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Article Open Access

A thirteen‑gene set efficiently predicts the prognosis of glioblastoma

  • Authors:
    • Huyin Yang
    • Luhao Jin
    • Xiaoyang Sun
  • View Affiliations / Copyright

    Affiliations: Department of Neurosurgery, Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu 223300, P.R. China
    Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 1613-1621
    |
    Published online on: January 2, 2019
       https://doi.org/10.3892/mmr.2019.9801
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Abstract

Glioblastoma multiforme (GBM) is the most common type of brain cancer; it usually recurs and patients have a short survival time. The present study aimed to construct a gene expression classifier and to screen key genes associated with GBM prognosis. GSE7696 microarray data set included samples from 10 recurrent GBM tissues, 70 primary GBM tissues and 4 normal brain tissues. Seed genes were identified by the ‘survival’ package in R and subjected to pathway enrichment analysis. Prognostic genes were selected from the seed genes using the ‘rbsurv’ package in R, unsupervised hierarchical clustering, survival analysis and enrichment analysis. Multivariate survival analysis was performed for the prognostic genes, and the GBM data set from The Cancer Genome Atlas database was utilized to validate the prognostic genes. Of the 1,785 seed genes analyzed, 13 prognostic feature genes, including collagen type XXVIII α1 chain (COL28A1), PDS5 cohesin‑associated factor A (PDS5A), zinc‑finger DHHC‑type containing 2 (ZDHHC2), zinc‑finger protein 24 (ZNF24), myosin VA (MYO5A) and myeloid/lymphoid or mixed‑lineage leukemia translocated to 4 (MLLT4), were identified. These genes performed well on sample classification and prognostic risk differentiation, and six pathways, including adherens junction, cyclic adenosine 3',5'‑monophosphate signaling and Ras signaling pathways, were enriched for these feature genes. The high‑risk group was slightly older compared with the low‑risk group. The validation data set confirmed the prognostic value of the 13 feature genes for GBM; of these, COL28A1, PDS5A, ZDHHC2, ZNF24, MYO5A and MLLT4 may be crucial. These results may aid the understanding of the pathogenesis of GBM and provide important clues for the development of novel diagnostic markers or therapeutic targets.
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Copy and paste a formatted citation
Spandidos Publications style
Yang H, Jin L and Sun X: A thirteen‑gene set efficiently predicts the prognosis of glioblastoma. Mol Med Rep 19: 1613-1621, 2019.
APA
Yang, H., Jin, L., & Sun, X. (2019). A thirteen‑gene set efficiently predicts the prognosis of glioblastoma. Molecular Medicine Reports, 19, 1613-1621. https://doi.org/10.3892/mmr.2019.9801
MLA
Yang, H., Jin, L., Sun, X."A thirteen‑gene set efficiently predicts the prognosis of glioblastoma". Molecular Medicine Reports 19.3 (2019): 1613-1621.
Chicago
Yang, H., Jin, L., Sun, X."A thirteen‑gene set efficiently predicts the prognosis of glioblastoma". Molecular Medicine Reports 19, no. 3 (2019): 1613-1621. https://doi.org/10.3892/mmr.2019.9801
Copy and paste a formatted citation
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Spandidos Publications style
Yang H, Jin L and Sun X: A thirteen‑gene set efficiently predicts the prognosis of glioblastoma. Mol Med Rep 19: 1613-1621, 2019.
APA
Yang, H., Jin, L., & Sun, X. (2019). A thirteen‑gene set efficiently predicts the prognosis of glioblastoma. Molecular Medicine Reports, 19, 1613-1621. https://doi.org/10.3892/mmr.2019.9801
MLA
Yang, H., Jin, L., Sun, X."A thirteen‑gene set efficiently predicts the prognosis of glioblastoma". Molecular Medicine Reports 19.3 (2019): 1613-1621.
Chicago
Yang, H., Jin, L., Sun, X."A thirteen‑gene set efficiently predicts the prognosis of glioblastoma". Molecular Medicine Reports 19, no. 3 (2019): 1613-1621. https://doi.org/10.3892/mmr.2019.9801
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