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Article Open Access

Integrative analysis of cancer driver genes in prostate adenocarcinoma

  • Authors:
    • Xin Zhao
    • Yi Lei
    • Ge Li
    • Yong Cheng
    • Haifan Yang
    • Libo Xie
    • Hao Long
    • Rui Jiang
  • View Affiliations / Copyright

    Affiliations: Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China, Department of Endocrinology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China
    Copyright: © Zhao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 2707-2715
    |
    Published online on: January 28, 2019
       https://doi.org/10.3892/mmr.2019.9902
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Abstract

Large‑scale genomics studies have identified recurrently mutated genes in the ETS gene family, including fusions and copy number variations (CNVs), which are involved in the development of prostate adenocarcinoma (PRAD). However, the aetiology of PRAD remains to be fully elucidated. In the present study, 333 driver genes were identified using four computational tools: OncodriveFM, OncodriveCLUST, iCAGES and DrGaP. In addition, 32 driver pathways were identified using DrGaP. SPOP, TP53, SPTA1, AHNAK, HMCN1, ATM, FOXA1, CSMD3, LRP1B and FREM2 were the 10 most recurrently mutated genes in PRAD. ITGAL, TAGAP, SIGLEC10, RAC2 and ITGA4 were the five hub genes in the yellow module that were associated with the number of positive lymph nodes. Hierarchical clustering analysis of the 20 driver genes with the most frequent CNVs revealed three clusters of patients with PRAD. Cluster 3 tumours exhibited significantly higher numbers of positive lymph nodes, higher Gleason scores, more advanced cancer stages and poorer prognosis than cluster 1 and 2 tumours. A total of 48 genes were significantly associated with the number of positive lymph nodes, Gleason scores and pathologic stage in patients with PRAD. The identified set of cancer genes and pathways sheds light on the tumorigenesis of PRAD and creates avenues for the development of prognostic biomarkers and driver gene‑targeted therapies in PRAD.
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Copy and paste a formatted citation
Spandidos Publications style
Zhao X, Lei Y, Li G, Cheng Y, Yang H, Xie L, Long H and Jiang R: Integrative analysis of cancer driver genes in prostate adenocarcinoma. Mol Med Rep 19: 2707-2715, 2019.
APA
Zhao, X., Lei, Y., Li, G., Cheng, Y., Yang, H., Xie, L. ... Jiang, R. (2019). Integrative analysis of cancer driver genes in prostate adenocarcinoma. Molecular Medicine Reports, 19, 2707-2715. https://doi.org/10.3892/mmr.2019.9902
MLA
Zhao, X., Lei, Y., Li, G., Cheng, Y., Yang, H., Xie, L., Long, H., Jiang, R."Integrative analysis of cancer driver genes in prostate adenocarcinoma". Molecular Medicine Reports 19.4 (2019): 2707-2715.
Chicago
Zhao, X., Lei, Y., Li, G., Cheng, Y., Yang, H., Xie, L., Long, H., Jiang, R."Integrative analysis of cancer driver genes in prostate adenocarcinoma". Molecular Medicine Reports 19, no. 4 (2019): 2707-2715. https://doi.org/10.3892/mmr.2019.9902
Copy and paste a formatted citation
x
Spandidos Publications style
Zhao X, Lei Y, Li G, Cheng Y, Yang H, Xie L, Long H and Jiang R: Integrative analysis of cancer driver genes in prostate adenocarcinoma. Mol Med Rep 19: 2707-2715, 2019.
APA
Zhao, X., Lei, Y., Li, G., Cheng, Y., Yang, H., Xie, L. ... Jiang, R. (2019). Integrative analysis of cancer driver genes in prostate adenocarcinoma. Molecular Medicine Reports, 19, 2707-2715. https://doi.org/10.3892/mmr.2019.9902
MLA
Zhao, X., Lei, Y., Li, G., Cheng, Y., Yang, H., Xie, L., Long, H., Jiang, R."Integrative analysis of cancer driver genes in prostate adenocarcinoma". Molecular Medicine Reports 19.4 (2019): 2707-2715.
Chicago
Zhao, X., Lei, Y., Li, G., Cheng, Y., Yang, H., Xie, L., Long, H., Jiang, R."Integrative analysis of cancer driver genes in prostate adenocarcinoma". Molecular Medicine Reports 19, no. 4 (2019): 2707-2715. https://doi.org/10.3892/mmr.2019.9902
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