Identification of potential crucial genes and molecular mechanisms in glioblastoma multiforme by bioinformatics analysis
- Xiaojie Chen
- Yuanbo Pan
- Mengxia Yan
- Guanshui Bao
- Xuhong Sun
Affiliations: Department of Neurology, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 201999, P.R. China, Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310000, P.R. China
- Published online on: May 20, 2020 https://doi.org/10.3892/mmr.2020.11160
Copyright: © Chen
et al. This is an open access article distributed under the
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Glioblastoma multiforme (GBM) is the most common and malignant brain tumor of the adult central nervous system and is associated with poor prognosis. The present study aimed to identify the hub genes in GBM in order to improve the current understanding of the underlying mechanism of GBM. The RNA‑seq data were downloaded from The Cancer Genome Atlas database. The edgeR package in R software was used to identify differentially expressed genes (DEGs) between two groups: Glioblastoma samples and normal brain samples. Gene Ontology (GO) functional enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed using Database for Annotation, Visualization and Integrated Discovery software. Additionally, Cytoscape and Search Tool for the Retrieval of Interacting Genes/Proteins tools were used for the protein‑protein interaction network, while the highly connected modules were extracted from this network using the Minimal Common Oncology Data Elements plugin. Next, the prognostic significance of the candidate hub genes was analyzed using UALCAN. In addition, the identified hub genes were verified by reverse transcription‑quantitative (RT‑q) PCR. In total, 1,483 DEGs were identified between GBM and control samples, including 954 upregulated genes and 529 downregulated genes (P<0.01; fold‑change >16) and these genes were involved in different GO terms and signaling pathways. Furthermore, CDK1, BUB1, BUB1B, CENPA and GNG3 were identified as key genes in the GBM samples. The UALCAN tool verified that higher expression level of CENPA was relevant to poorer overall survival rates. In conclusion, CDK1, BUB1, BUB1B, CENPA and GNG3 were found to be potential biomarkers for GBM. Additionally, ‘cell cycle’ and ‘γ‑aminobutyric acid signaling’ pathways may serve a significant role in the pathogenesis of GBM.