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Article Open Access

Identification of prognostic genes in kidney renal clear cell carcinoma by RNA‑seq data analysis

  • Authors:
    • Yanqin Gu
    • Linfeng Lu
    • Lingfeng Wu
    • Hao Chen
    • Wei Zhu
    • Yi He
  • View Affiliations / Copyright

    Affiliations: Department of Urology, The First Hospital of Jiaxing, Jiaxing, Zhejiang 314001, P.R. China
    Copyright: © Gu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 1661-1667
    |
    Published online on: February 13, 2017
       https://doi.org/10.3892/mmr.2017.6194
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Abstract

The present study aimed to analyze RNA-seq data of kidney renal clear cell carcinoma (KIRC) to identify prognostic genes. RNA‑seq data were downloaded from The Cancer Genome Atlas. Feature genes with a coefficient of variation (CV) >0.5 were selected using the genefilter package in R. Gene co‑expression networks were constructed with the WGCNA package. Cox regression analysis was performed using the survive package. Furthermore, a functional enrichment analysis was conducted using Database for Annotation, Visualization and Integrated Discovery tools. A total of 533 KIRC samples were collected, from which 6,758 feature genes with a CV >0.5 were obtained for further analysis. The KIRC samples were divided into two sets: The training set (n=319 samples) and the validation set (n=214 samples). Subsequently, gene co‑expression networks were constructed for the two sets. A total of 12 modules were identified, and the green module was significantly associated with survival time. Genes from the green module were revealed to be implicated in the cell cycle and p53 signaling pathway. In addition, a total of 11 hub genes were revealed, and 10 of them (CCNA2, CDC20, CDCA8, GTSE1, KIF23, KIF2C, KIF4A, MELK, TOP2A and TPX2) were validated as possessing prognostic value, as determined by conducting a survival analysis on another gene expression dataset. In conclusion, a total of 10 prognostic genes were identified in KIRC. These findings may help to advance the understanding of this disease, and may also provide potential biomarkers for therapeutic development.
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Copy and paste a formatted citation
Spandidos Publications style
Gu Y, Lu L, Wu L, Chen H, Zhu W and He Y: Identification of prognostic genes in kidney renal clear cell carcinoma by RNA‑seq data analysis. Mol Med Rep 15: 1661-1667, 2017.
APA
Gu, Y., Lu, L., Wu, L., Chen, H., Zhu, W., & He, Y. (2017). Identification of prognostic genes in kidney renal clear cell carcinoma by RNA‑seq data analysis. Molecular Medicine Reports, 15, 1661-1667. https://doi.org/10.3892/mmr.2017.6194
MLA
Gu, Y., Lu, L., Wu, L., Chen, H., Zhu, W., He, Y."Identification of prognostic genes in kidney renal clear cell carcinoma by RNA‑seq data analysis". Molecular Medicine Reports 15.4 (2017): 1661-1667.
Chicago
Gu, Y., Lu, L., Wu, L., Chen, H., Zhu, W., He, Y."Identification of prognostic genes in kidney renal clear cell carcinoma by RNA‑seq data analysis". Molecular Medicine Reports 15, no. 4 (2017): 1661-1667. https://doi.org/10.3892/mmr.2017.6194
Copy and paste a formatted citation
x
Spandidos Publications style
Gu Y, Lu L, Wu L, Chen H, Zhu W and He Y: Identification of prognostic genes in kidney renal clear cell carcinoma by RNA‑seq data analysis. Mol Med Rep 15: 1661-1667, 2017.
APA
Gu, Y., Lu, L., Wu, L., Chen, H., Zhu, W., & He, Y. (2017). Identification of prognostic genes in kidney renal clear cell carcinoma by RNA‑seq data analysis. Molecular Medicine Reports, 15, 1661-1667. https://doi.org/10.3892/mmr.2017.6194
MLA
Gu, Y., Lu, L., Wu, L., Chen, H., Zhu, W., He, Y."Identification of prognostic genes in kidney renal clear cell carcinoma by RNA‑seq data analysis". Molecular Medicine Reports 15.4 (2017): 1661-1667.
Chicago
Gu, Y., Lu, L., Wu, L., Chen, H., Zhu, W., He, Y."Identification of prognostic genes in kidney renal clear cell carcinoma by RNA‑seq data analysis". Molecular Medicine Reports 15, no. 4 (2017): 1661-1667. https://doi.org/10.3892/mmr.2017.6194
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