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Identification of a 4-microRNA signature for clear cell renal cell carcinoma metastasis and prognosis
Authors:Wu Xiwei  Weng Lihong  Li Xuejun  Guo Chao  Pal Sumanta K  Jin Jennifer M  Li Yuping  Nelson Rebecca A  Mu Bing  Onami Susan H  Wu Jeffrey J  Ruel Nora H  Wilczynski Sharon P  Gao Hanlin  Covarrubias Maricela  Figlin Robert A  Weiss Lawrence M  Wu Huiqing
Institution:Department of Molecular Medicine, City of Hope National Medical Center and Beckman Research Institute, Duarte, California, United States of America.
Abstract:Renal cell carcinoma (RCC) metastasis portends a poor prognosis and cannot be reliably predicted. Early determination of the metastatic potential of RCC may help guide proper treatment. We analyzed microRNA (miRNA) expression in clear cell RCC (ccRCC) for the purpose of developing a miRNA expression signature to determine the risk of metastasis and prognosis. We used the microarray technology to profile miRNA expression of 78 benign kidney and ccRCC samples. Using 28 localized and metastatic ccRCC specimens as the training cohort and the univariate logistic regression and risk score methods, we developed a miRNA signature model in which the expression levels of miR-10b, miR-139-5p, miR-130b and miR-199b-5p were used to determine the status of ccRCC metastasis. We validated the signature in an independent 40-sample testing cohort of different stages of primary ccRCCs using the microarray data. Within the testing cohort patients who had at least 5 years follow-up if no metastasis developed, the signature showed a high sensitivity and specificity. The risk status was proven to be associated with the cancer-specific survival. Using the most stably expressed miRNA among benign and tumorous kidney tissue as the internal reference for normalization, we successfully converted his signature to be a quantitative PCR (qPCR)-based assay, which showed the same high sensitivity and specificity. The 4-miRNA is associated with ccRCC metastasis and prognosis. The signature is ready for and will benefit from further large clinical cohort validation and has the potential for clinical application.
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