Systems biology-driven drug repurposing in glioblastoma

Background and purpose: Glioblastoma (GBM) is an aggressive brain tumor with limited treatment options and poor patient survival. This study aimed to identify key genes and altered pathways in GBM and to explore potential drug-repurposing candidates using a systems biology approach. Experimental approach: We analyzed gene expression data from two public datasets (GSE4290 and GSE50161) using Transcriptome Analysis Console. These datasets included 102 GBM samples and 23 non-tumor brain samples. We identified differentially expressed genes using statistical thresholds. A protein-protein interaction network was built using STRING and analyzed in Cytoscape based on degree, betweenness, and closeness metrics. Pathway analysis was carried out using KEGG. Survival analysis was performed with GEPIA2. Drug-gene interactions were screened using DGIdb, and molecular docking was carried out for selected targets. Findings/Results: A total of 1,704 upregulated and 1,345 downregulated genes were detected. Network analysis highlighted TP53, EGFR, CDC42, and STAT3 as central hub genes. Pathway analysis showed major changes in MAPK signaling, neuroactive ligand-receptor interaction, and extracellular matrix-receptor interaction. Higher expression of CDC42, EGFR, and TP53 was associated with shorter overall survival in GBM patients. Drug screening identified 28 approved compounds targeting six core genes. Docking analysis showed strong binding affinity for several candidates, with scores of -8.5 kcal/mol or lower. Conclusion and implications: This study identified important molecular targets and pathways in GBM and suggests several existing drugs with potential value for therapeutic repurposing. These findings may support future experimental and clinical studies in GBM treatment.