Abstract
OBJECTIVE: Glioblastoma, previously recognized as glioblastoma multiform (GBM), is the most aggressive and most common type of cancer that originates in the brain and has a very poor prognosis for survival. Glioblastoma, as one of the lethal cancers of the brain, is important to be studied in terms of molecular exploration. METHODS: Bioinformatics approaches could be a promising complementary study for identifying more robust biomarkers. This study evaluates the gene expression profile of normal brain endothelial cells versus glioblastoma tumor cells with positive CD3 in more depth by applying R Studio and Cytoscape and its plug-ins. RESULTS: A network of differentially expressed genes (DEGs) introduced promising candidates comprised of TP53, EGFR, FN1, JUN, and CDC42 and their related biological processes. Comprised of differentially expressed genes, this panel's dysregulation could significantly affect the stability of the protein-protein interaction (PPI) network. Moreover, previous studies have validated these genes' relevance to this cancer type. CONCLUSION: In conclusion, the molecular profile of glioblastoma aids in drug targeting following thorough validation assessments. Five key genes and their related biological processes are possible drug targets to control glioblastoma.