Identification of a Novel Eight-Gene Risk Model for Predicting Survival in Glioblastoma: A Comprehensive Bioinformatic Analysis

SIMPLE SUMMARY: People with glioblastoma (GBM) universally have poor survival despite undergoing aggressive treatments. In this study, we aimed to determine genetic biomarkers of GBM that exhibit prognostic implications and examine their role in the tumor microenvironment. To this end, we performed differential gene expression analysis in three independent GBM datasets, followed by establishing a risk model for disease progression. Containing eight genes, this model demonstrated robustness in identifying patient subgroups with poor survival outcome in independent datasets. ABSTRACT: Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. To this end, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes’ biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely CRNDE, NRXN3, POPDC3, PTPRN, PTPRN2, SLC46A2, TIMP1, and TNFSF9. The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and MGMT status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM.