Your browser doesn't support javascript.
loading
Quantitative proteomics reveals pregnancy prognosis signature of polycystic ovary syndrome women based on machine learning.
Wu, Yuanyuan; Liu, Cai; Huang, Jinge; Wang, Fang.
Affiliation
  • Wu Y; Traditional Chinese and Western Medicine, Gansu University of Chinese Medicine, Lanzhou, China.
  • Liu C; Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, China.
  • Huang J; Traditional Chinese and Western Medicine, Gansu University of Chinese Medicine, Lanzhou, China.
  • Wang F; Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, China.
Gynecol Endocrinol ; 40(1): 2328613, 2024 Mar 08.
Article in En | MEDLINE | ID: mdl-38497425
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.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Polycystic Ovary Syndrome Limits: Female / Humans / Pregnancy Language: En Journal: Gynecol Endocrinol / Gynecol. endocrinol / Gynecological endocrinology Journal subject: ENDOCRINOLOGIA / GINECOLOGIA Year: 2024 Document type: Article Affiliation country: China Country of publication: Reino Unido

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Polycystic Ovary Syndrome Limits: Female / Humans / Pregnancy Language: En Journal: Gynecol Endocrinol / Gynecol. endocrinol / Gynecological endocrinology Journal subject: ENDOCRINOLOGIA / GINECOLOGIA Year: 2024 Document type: Article Affiliation country: China Country of publication: Reino Unido