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Article Open Access

31 gene expression‑based signatures serve as indicators of prognosis for patients with glioma

  • Authors:
    • Zhongjun Yan
    • Jianlong Yang
    • Lingling Fan
    • Dongwei Xu
    • Yan Hu
  • View Affiliations / Copyright

    Affiliations: Neurosurgery Department, The Second Affiliated Hospital of the Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China, Neurosurgery Department, The First Hospital of Yulin, Yulin, Shaanxi 719000, P.R. China, Neurology Department, The First Affiliated Hospital of Xi'an Medical University, Xi'an, Shaanxi 710077, P.R. China
    Copyright: © Yan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 291-297
    |
    Published online on: May 7, 2019
       https://doi.org/10.3892/ol.2019.10327
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Abstract

Glioma has one of the highest mortality rates of all cancer types; however, the prognosis cannot be predicted effectively using clinical indicators, due to the biological heterogeneity of the disease. A total of 31 gene expression‑based signatures were identified using selected features in The Cancer Genome Atlas cohorts and machine learning methods. The signatures were assayed in the training dataset and were further validated in four completely independent datasets. Association analyses were implemented, and the results indicated that the signature was not significantly associated with age, radiation therapy or primary tumor size. A nomogram for the 1‑year overall survival rate of patients with glioma following initial diagnosis was plotted to facilitate the clinical utilization of the signature. Gene Set Enrichment Analysis was performed based on the signature, in order to determine the potential altered pathways. Metabolic pathways were determined to be significantly enriched. In summary, the 31 gene expression‑based signatures were effective and robust in predicting the clinical outcome of glioma in 1,016 glioma samples in five independent international cohorts.
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Copy and paste a formatted citation
Spandidos Publications style
Yan Z, Yang J, Fan L, Xu D and Hu Y: 31 gene expression‑based signatures serve as indicators of prognosis for patients with glioma. Oncol Lett 18: 291-297, 2019.
APA
Yan, Z., Yang, J., Fan, L., Xu, D., & Hu, Y. (2019). 31 gene expression‑based signatures serve as indicators of prognosis for patients with glioma. Oncology Letters, 18, 291-297. https://doi.org/10.3892/ol.2019.10327
MLA
Yan, Z., Yang, J., Fan, L., Xu, D., Hu, Y."31 gene expression‑based signatures serve as indicators of prognosis for patients with glioma". Oncology Letters 18.1 (2019): 291-297.
Chicago
Yan, Z., Yang, J., Fan, L., Xu, D., Hu, Y."31 gene expression‑based signatures serve as indicators of prognosis for patients with glioma". Oncology Letters 18, no. 1 (2019): 291-297. https://doi.org/10.3892/ol.2019.10327
Copy and paste a formatted citation
x
Spandidos Publications style
Yan Z, Yang J, Fan L, Xu D and Hu Y: 31 gene expression‑based signatures serve as indicators of prognosis for patients with glioma. Oncol Lett 18: 291-297, 2019.
APA
Yan, Z., Yang, J., Fan, L., Xu, D., & Hu, Y. (2019). 31 gene expression‑based signatures serve as indicators of prognosis for patients with glioma. Oncology Letters, 18, 291-297. https://doi.org/10.3892/ol.2019.10327
MLA
Yan, Z., Yang, J., Fan, L., Xu, D., Hu, Y."31 gene expression‑based signatures serve as indicators of prognosis for patients with glioma". Oncology Letters 18.1 (2019): 291-297.
Chicago
Yan, Z., Yang, J., Fan, L., Xu, D., Hu, Y."31 gene expression‑based signatures serve as indicators of prognosis for patients with glioma". Oncology Letters 18, no. 1 (2019): 291-297. https://doi.org/10.3892/ol.2019.10327
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