Bioinformatic Identification of a Six-Gene Biomarker Signature for Prognostic and Diagnostic Insight in Glioblastoma

Glioblastoma (GBM) is an aggressive type of brain tumor with a poor prognosis. This study aimed to identify key hub genes as potential prognostic biomarkers for GBM treatment, using the R package limma , we analyzed the GSE50161, GSE4290, GSE68848, and GSE70231 datasets from GEO to identify differentially expressed genes (DEGs). A protein-protein interaction (PPI) network was constructed using the STRING database, and hub genes were identified by using Cytohubba and MCODE plugins. Analysis of enrichment focusing on GO and KEGG pathway terms was performed with the DAVID database. The six key hub genes (CAMK2A, SYN1, SLC32A1, SYT1, STX1A and VAMP2) were identified among the top 210 DEGs using PPI and pathway analysis. Correlation study revealed links between the expressivity of such hub genes and factors like genetic mutation, overall survival (OS), tumor purity, and the infiltration of immune cells. We also found candidate transcription factors, miRNAs and chemotherapeutic drugs that have a therapeutic potential. Overall, these six hub genes can be considered as promising biomarkers to solve the problem of GBM heterogeneity in its diagnosis and treatment.