Identifying Key Pathogenic Mechanisms in Recurrent Glioblastoma Through Bioinformatics Analysis

Despite aggressive surgery and adjuvant therapy, glioblastoma commonly recurs within months. We aimed to identify key genes and microRNAs (miRNAs)- mRNA regulatory networks associated with recurrent glioblastoma and to nominate candidate repurposable drugs.

Two mRNA expression datasets (GSE58399 and GSE42669) from GEO were analyzed to identify differentially expressed genes (DEGs) between recurrent and primary tumors. Functional enrichment (Gene Ontology, KEGG) characterized implicated processes and pathways. A protein-protein interaction network (STRING and Cytoscape) identified hub genes. miRWalk 3.0, integrating TargetScan, miRDB, and miRTarBase, predicted miRNAs targeting hub genes and defined miRNA-mRNA interactions. The Connectivity Map (CMap) was used to prioritize small molecules predicted to reverse the DEGs signature. Kaplan-Meier survival analyses assessed associations between candidate genes and patient outcomes.

We identified 201 DEGs and constructed a PPI network comprising 180 nodes and 337 edges. Ten hub genes were prioritized. CMap nominated five top candidate compounds- levamisole, chlorzoxazone, ranitidine, atovaquone, and chrysin-as potential therapeutics for glioblastoma recurrence. Synaptotagmin 1 (SYT1) emerged among hub genes and was predicted to be regulated by 12 miRNAs. Elevated SYT1 expression correlated with poorer overall and progression-free survival in recurrent glioblastoma patients.

This integrative analysis highlights SYT1 and its upstream miRNAs as candidate biomarkers and potential therapeutic targets in recurrent glioblastoma and proposes several repurposable compounds for experimental validation. Further functional studies and clinical validation are required prior to translation.

Collectively, this study offers valuable insights into the regulatory landscape of recurrent glioblastoma and lays the groundwork for more targeted and personalized therapeutic approaches.