Potential of utilizing specific miRNAs as biomarkers for polycystic ovarian syndrome (PCOS)

Abstract

Polycystic Ovarian Syndrome is the one of the leading causes of infertility among women who are of child-bearing age. The syndrome’s vast range of phenotypes has made it challenging for researchers to not only consistently diagnose but also discover a cure. Currently, there are several proposed treatments being looked into, however, much of the research focuses on employing promising biomarkers, micro ribonucleic acids (miRNAs), that can potentially aid in diagnosis. The four prominent locations of research for these biomarkers include: ovarian tissues specifically looking into granulosa cells (GC), adipose tissue, follicular fluid, and the serum. My goal is to determine which of these areas holds the most promise to diagnose this syndrome in the years to come. This study reviewed a large collection of the current polycystic ovarian syndrome literature evaluating both reported miRNAs and how viable those would be as potential biomarkers to use for the future. The data showed that a majority of these promising biomarkers were found in granulosa cells, adipose tissue, and follicular fluid. Although there were miRNAs that were deemed promising in the serum, research is still far from conclusive in using these miRNAs as biomarkers for diagnosis of polycystic ovarian syndrome. By comparing the miRNAs selected from each type of location, I was able to conclude that miR-21, miR-93, miR-223, and miR-let-7b hold the most promise for the potential to become biomarkers for polycystic ovarian syndrome in the near future. Currently, there is a lot of research particularly surrounding these miRNAs and how they were shown to have been expressed in statistically significant levels among women with the syndrome. However, because of their complexity, miRNAs do not regulate one single pathway, it is hard to describe a mechanism that explains the pathophysiology of the syndrome. I believe we are still far away from successfully zooming in on one biomarker. By determining the most potential biomarker(s), we can focus resources and efforts towards finding a better diagnostic tool for this syndrome