Example identification of prognostic genes from TAGC RNA‑seq data

The present study aimed to analyze RNA-seq data of kidney renal clear cell carcinoma (KIRC) to identify prognostic genes.

  1. RNA‑seq data were downloaded from The Cancer Genome Atlas.
  2. Feature genes with a coefficient of variation (CV) >0.5 were selected using the genefilter package in R.
  3. Gene co‑expression networks were constructed with the WGCNA package.
  4. Cox regression analysis was performed using the survive package.
  5. A functional enrichment analysis was conducted using Database for Annotation, Visualization and Integrated Discovery tools.
  6. A total of 533 KIRC samples were collected, from which 6,758 feature genes with a CV >0.5 were obtained for further analysis.
  7. The samples were divided into two sets: The training set (n=319 samples) and the validation set (n=214 samples).
  8. 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.
  9. A total of 11 hub genes were revealed to be implicated in the cell cycle and p53 signaling pathway.
  10. A survival analysis was conducted on another gene expression dataset to validate them as possessing prognostic value.
  11.  A total of 10 prognostic genes (CCNA2, CDC20, CDCA8, GTSE1, KIF23, KIF2C, KIF4A, MELK, TOP2A and TPX2) were identified in KIRC.

rna-seq

Results of a cluster analysis, and 12 modules identified from the gene expression networks. (A) Training set; (B) validation set. Gray represents no module.

These findings may help to advance the understanding of this disease, and may also provide potential biomarkers for therapeutic development.

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. Mol Med Rep [Epub ahead of print]. [abstract]

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