Development and validation of a novel survival model for glioblastoma based on glycolysis-related genes.

Objective: To construct a glycolysis-related prognostic model to predict individualized survival in patients with glioblastoma (GBM). Methods: Clinical data for patients with GBM, including expression levels of glycolysis-related genes (GRGs), were extracted from The Cancer Genome Atlas. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were then carried out and protein-protein interactions were investigated. Univariate and multivariate Cox regression analyses were performed on the GRGs to identify the best prognosis-related genes. We then established and verified a novel prognostic model, based on the expression of differentially expressed GRGs that were significantly associated with overall survival in GBM patients. Results: ALDH3B1, CHPF, FBP1, ISG20 and STC1 were chosen to establish the prognostic risk score model. Patients with high risk scores had significantly poorer overall survival than patients with low risk scores. Conclusion: The glycolysis-related model has significant value in performing individualized survival predictions for GBM patients and could suggest better treatment options for GBM patients. Our results may help to elucidate GBM pathogenesis and contribute to clinical decision-making and individualized treatment. [ABSTRACT FROM AUTHOR]