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.
- 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 samples were divided into two sets: The training set (n=319 samples) and the validation set (n=214 samples).
- 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.
- A total of 11 hub genes were revealed to be implicated in the cell cycle and p53 signaling pathway.
- A survival analysis was conducted on another gene expression dataset to validate them as possessing prognostic value.
- A total of 10 prognostic genes (CCNA2, CDC20, CDCA8, GTSE1, KIF23, KIF2C, KIF4A, MELK, TOP2A and TPX2) were identified in KIRC.
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.