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Article Open Access

Identification of hub genes and pathways in glioblastoma by bioinformatics analysis

  • Authors:
    • Shoubo Yang
    • Kaidi Gao
    • Wenbin Li
  • View Affiliations / Copyright

    Affiliations: Department of Neuro-Οncology, Neurosurgery Center, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, P.R. China, Continuing Education and Training Department, Beijing Rehabilitation Hospital Affiliated to Capital Medical University, Beijing 100144, P.R. China
    Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY 4.0].
  • Pages: 1035-1041
    |
    Published online on: October 31, 2018
       https://doi.org/10.3892/ol.2018.9644
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Abstract

Glioblastoma (GBM) is the most common type of malignant brain tumor, and is associated with poor patient prognosis. A comprehensive understanding of the molecular mechanism underlying GBM may help to guide the identification of novel diagnoses and treatment targets. The gene expression profile of the GSE4290 GBM dataset was analyzed in order to identify differentially expressed genes (DEGs). Enriched pathways were identified through Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses. A protein‑protein interaction network was constructed in order to identify hub genes and for module analysis. Expression and survival analyses were conducted in order to screen and validate critical genes. A total of 1,801 DEGs were recorded, including 620 upregulated and 1,181 downregulated genes. Upregulated DEGs were enriched in the terms ‘mitotic cell cycle process’, ‘mitotic cell cycle’ and ‘cell cycle process’. Downregulated genes were enriched in ‘transsynaptic signaling’, ‘anterograde transsynaptic signaling’ and ‘synaptic signaling’. A total of 15 hub genes, which displayed a high degree of connectivity, were selected. These genes included vascular endothelial growth factor A, cyclin‑dependent kinase 1 (CDK1), cell‑division cycle protein 20 (CDC20), aurora kinase A (AURKA), and budding uninhibited by benzimidazoles 1 (BUB1). The identified DEGs and hub genes may help guide investigations on the mechanisms underlying the development and progression of GBM. CDK1, CDC20, AURKA and BUB1, which are involved in cell cycle pathways, may be potential targets in the diagnosis and therapy of GBM.
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Copy and paste a formatted citation
Spandidos Publications style
Yang S, Gao K and Li W: Identification of hub genes and pathways in glioblastoma by bioinformatics analysis. Oncol Lett 17: 1035-1041, 2019.
APA
Yang, S., Gao, K., & Li, W. (2019). Identification of hub genes and pathways in glioblastoma by bioinformatics analysis. Oncology Letters, 17, 1035-1041. https://doi.org/10.3892/ol.2018.9644
MLA
Yang, S., Gao, K., Li, W."Identification of hub genes and pathways in glioblastoma by bioinformatics analysis". Oncology Letters 17.1 (2019): 1035-1041.
Chicago
Yang, S., Gao, K., Li, W."Identification of hub genes and pathways in glioblastoma by bioinformatics analysis". Oncology Letters 17, no. 1 (2019): 1035-1041. https://doi.org/10.3892/ol.2018.9644
Copy and paste a formatted citation
x
Spandidos Publications style
Yang S, Gao K and Li W: Identification of hub genes and pathways in glioblastoma by bioinformatics analysis. Oncol Lett 17: 1035-1041, 2019.
APA
Yang, S., Gao, K., & Li, W. (2019). Identification of hub genes and pathways in glioblastoma by bioinformatics analysis. Oncology Letters, 17, 1035-1041. https://doi.org/10.3892/ol.2018.9644
MLA
Yang, S., Gao, K., Li, W."Identification of hub genes and pathways in glioblastoma by bioinformatics analysis". Oncology Letters 17.1 (2019): 1035-1041.
Chicago
Yang, S., Gao, K., Li, W."Identification of hub genes and pathways in glioblastoma by bioinformatics analysis". Oncology Letters 17, no. 1 (2019): 1035-1041. https://doi.org/10.3892/ol.2018.9644
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