Screening prognosis-related genes in glioblastoma by microarray analysis

This study was aimed to explore the prognosis-related biomarkers in glioblastoma and guide the therapy. The gene expression profile of glioblastoma samples with different prognosis outcomes was downloaded from National Center of Biotechnology Information Gene Expression Omnibus. The differently expressed genes (DEGs) among different samples were identified through pairwise comparison via Limma package of R. The DEGs were clustered using the Mfuzz package of R . The clusters with gene expression increasing or decreasing with the prognosis were selected, and functional enrichment of the selected genes was analyzed via the Database for Annotation, Visualization and Integrated Discovery. A protein–protein interaction (PPI) network of the selected genes was constructed through the Search Tool for Retrieval of Interacting Proteins and visualized by Cytoscape. The Cancer Genome Atlas database and IVY-GAP database were used to verify the DEGs. We analyzed the correlation between subtypes and the DEGs. Totally, 2649 DEGs were identified and divided into 10 clusters. Expression value of the genes in clusters 2 and 9 kept increasing and decreasing, respectively, with the improved prognosis. The DEGs of cluster 2/9 were enriched in 23/24 Gene Ontology terms and 6/4 Kyoto Encyclopedia of Genes and Genomes pathways. Annotation of transcription factor binding sites of DEGs revealed that most genes were regulated by transcription factors. In the PPI network, CACNA1D , GNAO1 , STAT3 and ERBB3 had 11, 11, 11 and 10 node degree, respectively. Bioinformatics methods could help to identify significant genes and pathways in glioblastoma. CACNA1D , GNAO1 , STAT3 and ERBB3 might serve as the prognostic biomarkers in glioblastoma.