Abstract
Increasing evidence suggests that mitophagy is crucially involved in the progression of polycystic ovary syndrome (PCOS). Exploration of PCOS-specific biomarkers related to mitophagy is expected to provide critical insights into disease pathogenesis. In this study, we employed bioinformatic analyses and machine learning algorithms to determine novel biomarkers for PCOS that may be tied with mitophagy. A grand total of 12 differential expressed mitophagy-related genes (DE-MRGs) associated with PCOS were identified. TOMM5 and MAP1LC3A among the 12 DE-MRGs were recognized as potential marker genes by LASSO, RF and SVM-RFE algorithms. The area under the ROC curve (AUROC) of MAP1LC3A were all greater than 0.8 both in the training set and validation sets. The CIBERSORT analysis indicated a potential association between alterations in the immune microenvironment of PCOS individuals and MAP1LC3A expression. In addition, we found that MAP1LC3A was positively related to the testosterone levels of PCOS patients. Overall, MAP1LC3A was identified as optimal PCOS-specific biomarkers related to mitophagy. Our findings created a diagnostic strength and offered a perspective for investigating the mitophagy process in PCOS.