COMPUTATIONAL BIOLOGY APPROACHES AND BIOINFORMATICS TO IDENTIFY KEY GENES IN POLYCYSTIC OVARY SYNDROME: A SYSTEMATIC REVIEW

Background & Aims:

Polycystic ovary syndrome (PCOS) is the most common female endocrine disease and often causes infertility in the women of reproductive age. This syndrome includes different genes and proteins, multiple pathways, and complex processes of hormone secretion. Therefore, a single factor cannot explain the pathogenesis of PCOS. Using computational biology and omicses including genomics, transcriptomics, proteomics, and metabolomics, can provide faster and more effective methods for studying the pathogenesis of complex diseases such as PCOS.

Materials & Methods: 

In this study, to find the related studies, PubMed, Google Scholar, and Science Direct databases were searched without time constraints for 3 years using the keywords of "Polycystic Ovary Syndrome, Computational Biology, protein-protein interaction, Network Biology, and Pathways analysis".

Various databases have been designed and made available to the public to repository data, Protein-Protein interactions, networks, and biological pathways related to humans. Three databases PCOSBase, PCOSKB, and PCOSDB have been explicitly developed for polycystic ovary syndrome.

Since the molecular mechanisms of polycystic ovary syndrome are still not completely understood, to realize this syndrome, besides experimental results using omics platforms, computational biology, and bioinformatics tools, it is necessary to identify the interaction between proteins and the pathways involved in it.