Accurate molecular classification of renal tumors using microRNA expression.

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.

MiR-92b and miR-9/9* are specifically expressed in brain primary tumors and can be used to differentiate primary from metastatic brain tumors.

A recurring challenge for brain pathologists is to diagnose whether a brain malignancy is a primary tumor or a metastasis from some other tissue. The accurate diagnosis of brain malignancies is essential for selection of proper treatment. MicroRNAs are a class of small non-coding RNA species that regulate gene expression; many exhibit tissue-specific expression and are misregulated in cancer. Using microRNA expression profiling, we found that hsa-miR-92b and hsa-miR-9/hsa-miR-9* are over-expressed, specifically in brain primary tumors, as compared to primary tumors from other tissues and their metastases to the brain. By considering the expression of only these two microRNAs, it is possible to distinguish between primary and metastatic brain tumors with very high accuracy. These microRNAs thus represent excellent biomarkers for brain primary tumors. Previous reports have found that hsa-miR-92b and hsa-miR-9/hsa-miR-9* are expressed more strongly in developing neurons and brain than in adult brain. Thus, their specific over-expression in brain primary tumors supports a functional role for these microRNAs or a link between neuronal stem cells and brain tumorigenesis.