Abstract
Background
Gastric cancer (GC) is one of the leading causes of cancer-related death. Glycolysis plays a pivotal role in tumor microenvironment (TME) reprogramming. This study assessed the roles of glycolysis-related genes (GRGs) in predicting prognosis and indicating the immune microenvironment features in gastric cancer patients.
Methods
Gene expression data and clinical data of GC patients were obtained from The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort and validated using datasets acquired from the Gene Expression Omnibus (GEO). A total of 326 GRGs were identified from the Molecular Signatures Database (MSigDB). Subtypes of GC were delineated via consensus clustering based on GRG expression. A multigene risk score model was developed using multivariate Cox regression analysis. The CIBERSORT and ESTIMATE algorithms were used to evaluate the immune microenvironment. To probe the biological function of critical genes, wound healing assays, transwell invasion assays, and MTT assays were used.
Results
The patients were divided into two groups, namely, the metabolic subtype (cluster A) and immune subtype (cluster B), based on the expression patterns of the GRGs. Patients in cluster B had a worse prognosis. A risk score model based on the expression of six GRGs, including ME1, PLOD2, NUP50, CXCR4, SLC35A3, and SRD35A3, could predict patient prognosis. Knockdown of CXCR4 significantly attenuated the glycolytic capacity, as well as the migration, invasion, and proliferation of GC cells. Interestingly, although both the immune subtype (cluster B) and high-risk groups had unfavorable prognosis, these two cohorts had favorable immune microenvironment and increased expression of immune checkpoint genes. We found that high expression of CXCR4 and low expression of ME1 were positively correlated with the infiltration of CD8 + T cells and the response to treatment with an anti-PD-1 immune checkpoint inhibitor.
Conclusions
In the present study, we identified that the expression patterns of GRGs could be used to predict the prognosis of GC patients and may be helpful in guiding clinical treatment decisions.