Quantitative proteomics reveals pregnancy prognosis signature of polycystic ovary syndrome women based on machine learning

We aimed to screen and construct a predictive model for pregnancy loss in polycystic ovary syndrome (PCOS) patients through machine learning methods. We obtained the endometrial samples from 33 PCOS patients and 7 healthy controls at the Reproductive Center of the Second Hospital of Lanzhou University from September 2019 to September 2020. Liquid chromatography tandem mass spectrometry (LCMS/MS) was conducted to identify the differentially expressed proteins (DEPs) of the two groups. Gene Ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to analyze the related pathways and functions of the DEPs. Then, we used machine learning methods to screen the feature proteins. Multivariate Cox regression analysis was also conducted to establish the prognostic models. The performance of the prognostic model was then evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). In addition, the Bootstrap method was conducted to verify the generalization ability of the model. Finally, linear correlation analysis was performed to figure out the correlation between the feature proteins and clinical data. Four hundred and fifty DEPs in PCOS and controls were screened out, and we obtained some pathways and functions. A prognostic model for the pregnancy loss of PCOS was established, which has good discrimination and generalization ability based on two feature proteins (TIA1, COL5A1). Strong correlation between clinical data and proteins were identified to predict the reproductive outcome in PCOS. The model based on the TIA1 and COL5A1 protein could effectively predict the occurrence of pregnancy loss in PCOS patients and provide a good theoretical foundation for subsequent research.

本研究旨在通过机器学习方法筛选并构建多囊卵巢综合征（PCOS）患者妊娠丢失的预测模型。研究人员从兰州大学第二医院生殖中心于2019年9月至2020年9月期间，收集了33名PCOS患者及7名健康对照者的子宫内膜样本。采用液相色谱串联质谱法（LCMS/MS）鉴定两组间的差异表达蛋白（DEPs）。对DEPs的相关通路和功能进行了基因本体（GO）及京都基因与基因组百科全书（KEGG）富集分析。随后，运用机器学习方法筛选特征蛋白。同时，通过多变量Cox回归分析建立了预后模型。利用受试者工作特征（ROC）曲线、校准曲线及决策曲线分析（DCA）评估了预后模型的性能。此外，采用Bootstrap方法验证了模型的泛化能力。最后，通过线性相关性分析揭示了特征蛋白与临床数据之间的关联。共筛选出450个PCOS及对照组的差异表达蛋白，并确定了相关通路和功能。基于特征蛋白TIA1和COL5A1建立了PCOS妊娠丢失的预后模型，该模型具有良好的鉴别和泛化能力。临床数据与蛋白之间显著的相关性被识别，以预测PCOS的生育结果。基于TIA1和COL5A1蛋白的模型能够有效预测PCOS患者的妊娠丢失发生，并为后续研究提供了良好的理论基础。