Background: Renal cell carcinoma (RCC) is the most common malignant tumor of the
Objective: The aim of this study was to identify key genes signatures during RCC and uncover
their potential mechanisms.
Method: Firstly, the gene expression profiles of GSE53757 which contained 144 samples, including
72 kidney cancer samples and 72 controls, were downloaded from the GEO database. And then
differentially expressed genes (DEGs) between the kidney cancer samples and the controls were
identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID.
Furthermore, the correlation-based feature subset (CFS) method was applied to the selection of key
genes of DEGs. In addition, the classification model between the kidney cancer samples and the
controls was built by Adaboost based on the selected key genes.
Results: 213 DEGs including 80 up-regulated and 133 down-regulated genes were selected as the
feature genes to build the classification model between the kidney cancer samples and the controls
by CFS method. The accuracy of the classification model by using 5-folds cross-validation test and
independent set test is 84.4% and 83.3%, respectively. Besides, TYROBP, CD4163, CAV1,
CXCL9, CXCL11 and CXCL13 also can be found in the top 20 hub genes screened by proteinprotein
interaction (PPI) network.
Conclusion: It indicated that CFS is a useful tool to identify key genes in kidney cancer. Besides,
we also predicted genes such as TYROBP, CD4163, CAV1, CXCL9, CXCL11 and CXCL13 that
might target genes to diagnose the kidney cancer.