Glycolysis-related five-gene signature correlates with prognosis and immune infiltration in gastric cancer

Abstract

BACKGROUND Gastric cancer (GC) is one of the most common malignancies worldwide. Glycolysis has been demonstrated to be pivotal for the carcinogenesis of GC. AIM To develop a glycolysis-based gene signature for prognostic evaluation in GC patients. METHODS Differentially expressed genes correlated with glycolysis were identified in stomach adenocarcinoma data (STAD). A risk score was established through a univariate Cox and least absolute shrinkage and selection operator analysis. The model was evaluated using the area under the receiver operating characteristic curves. RNA-sequencing data from high- and low-glycolysis groups of STAD patients were analyzed using Cibersort algorithm and Spearman correlation to analyze the interaction of immune cell infiltration and glycolysis. Multiomics characteristics in different glycolysis status were also analyzed. RESULTS A five-gene signature comprising syndecan 2, versican, malic enzyme 1, pyruvate carboxylase and SRY-box transcription factor 9 was constructed. Patients were separated to high- or low-glycolysis groups according to risk scores. Overall survival of patients with high glycolysis was poorer. The sensitivity and specificity of the model in prediction of survival of GC patients were also observed by receiver operating characteristic curves. A nomogram including clinicopathological characteristics and the risk score also showed good prediction for 3- and 5-year overall survival. Gene set variation analysis showed that high-glycolysis patients were related to dysregulation of pancreas beta cells and estrogen late pathways, and low-glycolysis patients were related to Myc targets, oxidative phosphorylation, mechanistic target of rapamycin complex 1 signaling and G2M checkpoint pathways. Tumor-infiltrating immune cells and multiomics analysis suggested that the different glycolysis status was significantly correlated with multiple immune cell infiltration. The patients with high glycolysis had lower tumor mutational burden and neoantigen load, higher incidence of microsatellite instability and lower chemosensitivity. High glycolysis status was often found among patients with grade 2/3 cancer or poor prognosis. CONCLUSION The genetic characteristics revealed by glycolysis could predict the prognosis of GC. High glycolysis significantly affects GC phenotype, but the detailed mechanism needs to be further studied.