Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments.

ABSTRACT:

Background

Although major driver gene have been identified, the complex molecular heterogeneity of renal cell cancer (RCC) remains unclear. Therefore, more relevant genes need to be identified to explain the pathogenesis of renal cancer.

Methods

Microarray datasets GSE781, GSE6344, GSE53000 and GSE68417 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by employing GEO2R tool, and function enrichment analyses were performed by using DAVID. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. Survival analysis was performed using GEPIA. Differential expression was verified in Oncomine. Cell experiments (cell viability assays, transwel

SUBMITTER: Chen Y 

PROVIDER: S-EPMC7372855 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature