Biomarker identification and mechanism of polycystic ovary syndrome based on multi-omics analysis
10.11665/j.issn.1000-5048.2025030301
- VernacularTitle:基于多组学分析的多囊卵巢综合征生物标志物及其机制
- Author:
Xinna LIU
1
;
Mengqun LIU
;
Jiwen WANG
;
Junbiao YANG
;
Xuzhe ZHOU
;
Chen LIU
;
Mingxuan LI
;
Ying WANG
Author Information
1. 中国药科大学生命科学与技术学院, 南京 211198;南京中医药大学药学院, 南京 210023;江南大学生命科学与健康工程学院, 无锡 214122
- Publication Type:Journal Article 期刊文章
- Keywords:
polycystic ovary syndrome;
serum biomarker;
letrozole;
metabolomics;
proteomics
- From:
Journal of China Pharmaceutical University
2025;56(5):634-644
- CountryChina
- Language:Chinese
-
Abstract:
Based on a letrozole-induced rat model of polycystic ovary syndrome (PCOS), serum metabolomics was employed to characterize metabolic abnormalities, identify potential biomarkers, and investigate their roles in the pathogenesis and progression of PCOS. Metabolomic analyses revealed significantly decreased levels of cholesterol, pregnenolone, leucine, and citrate in the serum of model rats, accompanied by elevated levels of androsterone glucuronide (ADTG) and linoleic acid, indicating dysregulation of key pathways including steroid biosynthesis, branched-chain amino acid metabolism, tricarboxylic acid (TCA) cycle, and lipid metabolism. To elucidate the tissue origins and molecular mechanisms underlying these metabolic alterations, ovarian proteomics and qRT-PCR analyses were further integrated. The results confirmed the upregulation of key enzymes involved in the related metabolic pathways, such as 17α-hydroxylase (CYP17A1), 11β-hydroxysteroid dehydrogenase (HSD11B1), branched-chain amino acid aminotransferase (BCAT2), fatty acid desaturase 2 (FADS2), and oxoglutarate dehydrogenase (OGDH). These findings suggest that both “cholesterol precursor depletion-androgen accumulation” and “energy/lipid metabolic reprogramming” constitute core features of metabolic disturbances in PCOS. Through multi-omic cross-validation, six serum metabolites with high stability and clinical translational potential were identified as promising combinational biomarkers for the auxiliary diagnosis of PCOS. This study employed a metabolomics-guided strategy, supported by proteomic and transcriptomic validation, which has not only deepened our understanding of PCOS metabolic mechanisms but also provided us with a theoretical foundation for its auxiliary diagnosis.
