Identification of Key Genes and Signaling Pathways Associated with the Progression of Glioblastoma multiform

Genomic features have been gradually regarded as part of the basics to the clinical diagnosis, prognosis and treatment for glioblastoma multform (GBM). However, the molecular modifications taking place during the advancement of GBM remain unclear. Therefore, recognition of potential important genes and pathways in the gastric cancer progression is important to clinical practices. In the present study, gene expression dataset (GSE116520) of GBM were selected from the Gene Expression Omnibus (GEO) database and were further used to identify differentially expressed genes (DEGs). Then, pathway and Gene Ontology (GO) enrichment analyses were conducted, and a protein-protein interaction (PPI) network was constructed to explore the potential mechanism of GBM carcinogenesis. Significant modules were discovered using the PEWCC1 plugin for Cytoscape. In addition, a target gene - miRNA regulatory network and target gene - TF regulatory network in GBM were constructed using common deregulated miRNAs, TFs and DEGs. Finally, we carried on validation of hub genes by UALCAN, cBioporta, human protein atlas, ROC (Receiver operating characteristic) curve analysis, RT-PCR and immune infiltration analysis. The results indicated that a total of 947 differential expressed genes (DEGs) (477 up regulated and 470 down regulated) was identified in microarray profiles. Pathway enrichment analysis revealed that DEGs (up and down regulated) were mainly associated in reactive oxygen species degradation, ribosome, homocarnosine biosynthesis and GABAergic synapse, whereas GO enrichment analyses revealed that DEGs (up and down regulated) were mainly associated in macromolecule catabolic process, cytosolic part, synaptic signaling and synapse part as the main pathways associated in these processes. Finally, we filtered out hub genes, including MYC, ARRB1, RPL7A, SNAP25, SOD2, SVOP, ABCC3 and ABCA2, from the all networks. Validation of hub genes suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for GBM, which will be useful for further clinical applications in GBM diagnosis, prognosis and targeted therapy.