Abstract
Glioblastoma multiforme (GBM) is a highly aggressive primary brain tumor associated with high fatality rates, poor prognosis, and limited treatment options. To enhance our understanding of the disease and pave the way for targeted therapies, it is imperative to identify key genes influencing GBM progression. In this study, we harnessed RNA-Seq gene count data from GBM patients sourced from the GEO database, conducting an in-depth analysis of gene expression patterns. Our investigation involved the stratification of samples into two distinct sets, Group I and Group II, comparing low-grade and GBM tumor samples, respectively. Subsequently, we performed differential expression analysis and enrichment analysis to uncover significant gene signatures. To elucidate the protein-protein interactions that underlie GBM, we leveraged the STRING plugin within Cytoscape for comprehensive network visualization and analysis. By applying Maximal clique centrality (MCC) scores, we identified a set of 10 hub genes in each group. These hub genes were subjected to survival analysis, highlighting their prognostic relevance. In Group I, comprising BUB1, DLGAP5, BUB1B, CDK1, TOP2A, CDC20, KIF20A, ASPM, BIRC5, and CCNB2, these genes emerged as potential biomarkers associated with the transition to low-grade tumors. In Group II, encompassing LIF, LBP, CSF3, IL6, CCL2, SAA1, CCL20, MMP9, CXCL10, and MMP1, these genes were implicated in transforming adult glioblastoma. Kaplan–Meier's overall survival analysis of these hub genes revealed that modifications, particularly upregulation of these candidate genes, were associated with reduced survival in GBM patients. The findings underscore the significance of genomic alterations and differential gene expression in GBM, presenting opportunities for early diagnosis and targeted therapeutic interventions. This study offers valuable insights into the potential avenues for improving the clinical management of GBM.