Prediction of the Signaling Pathway in Polycystic Ovary Syndrome Using an Integrated Bioinformatics Approach

Abstract

Objectives: The purpose of this study was to define the underlying biological mechanisms of polycystic ovarian syndrome (PCOS) utilizing the protein-protein interaction networks (PPINs) that were constructed based on the putative disease-causing genes for PCOS.

Design: No animals were used in this research because this is an in silico study that mainly uses software and online analysis tools. Participants/Materials, Settings: Gene datasets related to PCOS were obtained from Genecards.

Methods: The PPINs of PCOS were created using the String Database after genes related to PCOS were obtained from Genecards. After that, we performed an analysis of the hub-gene clusters extracted from the PPIN using the ShinyGO algorithm. In the final step of this research project, functional enrichment analysis was used to investigate the primary biological activities and signaling pathways that were associated with the hub clusters.

Results: The Genecards database provided the source for the identification of a total of 1,072 potential genes related to PCOS. The PPIN that was generated by using the genes that we collected above contained a total of 82 genes and three different types of cluster interaction interactions. In addition, after conducting research on the PPIN with the shinyGO plug-in, 19 of the most important gene clusters were discovered. The primary biological functions that were enriched in the key clusters that were developed were ovarian steroidogenesis, the breast cancer pathway, regulation of lipid and glucose metabolism by the AMPK pathway, and ovarian steroidogenesis. The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated with the pathogenesis of PCOS.

Limitations: Several of the significant genes that were identified in this study, such as ACVR1, SMAD5, BMP6, SMAD3, SMAD4, and anti-mullerian hormone. It is necessary to do additional research using large samples, several centers, and multiple ethnicities in order to verify these findings.

Conclusions: The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated with the pathogenesis of PCOS. This information may possibly bring unique insights for the treatment of PCOS as well as the investigation of its underlying pathogenic mechanism.

Keywords: Bioinformatics; Obstetric gynecology; Pathway analysis; Polycystic ovarian syndrome; System biology.