The prognostic significance of genes involved in glycolysis, immunity, and epithelial-to-mesenchymal transition in glioblastoma

Abstract

Abstract Glioblastoma (GBM) is the most prevalent form of primary brain cancer. In the therapeutic therapy of GBM, there are still several ambiguities. GBM patients urgently need further research to find significant prognostic markers and more effective treatment choices. However, current stage-based clinical approaches still need to be improved for predicting survival and making decisions. This research intended to develop a new GBM risk assessment model based on glycolysis, immunology, and epithelial-mesenchymal transition (EMT) gene signatures. In this analysis, the cohort was constructed using TCGA-GBM data. Leveraging bioinformatics and machine algorithms, we developed a risk model based on glycolysis, immunological, and EMT gene signatures, which was then employed to classify patients into high and low-risk categories. Subsequently, we evaluated whether the risk score was associated with the immunological microenvironment, immunotherapy response, and numerous anticancer drug sensitivity. The unique risk model based on glycolysis, immunological, and EMT gene signatures could assist in predicting clinical prognosis and directing therapy decisions for GBM patients.