Molecular Diagnosis of Polycystic Ovary Syndrome in Obese and Non-Obese Women by Targeted Plasma miRNA Profiling.

Polycystic ovary syndrome is diagnosed based on clinical signs, but its presentation is heterogeneous and potentially confounded by concurrent conditions, as obesity and insulin-resistance. MicroRNAs have recently emerged as putative pathophysiological and diagnostic factors in PCOS. However, no reliable miRNA-based method for molecular diagnosis of PCOS has been reported. The aim of this study was to develop a tool for accurate diagnosis of PCOS by targeted miRNA profiling of plasma samples, defined on the basis of unbiased biomarker-finding analyses and biostatistical-tools.
A case-control PCOS cohort was cross-sectionally studied, including 170 women classified into four groups: non-PCOS/lean; non-PCOS/obese; PCOS/lean; and PCOS/obese women. High-throughput miRNA analyses were performed in plasma, using NanoString technology and a 800-human-miRNA panel, followed by targeted-qPCR validation. Statistics were applied to define optimal normalization methods, identify deregulated biomarker miRNAs and build classification algorithms, considering PCOS and obesity as major categories.
The geometric mean of circulating hsa-miR-103a-3p, hsa-miR-125a-5p and hsa-miR-1976, selected among 125 unchanged miRNAs, was defined as optimal reference for internal normalization (named mR3-method). Ten miRNAs were identified and validated after mR3-normalization as differentially expressed across the groups. Multinomial LASSO-Regression and decision-tree models were built to reliably discriminate PCOS vs. non-PCOS, either in obese or non-obese women, using subsets of these miRNAs as performers.
We define herein a robust method for molecular classification of PCOS, based on unbiased identification of miRNA biomarkers and decision-tree protocols. This method allows not only reliable diagnosis of non-obese women with PCOS, but also discrimination between PCOS and obesity.