Comprehensive Analysis of the Association Between the Glycolysis Prognostic Risk Signature and Immune Infiltration in Endometrial Cancer

Abstract

Background: Glycolysis is the primary mechanism of energy metabolism in tumor cells. The purpose of this study is to develop a glycolysis-related risk signature for endometrial cancer and analyze its relationship with immune function.Methods: The mRNA expression profiling and clinical information of endometrial cancer were downloaded from TCGA database. GSEA was performed to screen out gene sets related to glycolysis, and the R software was used to screen DEGs. Univariate Cox and LASSO regression analyses were used to construct a glycolysis-related prognostic signature for endometrial cancer. WGCNA were performed to identify the potential mechanisms associated with the prognostic signature. Immune scores, stromal scores and ESTIMATE scores were calculated based on the R “ESTIMATE” algorithm. The “CIBERSORT” algorithm was used to evaluate the infiltration of immune cells. ROC curve, nomogram, gene alteration, coexpression analyses and PPIs were also performed. Results: We identified a glycolysis-related gene signature (PFKM, PSMC4, NUP85, PDHA1, CDK1, CLDN9, CENPA, GPI, NUP155 and GPCI) for predicting the prognosis of endometrial cancer. Based on this gene signature, the patients were divided into high- and low-risk subgroups. The overall survival of patients in the high-risk subgroup was significantly lower than that of the low-risk group. Multivariate Cox regression analysis results showed that the ten-gene signature was an independent prognostic factor for endometrial cancer. The ROC curve confirmed the accuracy of the prognostic signature (AUC=0.730). The immune scores and ESTIMATE scores of patients in the high-risk subgroup were lower than those of the low-risk subgroup, while the low immune score and ESTIMATE scores were closely related to the poor prognosis of patients. The high-risk group was associated with lower cellular immune infiltration of resting dendritic cells, resting memory T cells and Tregs, while the overall survival rate of resting dendritic cells and Tregs in the low-proportion group was significantly lower than that in the high-proportion group. Conclusions: we constructed a glycolysis-related ten-gene signature to predict the survival and prognosis of endometrial cancer. Our findings may help to elucidate the mechanism of glycolysis and provide new ideas for targeted therapy and prognosis of endometrial cancer.