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

Abstract

Objective

We aimed to screen and construct a predictive model for pregnancy loss in polycystic ovary syndrome (PCOS) patients through machine learning methods.

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.

Results

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.

Conclusion

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.

Keywords:

• Polycystic ovary syndrome
• prognostic model
• quantitative proteomics
• machine learning
• TIA1
• COL5A1

Ethics statement

The studies involving human participants were reviewed and approved by the Ethics Committee of Lanzhou University Second Hospital. All methods were performed according to the Declaration of Helsinki. The patients/participants provided their written informed consent to participate in this study.

Authors’ contributions

YYW and CL supervised the whole study, designed the concept, analyzed the data, YYW wrote the manuscript. CL edited the final manuscript. YYW, FW, and JGH collected and analyzed the data. All authors read and approved the final manuscript.

Disclosure statement

The authors declare that they have no competing interests.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession numbers can be found below: The mass spectrometry proteomics data have been deposited to the ProteomeXchange consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD032383.

Additional information

Funding

This work was funded by the National Natural Science Foundation of China (Grant No. 81960515), the Science Foundation of Lanzhou University (Grant No. 054000229).