Identification of a microRNA panel for clear-cell kidney cancer.

OBJECTIVES: To identify a robust panel of microRNA signatures that can classify tumor from normal kidney using microRNA expression levels. Mounting evidence suggests that microRNAs are key players in essential cellular processes and that their expression pattern can serve as diagnostic biomarkers for cancerous tissues. METHODS: We selected 28 clear-cell type human renal cell carcinoma (ccRCC), samples from patient-matched specimens to perform high-throughput, quantitative real-time polymerase chain reaction analysis of microRNA expression levels. The data were subjected to rigorous statistical analyses and hierarchical clustering to produce a discrete set of microRNAs that can robustly distinguish ccRCC from their patient-matched normal kidney tissue samples with high confidence. RESULTS: Thirty-five microRNAs were found that can robustly distinguish ccRCC from their patient-matched normal kidney tissue samples with high confidence. Among this set of 35 signature microRNAs, 26 were found to be consistently downregulated and 9 consistently upregulated in ccRCC relative to normal kidney samples. Two microRNAs, namely, MiR-155 and miR-21, commonly found to be upregulated in other cancers, and miR-210, induced by hypoxia, were also identified as overexpressed in ccRCC in our study. MicroRNAs identified as downregulated in our study can be correlated to common chromosome deletions in ccRCC. CONCLUSIONS: Our analysis is a comprehensive, statistically relevant study that identifies the microRNAs dysregulated in ccRCC, which can serve as the basis of molecular markers for diagnosis.