Title:3 CpG Methylation Biomarkers for the Diagnosis of Polycystic Ovary
Syndrome (PCOS) in Blood Samples
Volume: 25
Issue: 8
Author(s): Linling Xie, Xiaotao Jiang, Yi Chen, Cihui Huang, Yanfen Chen, Guantong Liu, Wenxi Sun, Lei Zeng*Ruling Lu*
Affiliation:
- Guangzhou University of Chinese Medicine, Guangzhou 515000, China
- Department of Obstetrics and Gynecology,
First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 515000, China
- Lingnan Medical
Research Center of Guangzhou University of Chinese Medicine, Guangzhou, 515000, China
- Guangzhou University of Chinese Medicine, Guangzhou 515000, China
- Department of Obstetrics and Gynecology,
First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 515000, China
- Lingnan Medical
Research Center of Guangzhou University of Chinese Medicine, Guangzhou, 515000, China
Keywords:
Diagnostic markers, methylation, polycystic ovary syndrome, LASSO regression, WGCNA, CPG.
Abstract:
Background: Polycystic ovary syndrome (PCOS) is a common endocrine disease in
women that seriously interferes with patient's metabolic and reproductive functions. The current
diagnostic criteria for PCOS are expert-based and still disputed. Previous studies have identified
changes in DNA methylation in peripheral blood of women with PCOS, but their diagnostic
potential for PCOS remains to be studied.
Objective: The present study aimed to identify potential methylation biomarkers for the diagnosis
of PCOS in blood.
Methods: Methylation profiling of peripheral blood was downloaded from a public database, Gene
Expression Omnibus (GEO), including 30 PCOS patients (diagnosed with the revised 2003
Rotterdam consensus criteria) and 30 age-matched healthy women recruited from Centre of
Reproductive Medicine, Linyi People’s Hospital, Shandong, China. Weighted gene co-expression
network analysis (WGCNA) was utilized to identify PCOS-related co-methylation CpG sites (co-
MPs). Functional enrichment analysis was performed on the localized genes of PCOS-related co-
MPs. The least absolute shrinkage and selection operator (LASSO) regression was used to screen
out CpG methylation signatures for PCOS diagnosis, and receiver operating characteristic (ROC)
analysis was conducted to evaluate their diagnostic accuracy. To assess the accuracy of the
combination of the investigated indicators, multivariate ROC analysis was performed on the
predicted probability values obtained using binary logistic regression on the methylation levels of
selected CpGs.
Results: Seven co-methylation modules were obtained, among which the turquoise module is the
most relevant to PCOS, containing 194 co-MPs. The genes that these co-MPs located in were
mainly associated with the immune-related pathway. According to LASSO regression, three Co-
MPs (cg23464743, cg06834912, cg00103771) were identified as potential diagnostic biomarkers
of PCOS. ROC analysis showed an AUC (area under curve) of 0.7556 (sensitivity 60.0%,
specificity 83.3%) for cg23464743, 0.7822 (sensitivity 70.0%, specificity 80.0%) for cg06834912,
and 0.7611 (sensitivity 63.3%, specificity 83.3%) for cg00103771. The diagnostic accuracy of the
combination of these 3 indicators presented to be higher than any single one of them, with the
AUC of 0.8378 (sensitivity 73.3%, specificity 93.3%).
Conclusion: The combination of 3 CpG methylation signatures in blood was identified with a good
diagnostic accuracy for PCOS, which may bring new insight into the development of PCOS
diagnostic markers in the future.