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Article Open Access

A network‑based analysis for mining the risk pathways in glioblastoma

  • Authors:
    • Jing Li
    • Yujie Xie
    • Chi Zhang
    • Jianxiong Wang
    • Yong Wu
    • Yuan Yang
    • Yang Xie
    • Zhiyu Lv
  • View Affiliations / Copyright

    Affiliations: Department of Hepatobiliary Surgery, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China, Department of Rehabilitation Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China, Department of Neurology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China
    Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 2712-2717
    |
    Published online on: July 9, 2019
       https://doi.org/10.3892/ol.2019.10598
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Abstract

The most malignant type of brain tumour is glioblastoma multiforme (GBM). Patients with GBM often have a poor prognosis, as a result of incomplete or inaccurate diagnoses. Regulatory pathways have been demonstrated to serve important roles in complex human diseases. Therefore, deciphering these risk pathways may shed light on the molecular mechanisms underlying GBM progression. In the present study, differentially expressed genes and microRNAs (miRNAs) in a publicly available database were identified between normal and tumour samples. To determine the pathophysiology and molecular mechanisms underlying GBM, integrated network analysis was performed to mine GBM‑specific risk pathways. Specifically, a GBM‑specific regulatory network was constructed that integrated manually curated GBM‑associated transcription and post‑transcriptional data resources, including transcription factors and miRNAs. A total of 1,827 differentially expressed genes and 30 miRNAs were identified. The differentially expressed genes were significantly enriched in a number of immune response‑associated functions. Based on the GBM‑specific regulatory network, 15 risk regulatory pathways containing not only known regulators, but also potential novel targets that might be involved in tumourigenesis were identified. Network analysis provides a strategy for leveraging genomic data to identify potential oncogenic pathways and molecular targets for GBM.
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Copy and paste a formatted citation
Spandidos Publications style
Li J, Xie Y, Zhang C, Wang J, Wu Y, Yang Y, Xie Y and Lv Z: A network‑based analysis for mining the risk pathways in glioblastoma. Oncol Lett 18: 2712-2717, 2019.
APA
Li, J., Xie, Y., Zhang, C., Wang, J., Wu, Y., Yang, Y. ... Lv, Z. (2019). A network‑based analysis for mining the risk pathways in glioblastoma. Oncology Letters, 18, 2712-2717. https://doi.org/10.3892/ol.2019.10598
MLA
Li, J., Xie, Y., Zhang, C., Wang, J., Wu, Y., Yang, Y., Xie, Y., Lv, Z."A network‑based analysis for mining the risk pathways in glioblastoma". Oncology Letters 18.3 (2019): 2712-2717.
Chicago
Li, J., Xie, Y., Zhang, C., Wang, J., Wu, Y., Yang, Y., Xie, Y., Lv, Z."A network‑based analysis for mining the risk pathways in glioblastoma". Oncology Letters 18, no. 3 (2019): 2712-2717. https://doi.org/10.3892/ol.2019.10598
Copy and paste a formatted citation
x
Spandidos Publications style
Li J, Xie Y, Zhang C, Wang J, Wu Y, Yang Y, Xie Y and Lv Z: A network‑based analysis for mining the risk pathways in glioblastoma. Oncol Lett 18: 2712-2717, 2019.
APA
Li, J., Xie, Y., Zhang, C., Wang, J., Wu, Y., Yang, Y. ... Lv, Z. (2019). A network‑based analysis for mining the risk pathways in glioblastoma. Oncology Letters, 18, 2712-2717. https://doi.org/10.3892/ol.2019.10598
MLA
Li, J., Xie, Y., Zhang, C., Wang, J., Wu, Y., Yang, Y., Xie, Y., Lv, Z."A network‑based analysis for mining the risk pathways in glioblastoma". Oncology Letters 18.3 (2019): 2712-2717.
Chicago
Li, J., Xie, Y., Zhang, C., Wang, J., Wu, Y., Yang, Y., Xie, Y., Lv, Z."A network‑based analysis for mining the risk pathways in glioblastoma". Oncology Letters 18, no. 3 (2019): 2712-2717. https://doi.org/10.3892/ol.2019.10598
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