Identification of Characteristic Genes in Glioblastoma by Integrated Bioinformatics and Weighted Gene Correlation Network Analysis

OBJECTIVE To identify the key characteristic genes, pathways and transcriptional regulatory mechanisms of tumors using glioblastoma(GBM) chip data, integrated bioinformatics methods and weighted gene correlation network analysis (WGCNA). METHODS High throughput chip data was downloaded from GEO database. The integrated bioinformatics methods were used to identify the differentially expressed genes. WGCNA was used to analyze the hub genes, which was the key gene in GBM. Differentially expressed genes and hub genes were integrated by the Venn analysis, and the characteristic genes in GBM was identified. The Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment were used to analyze the functions and pathways enriched by GBM characteristic genes. Kaplan-Meier analysis was used to evaluate the association between the levels of the characteristic genes and patients' overall survival. The online tool Gene-cloud of Biotechnology Information(GCBI) was used to analyze the transcription factors regulating these characteristic genes. RESULTS Two hundred and seventy-three characteristic genes were identified. These genes most likely affected the functions of ion channel, protein kinase and GABA receptor. The expression level of CHD5, SYP, and PHYHIP were significantly related to the overall survival of GBM patients. Transcription factor Sp1, Sp3, and REST might be the key transcription factors for these characteristic genes. CONCLUSION The present study identifies the characteristic genes of GBM and their regulatory mechanism by various bioinformatics methods. The results may provide new basis for the precise treatment of GBM.