Discovering potential therapeutic targets in glioblastoma multiforme using a multi-omics approach

Abstract

Background

Glioblastoma multiforme (GBM) is a highly aggressive primary brain tumor associated with high fatality rates, poor prognosis, and limited treatment options. In this study, we utilized RNA-Seq gene count data from GBM patients, sourced from the Gene Expression Omnibus (GEO) database, to conduct an in-depth analysis of gene expression patterns.

Methods

Our investigation involved stratifying samples into two distinct sets, Group I and Group II, comparing normal, 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 associated with GBM, we used the STRING plugin within Cytoscape for comprehensive network visualization and analysis.

Results

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, genes such as LIF, LBP, CSF3, IL6, CCL2, SAA1, CCL20, MMP9, CXCL10, and MMP1 were found to be involved in the transformation to adult glioblastoma. Kaplan–Meier's overall survival analysis of these hub genes revealed that modifications, particularly the upregulation of these candidate genes, were associated with reduced survival in GBM patients.

Conclusions

The findings established the significance of genomic alterations and differential gene expression in GBM, presenting opportunities for prognostic and targeted therapeutic interventions. This study provides valuable insights into potential avenues for enhancing the clinical management of GBM.