Research Paper Volume 11, Issue 16 pp 6029—6052

Identification of 9 key genes and small molecule drugs in clear cell renal cell carcinoma

Yongwen Luo 1, *, , Dexin Shen 1, *, , Liang Chen 1, , Gang Wang 2, 3, 4, , Xuefeng Liu 5, , Kaiyu Qian 2, 3, , Yu Xiao 1, 2, 3, 4, , Xinghuan Wang 1, 6, , Lingao Ju 2, 3, ,

  • 1 Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
  • 2 Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China
  • 3 Human Genetics Resource Preservation Center of Hubei Province, Wuhan, China
  • 4 Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
  • 5 Department of Pathology, Lombardi Comprehensive Cancer Center, Georgetown University Medical School, Washington, DC 20007, USA
  • 6 Medical Research Institute, Wuhan University, Wuhan, China
* Equal contribution

received: March 12, 2019 ; accepted: August 5, 2019 ; published: August 18, 2019 ;
How to Cite

Copyright © 2019 Luo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 3.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor that the underlying molecular mechanisms are largely unclear. This study aimed to elucidate the key candidate genes and pathways in ccRCC by integrated bioinformatics analysis. 1387 differentially expressed genes were identified based on three expression profile datasets, including 673 upregulated genes and 714 downregulated genes. Then we used weighted correlation network analysis to identify 6 modules associated with pathological stage and grade, blue module was the most relevant module. GO and KEGG pathway analyses showed that genes in blue module were enriched in cell cycle and metabolic related pathways. Further, 25 hub genes in blue module were identified as hub genes. Based on GEPIA database, 9 genes were associated with progression and prognosis of ccRCC patients, including PTTG1, RRM2, TOP2A, UHRF1, CEP55, BIRC5, UBE2C, FOXM1 and CDC20. Then multivariate Cox regression showed that the risk score base on 9 key genes signature was a clinically independent prognostic factor for ccRCC patients. Moreover, we screened out several new small molecule drugs that have the potential to treat ccRCC. Few of them were identified as biomarkers in ccRCC. In conclusion, our research identified 9 potential prognostic genes and several candidate small molecule drugs for ccRCC treatment.


ccRCC: Clear cell renal cell carcinoma; CMap: Connectivity map; DAVID: Database for Annotation, Visualization, and Integrated Discovery; DEGs: Differentially expressed genes; DFS: Disease-free survival; FDR: False discovery rate; GEO: Gene expression omnibus; GO: Gene ontology; GS: Gene significance; GSEA: Gene set enrichment analysis; HDAC: Histone deacetylase; KEGG: Kyoto encyclopedia of genes and genomes; ME: Module eigengene; MS: Module significance; OS: Overall survival; TCGA: The cancer genome atlas; TOM: Topological overlap matrix; WGCNA: Weighted gene co-expression network analysis.