Identification of a novel glycolysis-related prognostic signature for predicting prognosis and tumor microenvironment of lung adenocarcinoma

Abstract

Abstract Background: Glycolysis affects tumor growth, invasion, treatment resistance, and the tumor microenvironment. In this study, we aimed to construct a glycolysis-related prognostic signature for lung adenocarcinoma (LUAD) and analyze its relationship with the tumor microenvironment (TME). Methods: We analyzed the data of a training set from The Cancer Genome Atlas (TCGA) database and four validation cohorts from the Gene Expression Omnibus (GEO) databases which included 1,689 patients with LUAD. The genetic and transcriptional alterations of glycolysis-related genes (GRGs) were investigated, and evaluated their prognostic value in LUAD patients. The glycolysis-related patterns were identified using consensus unsupervised clustering analysis. A glycolysis-related prognostic signature was then established using the least absolute shrinkage and selection operator and Cox regression analysis. Gene set variation, clinical relevance, and TME analyses were conducted to explain the biological functions of glycolysis regulators and their performance in prognostic prediction. Results: The glycolysis-related patterns were identified based on 18 GRGs, with significant differences in survival between the patterns. By screening of differentially expressed genes between patterns, a novel glycolysis-related prognostic signature was final obtained, including ALDOA, FOSL2, PDE6D, PPARD, and RASAL2, with robust and accurate prognostic performance. The high-, and low risk groups, distinguished by the signature formula, presented a significant difference in overall survival, TME, and immunotherapy response in LUAD patients. The potential roles of the glycolysis-related prognostic signature were confirmed using the validation dataset. Conclusions: This study identified five glycolysis-related prognostic genes that effectively predicted the survival of LUAD. To a certain extent, the newly identified gene signature was related to the TME, especially immune cell infiltration. These findings provide potential biomarkers and therapeutic targets for LUAD.