Integrative analysis of glioblastoma multiforme: the power of non-coding RNAs and hub genes in cancer research.

Abstract

This study aims to dissect the complex molecular landscape of glioblastoma multiforme (GBM), focusing on identifying key regulatory non-coding RNAs and protein-coding genes that could serve as therapeutic targets and prognostic biomarkers. GBM is an aggressive form of brain cancer characterized by poor prognosis and limited treatment options. Recent advances in high-throughput genomic technologies have opened new avenues for understanding the molecular underpinnings of GBM, with a particular focus on the roles of ncRNAs. We utilized multiple datasets from the NCBI Gene Expression Omnibus (GEO) to analyze mRNA, miRNA, lncRNA, and circRNA expression profiles in GBM versus normal brain tissues. Differential expression analysis was conducted using GEO2R, followed by pathway enrichment and protein-protein interaction (PPI) network analyses using DAVID and STRING databases, respectively. Hub genes were identified and validated through GEPIA2, and a competing endogenous RNA (ceRNA) network was constructed to elucidate the interactions between non-coding RNAs (ncRNAs) and protein-coding genes. The study identified several differentially expressed genes and ncRNAs, highlighting complex interactions within the PPI network and significant pathway enrichments implicated in GBM progression. The ceRNA network analysis revealed potential regulatory axes mediated by ncRNAs. Validation of hub genes confirmed their differential expression and prognostic value. Additionally, correlations between gene expression, drug sensitivity, and immune cell infiltration were analyzed, offering insights into personalized therapeutic approaches. Our findings underscore the intricate molecular networks in GBM, emphasizing the role of ncRNAs in tumor biology and their potential as therapeutic targets. The study highlights the necessity for further experimental validation of bioinformatics predictions to bridge the gap between genomic insights and clinical application, ultimately aiming to enhance the prognosis and treatment strategies for GBM patients.