Autophagy Proteins and clinical data reveal the prognosis of polycystic ovary syndrome

Abstract

Abstract Objective We aimed to investigate the significance of autophagy proteins and their association with clinical data on pregnancy loss in polycystic ovary syndrome (PCOS), while also constructing predictive models. Methods This study was a secondary analysis. we collected endometrial samples from 33 patients with polycystic ovary syndrome (PCOS) and 7 patients with successful pregnancy control women at the Reproductive Center of the Second Hospital of Lanzhou University between September 2019 and September 2020. Liquid chromatography tandem mass spectrometry was employed to identify expressed proteins in the endometrium of 40 patients. R was use to identify differential expression proteins(DEPs). Subsequently, Metascape was utilized for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Multivariate Cox analysis was performed to analyze autophagy proteins associated with reproductive outcomes, while logistic regression was used for analyzing clinical data. Linear correlation analysis was conducted to examine the relationship between autophagy proteins and clinical data. We established prognostic models and constructed the nomograms based on proteome data and clinical data respectively. The performance of the prognostic model was evaluated by the receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Results A total of 5331 proteins were identified, with 450 proteins exhibiting significant differential expression between the PCOS and control groups. A prognostic model for autophagy protein was developed based on three autophagy proteins (ARSA, ITGB1, and GABARAPL2). Additionally, another prognostic model for clinical data was established using insulin, TSH, TPOAB, and VD3. Our findings revealed a significant positive correlation between insulin and ARSA (R = 0.49), as well as ITGB1 (R = 0.3). Conversely, TSH exhibited a negative correlation with both ARSA (-0.33) and ITGB1 (R = -0.26). Conclusion Our research could effectively predict the occurrence of pregnancy loss in PCOS patients and provide a basis for subsequent research.