Identification of an optimized glycolytic-related risk signature for predicting the prognosis in breast cancer using integrated bioinformatic analysis

Abstract

Aberrant metabolic disorders and significant glycolytic alterations in tumor tissues and cells are hallmarks of breast cancer (BC) progression. This study aims to elucidate the key biomarkers and pathways mediating abnormal glycolysis in breast cancer using bioinformatics analysis. Differential genes expression analysis, gene ontology analysis, Kyoto encyclopedia of genes and genomes analysis, gene set enrichment analyses, and correlation analysis were performed to explore the expression and prognostic implications of glycolysis-related genes. We effectively integrated 4 genes to construct a prognostic model of shorter survival in the high-risk versus low-risk group. The prognostic model showed promising predictive value and may be an integral part of the prognosis of BC. The survival analysis and receiver operating characteristic curves suggested that the signature showed a good predictive performance in both the The Cancer Genome Atlas training set and 2 gene expression omnibus validation sets. Multivariable analysis demonstrated that the 4-gene signature had an independent prognostic value. Furthermore, all calibration curves exhibited robust validity in prognostic prediction. We established an optimized 4-gene signature to clarify the connection between glycolysis and BC, and offered an attractive platform for risk stratification and prognosis predication of BC patients.