Screening of feature genes of the renal cell carcinoma with DNA microarray.

AIM

To investigate the underlying molecular mechanisms of renal cell carcinoma (RCC) by using the microarray expression profiles of normal kidney and RCC tissue for early diagnosis and treatment of RCC.

MATERIALS AND METHODS

The gene expression profile of GES781 was downloaded from Gene Expression Omnibus database, including including nine tissue samples of RCC tissues removed from nine patients and eight adjacent normal renal tissue samples. We identified the differentially expressed genes (DEGs) by Multtest package in R software. The screened DEGs were further analyzed by bioinformatics methods. Firstly, the comparison of the DEGs expression degree was performed by cluster analysis. Secondly, DAVID was used to perform functional analysis of up- and down- regulated genes and the protein-protein interaction (PPI) networks were constructed by prePPI. Finally, the pathways of genes in PPI networks were discovered by WebGestalt.

RESULTS

Compared with the control, we screened 648 down-regulated and 681 up-regulated DEGs. And the down- and up-regulated DEGs with maximum expression degree were UMOD (uromodulin) and FABP7 (fatty acid binding protein 7), respectively. There was significant difference in the gene expression between the normal kidney and RCC tissue. The up-regulated DEGs in RCC tissue were significantly related to the immune responses and the down-regulated DEGs were significantly related to the oxidation reduction. The most significant pathway in the PPI network of UMOD was cytokine-cytokine receptor interaction.

CONCLUSIONS

The screened DEGs have the potential to become candidate target molecules to monitor, diagnose and treat the RCC, and might be beneficial for the early diagnosis and medication control of RCC.