Minning of Immuno-mitotic and GABAergic genes as potential biomarkers of glioblastoma: An integrated transcriptomic analysis

Abstract

Glioblastoma (GBM) is a highly lethal Central Nervous System (CNS) tumor prevalent in both adults and children, exhibiting elevated rates of mortality and morbidity. Due to the heterogenous nature of GBM, coupled with its nonspecific symptoms underscore the imperative for innovative biomarkers to enhance prognosis and the development of efficacious therapeutic interventions. This bioinformatics study seeks to elucidate the culprit genes, both up-regulated and down-regulated, within the context of their functional relevance, through a comparative analysis of gene expression profiles in GBM and normal brain tissues. Deregulated genes were identified from two Gene Expression Omnibus (GEO) datasets, employing the GEO2R tool to analyze expression data from normal and GBM tissues. Subsequently, differences in expression of genes (DEGs) through functional enrichment analysis were conducted by DAVID to discern their functional implications. Further, Protein-Protein Interaction (PPI) networks were constructed to identify hub genes among the selected up-regulated and down-regulated genes, employing various bioinformatics tools. The impact of the selected hub genes on patient overall survival was investigated using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Notably, the up-regulated hub genes KIF2C and TTK exhibited significant correlations with overall survival, implicating their potential as immuno-mitotic biomarkers. Conversely, GAD2, the sole down-regulated hub gene, emerged as a promising molecular target for GBM, given its association with GABAergic signaling and amino acid metabolism. Consequently, these findings suggest that KIF2C and TTK may serve as immune-mitotic biomarkers, while GAD2 could be explored as a molecular target for GBM therapy. Nevertheless, additional research is essential to unravel the precise mechanistic underpinnings of GBM.