkers with cutting-edge technologies continues to offer a more promising future for personalized cancer care.

Glycosylation: mechanisms, biological functions and clinical implications

Protein post-translational modification (PTM) is a covalent process that occurs in proteins during or after translation through the addition or removal of one or more functional groups, and has a profound effect on protein function. Glycosylation is one of the most common PTMs, in which polysaccharides are transferred to specific amino acid residues in proteins by glycosyltransferases. A growing body of evidence suggests that glycosylation is essential for the unfolding of various functional activities in organisms, such as playing a key role in the regulation of protein function, cell adhesion and immune escape. Aberrant glycosylation is also closely associated with the development of various diseases. Abnormal glycosylation patterns are closely linked to the emergence of various health conditions, including cancer, inflammation, autoimmune disorders, and several other diseases. However, the underlying composition and structure of the glycosylated residues have not been determined. It is imperative to fully understand the internal structure and differential expression of glycosylation, and to incorporate advanced detection technologies to keep the knowledge advancing. Investigations on the clinical applications of glycosylation focused on sensitive and promising biomarkers, development of more effective small molecule targeted drugs and emerging vaccines. These studies provide a new area for novel therapeutic strategies based on glycosylation.

Clinical relevance of glycosylation in triple negative breast cancer: a review

Glycosylation alterations in TNBC have significant implications for tumor behavior, diagnosis, prognosis, and therapeutic strategies. Dysregulated glycosylation affects cell adhesion, signaling, immune recognition, and response to therapy in TNBC. Different types of glycosylation, including N-linked glycosylation, O-linked glycosylation, glycosphingolipid glycosylation, mucin-type glycosylation, and sialylation, play distinct roles in TNBC. The "barcoding" method based on glycosylation sites of the membrane type mannose receptor (MR) shows promise in accurately distinguishing breast cancer subtypes, including TNBC. Alpha-L-fucosidase 1 (FUCA1) and Monocarboxylate transporter 4 (MCT4) have been identified as potential diagnostic and prognostic markers for TNBC. The glycosylation status of PD-L1 impacts the response to immune checkpoint blockade therapy in TNBC. Inhibiting fucosylation of B7H3 enhances immune responses and improves anti-tumor effects. Targeting glycosylated B7H4 and modulating estrogen metabolism through glycosylation-related mechanisms are potential therapeutic strategies for TNBC. Understanding the role of glycosylation in TNBC provides insights into disease mechanisms, diagnosis, and potential therapeutic targets. Further research in this field may lead to personalized treatment approaches and improved outcomes for TNBC patients.

A novel PD-1/PD-L1 pathway-related seven-gene signature for the development and validation of the prognosis prediction model for breast cancer

Background

Breast cancer (BC/BRCA) is the most common carcinoma in women. The average 5-year survival rate of BC patients with stage IV disease is 26%. A considerable proportion of patients still do not receive effective therapy. It is an unmet need to identify novel biomarkers for BC patients. Herein, we evaluated whether the programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) status is associated with the clinical outcomes of BC, based on data from The Cancer Genome Atlas (TCGA).

Methods

Clinical and transcriptome data of BC patients were obtained from TCGA dataset, and prognostic genes in BC patients were identified, as well as the PD-1/PD-L1 pathway mainly associating with the BC patients. Following the execution of the consensus clustering algorithm, BC patients were segregated into two clusters, and subsequent investigation of the potential mechanisms between them was carried out. A comparison of ferroptosis and N6-methyladenosine (m6A) was conducted between the two groups with the greatest difference in prognosis. Based on least absolute shrinkage and selection operator (LASSO) analysis, a signature associated with the PD-1/PD-L1 pathway was developed, and the prognosis outcome and the predictive accuracy of the signature model were further assessed.

Results

Prognostic genes in BC patients were studied using TCGA data and it was found that the PD-1/PD-L1 pathway was most associated with the BC patients. Then, a low-risk (C1) group and a high-risk (C2) group of BC patients were constructed based on a PD-1/PD-L1 pathway-related signature. The functional analyses suggested that the underlying mechanisms between these groups were mainly associated with immune-related pathways. We found that ferroptosis and m6A were significantly different between the two groups. A PD-1/PD-L1 pathway-related gene signature was further developed to predict survival of BC patients, including 7 genes [mitogen-activated protein kinase kinase 6 (MAP2K6), NF-kappa-B inhibitor alpha (NFKBIA), NFKB Inhibitor Epsilon (NFKBIE), Interferon gamma (IFNG), Toll/interleukin-1 receptor domain-containing adapter protein (TIRAP), IkappaB kinase (CHUK), and Casein kinase 2 alpha 3 gene (CSNK2A3)]. The receiver operating characteristic (ROC) curves were analyzed to further assess the prognostic values of these 7 genes. The 1-, 3-, and 5-year values of the areas under the curve (AUCs) for overall survival were 0.651, 0.658, and 0.653 in this seven gene signature model, respectively.

Conclusions

PD-1/PD-L1 pathway-related subtypes of BC were identified, which were closely associated with the immune microenvironment, the ferroptosis status, and m6A in BC patients. The gene signature involved in the PD-1/PD-L1 pathway might help to make a distinction and predict prognosis in BC patients.

N-glycan profiling of tissue samples to aid breast cancer subtyping

Breast cancer is a highly heterogeneous disease. Its intrinsic subtype classification for diagnosis and choice of therapy traditionally relies on the presence of characteristic receptors. Unfortunately, this classification is often not sufficient for precise prediction of disease prognosis and treatment efficacy. The N-glycan profiles of 145 tumors and 10 healthy breast tissues were determined using Matrix-Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry. The tumor samples were classified into Mucinous, Lobular, No-Special-Type, Human Epidermal Growth Factor 2 + , and Triple-Negative Breast Cancer subtypes. Statistical analysis was conducted using the reproducibility-optimized test statistic software package in R, and the Wilcoxon rank sum test with continuity correction. In total, 92 N-glycans were detected and quantified, with 59 consistently observed in over half of the samples. Significant variations in N-glycan signals were found among subtypes. Mucinous tumor samples exhibited the most distinct changes, with 28 significantly altered N-glycan signals. Increased levels of tri- and tetra-antennary N-glycans were notably present in this subtype. Triple-Negative Breast Cancer showed more N-glycans with additional mannose units, a factor associated with cancer progression. Individual N-glycans differentiated Human Epidermal Growth Factor 2 + , No-Special-Type, and Lobular cancers, whereas lower fucosylation and branching levels were found in N-glycans significantly increased in Luminal subtypes (Lobular and No-Special-Type tumors). Clinically normal breast tissues featured a higher abundance of signals corresponding to N-glycans with bisecting moiety. This research confirms that histologically distinct breast cancer subtypes have a quantitatively unique set of N-glycans linked to clinical parameters like tumor size, proliferative rate, lymphovascular invasion, and metastases to lymph nodes. The presented results provide novel information that N-glycan profiling could accurately classify human breast cancer samples, offer stratification of patients, and ongoing disease monitoring.

Potassium Channels, Glucose Metabolism and Glycosylation in Cancer Cells

Potassium channels emerge as one of the crucial groups of proteins that shape the biology of cancer cells. Their involvement in processes like cell growth, migration, or electric signaling, seems obvious. However, the relationship between the function of K+ channels, glucose metabolism, and cancer glycome appears much more intriguing. Among the typical hallmarks of cancer, one can mention the switch to aerobic glycolysis as the most favorable mechanism for glucose metabolism and glycome alterations. This review outlines the interconnections between the expression and activity of potassium channels, carbohydrate metabolism, and altered glycosylation in cancer cells, which have not been broadly discussed in the literature hitherto. Moreover, we propose the potential mediators for the described relations (e.g., enzymes, microRNAs) and the novel promising directions (e.g., glycans-orinented drugs) for further research.