Accurate molecular classification of renal tumors using microRNA expression.

Abstract

Subtypes of renal tumors have different genetic backgrounds, prognoses, and responses to surgical and medical treatment, and their differential diagnosis is a frequent challenge for pathologists. New biomarkers can help improve the diagnosis and hence the management of renal cancer patients. We extracted RNA from 71 formalin-fixed paraffin-embedded (FFPE) renal tumor samples and measured expression of more than 900 microRNAs using custom microarrays. Clustering revealed similarity in microRNA expression between oncocytoma and chromophobe subtypes as well as between conventional (clear-cell) and papillary tumors. By basing a classification algorithm on this structure, we followed inherent biological correlations and could achieve accurate classification using few microRNAs markers. We defined a two-step decision-tree classifier that uses expression levels of six microRNAs: the first step uses expression levels of hsa-miR-210 and hsa-miR-221 to distinguish between the two pairs of subtypes; the second step uses either hsa-miR-200c with hsa-miR-139-5p to identify oncocytoma from chromophobe, or hsa-miR-31 with hsa-miR-126 to identify conventional from papillary tumors. The classifier was tested on an independent set of FFPE tumor samples from 54 additional patients, and identified correctly 93% of the cases. Validation on qRT-PCR platform demonstrated high correlation with microarray results and accurate classification. MicroRNA expression profiling is a very effective molecular bioassay for classification of renal tumors and can offer a quantitative standardized complement to current methods of tumor classification.

DOI: 10.2353/jmoldx.2010.090187

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@article{Fridman2010AccurateMC, title={Accurate molecular classification of renal tumors using microRNA expression.}, author={Eddie Fridman and Zohar A. Dotan and Iris Barshack and Miriam Ben David and Avital Dov and Sarit Tabak and Orit Zion and Sima Benjamin and Hila Benjamin and Hagit Kuker and Camila Avivi and Kinneret Rosenblatt and Sylvie Polak-Charcon and Jacob Ramon and Nitzan Rosenfeld and Yael Spector}, journal={The Journal of molecular diagnostics : JMD}, year={2010}, volume={12 5}, pages={687-96} }