Identification of hub genes as novel biomarkersin glioblastoma using integrated bioinformaticsanalysis and experimental validation

Introduction: Glioblastoma is a fatal brain cancer, and identifyingnovel biomarkers is crucial for early diagnosis, prognosisand treatment. Gene expression data analysis using bioinformaticstools can help identify potential biomarkers. This studyaimed to identify hub genes as glioblastoma biomarkers by analyzingdifferentially expressed genes (DEGs).Materials and Methods: We analyzed gene expression profilesof glioblastoma tumor and normal brain tissues from the GEO expressedgenes (DEGs) between the samples, which were furtheranalyzed for biological functions using Gene Ontologyand KEGG pathway enrichment. A protein-protein interaction(PPI) network was constructed using the STRING database,and MCODE and CentiScaPe plug-ins in Cytoscape were usedfor more detailed analysis. Hub genes were validated with GEPIA'sBox Plots, and their correlation with overall survival wasanalyzed by Survival Plots.Results: By comparing glioblastoma tumor and normal braintissues, ۱۷۶۲ DEGs were identified, with ۶۴۱ upregulated and۱۱۲۱ downregulated genes. The upregulated DEGs were significantlyenriched in biological processes (BP) related to positiveregulation of biological processes, tissue development, cellcycle, and cell division. Protein binding and the control of theextracellular matrix structural constituent were also identifiedas significant molecular functions (MF). KEGG analysis revealedthat upregulated DEGs were mainly involved in regulatingthe cell cycle, while downregulated DEGs were enriched inthe GABAergic synapse and Morphine addiction pathways. APPI network of upregulated DEGs identified three hub genes,FANCI, CKAP۲L, and FBXO۵, which were significantly upregulatedin glioblastoma tissues and negatively correlated withoverall survival in glioblastoma patients.Conclusion: In Conclusion: , our study identified three hubgenes, FANCI, CKAP۲L, and FBXO۵, as potential novel biomarkersfor glioblastoma, which may provide new insightsinto the diagnosis, prognosis, and treatment of this devastatingcancer. Bioinformatics analysis can help pinpoint therapeutictargets for cancer.