Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in glioblastoma

Zhou,Lingqi; Tang,Hai; Wang,Fang; Chen,Lizhi; Ou,Shanshan; Wu,Tong; Xu,Jie; Guo,Kaihua

doi:10.3892/mmr.2018.9411

Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in glioblastoma

Authors:
- Lingqi Zhou
- Hai Tang
- Fang Wang
- Lizhi Chen
- Shanshan Ou
- Tong Wu
- Jie Xu
- Kaihua Guo
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Affiliations: Department of Anatomy and Neurobiology, Zhongshan School of Medicine, Sun Yat‑sen University, Guangzhou, Guangdong 510080, P.R. China, Department of Molecular Diagnostics, Sun Yat‑sen University Cancer Center, Guangzhou, Guangdong 510060, P.R. China
Published online on: August 21, 2018 https://doi.org/10.3892/mmr.2018.9411
Pages: 4185-4196
Copyright: © Zhou et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Glioblastoma (GBM) is the most common type of malignant tumor of the central nervous system. The prognosis of patients with GBM is very poor, with a survival time of ~15 months. GBM is highly heterogeneous and highly aggressive. Surgical removal of intracranial tumors does provide a good advantage for patients as there is a high rate of recurrence. The understanding of this type of cancer needs to be strengthened, and the aim of the present study was to identify gene signatures present in GBM and uncover their potential mechanisms. The gene expression profiles of GSE15824 and GSE51062 were downloaded from the Gene Expression Omnibus database. Normalization of the data from primary GBM samples and normal samples in the two databases was conducted using R software. Then, joint analysis of the data was performed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and the protein‑protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed using Cytoscape software. Identification of prognostic biomarkers was conducted using UALCAN. In total, 9,341 DEGs were identified in the GBM samples, including 9,175 upregulated genes and 166 downregulated genes. The top 1,000 upregulated DEGs and all of the downregulated DEGs were selected for GO, KEGG and prognostic biomarker analyses. The GO results showed that the upregulated DEGs were significantly enriched in biological processes (BP), including immune response, cell division and cell proliferation, and the downregulated DEGs were also significantly enriched in BP, including cell growth, intracellular signal transduction and signal transduction by protein phosphorylation. KEGG pathway analysis showed that the upregulated DEGs were enriched in circadian entrainment, cytokine‑cytokine receptor interaction and maturity onset diabetes of the young, while the downregulated DEGs were enriched in the TGF‑β signaling pathway, MAPK signaling pathway and pathways in cancer. All of the downregulated genes and the top 1,000 upregulated genes were selected to establish the PPI network, and the sub‑networks revealed that these genes were involved in significant pathways, including olfactory transduction, neuroactive ligand‑receptor interaction and viral carcinogenesis. In total, seven genes were identified as good prognostic biomarkers. In conclusion, the identified DEGs and hub genes contribute to the understanding of the molecular mechanisms underlying the development of GBM and they may be used as diagnostic and prognostic biomarkers and molecular targets for the treatment of patients with GBM in the future.

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