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.

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.