Abstract

Background: Renal cell carcinoma (RCC) is the most common malignant tumor of the adult kidney.

Objective: The aim of this study was to identify key genes signatures during RCC and uncover their potential mechanisms.

Methods: 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.

Keywords: Gene expression profiles, gene selection, renal cell carcinoma, correlation-based feature subset (CFS), Adaboost, gene ontology.