Expression Patterns of Glycosylation Regulators Define Tumor Microenvironment and Immunotherapy in Gastric Cancer

Abstract

Glycosylation (Glyc) is prevalently related to gastric cancer (GC) pathophysiology. However, studies on the relationship between glycosylation regulators and tumor microenvironment (TME) and immunotherapy of GC remain scarce. We extracted expression data of 1,956 patients with GC from eight cohorts and systematically characterized the glycosylation patterns of six marker genes into phenotype clusters using the unsupervised clustering method. Next, we constructed a Glyc. score to quantify the glycosylation index of each patient with GC. Finally, we analyzed the relationship between Glyc. score and clinical traits including molecular subtype, TME, and immunotherapy of GC. On the basis of prognostic glycosylation-related differentially expressed genes, we constructed the Glyc. score and divided the samples into the high– and low–Glyc. score groups. The high–Glyc. score group showed a poor prognosis and was validated in multiple cohorts. Functional enrichment analysis revealed that the high–Glyc. score group was enriched in metabolism-related pathways. Furthermore, the high–Glyc. score group was associated with the infiltration of immune cells. Importantly, the established Glyc. score would contribute to predicting the response to anti–PD-1/L1 immunotherapy. In conclusion, the Glyc. score is a potentially useful tool to predict the prognosis of GC. Comprehensive analysis of glycosylation may provide novel insights into the epigenetics of GC and improve treatment strategies.

A glycosylation signature for predicting the progression and immunotherapeutic response of prostate cancer

“…The consensus clustering algorithm, an unsupervised clustering method, is commonly used for the interpretation of high-dimensional and large datasets. [25][26][27] By performing consensus clustering analysis, Zhang et al 25 DEG analysis is a commonly used approach for identifying genes whose expression is significantly different between two phenotypes. [25][26][27] In the present study, we found that multiple KEGG pathways were significantly different between the two glycosylation pattern groups.…”

“…[25][26][27] By performing consensus clustering analysis, Zhang et al 25 DEG analysis is a commonly used approach for identifying genes whose expression is significantly different between two phenotypes. [25][26][27] In the present study, we found that multiple KEGG pathways were significantly different between the two glycosylation pattern groups. To further explore the molecular mechanism underlying how the glycosylation pattern influences the prognosis of PCa, we screened the phenotype-related DEGs between the two glycosylation pattern groups and performed DEG analysis.…”

“…The consensus clustering algorithm, an unsupervised clustering method, is commonly used for the interpretation of high‐dimensional and large datasets 25–27 . By performing consensus clustering analysis, Zhang et al 25 revealed three immune phenotypes in gastric cancer, including immune‐excluded, immune‐inflamed and immune‐desert phenotypes.…”

“…Massive amounts of cancer‐related genomic and transcriptomic profile data and their associated clinical data have accumulated in public repositories, such as The Cancer Genome Atlas (TCGA) 25,26 . Based on these advanced techniques and convenient databases, many molecular mechanisms underlying tumor occurrence, progression, and recurrence have been discovered 25–27 . However, to our knowledge, no study has systematically analyzed the expression and mutation profiles of GRgenes in PCa, and the role of glycosylation alteration in the prognostic prediction of PCa has yet to be clarified.…”

“…25,26 Based on these advanced techniques and convenient databases, many molecular mechanisms underlying tumor occurrence, progression, and recurrence have been discovered. [25][26][27] However, to our knowledge, no study has systematically analyzed the expression and mutation profiles of GRgenes in PCa, and the role of glycosylation alteration in the prognostic prediction of PCa has yet to be clarified.…”