3 CpG Methylation Biomarkers for the Diagnosis of Polycystic Ovary
Syndrome (PCOS) in Blood Samples

Linling       Xie; Xiaotao       Jiang; Yi       Chen; Cihui       Huang; Yanfen       Chen; Guantong       Liu; Wenxi       Sun; Lei       Zeng; Ruling       Lu

doi:10.2174/1386207321666210602170054

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.

Keywords: Diagnostic markers, methylation, polycystic ovary syndrome, LASSO regression, WGCNA, CPG.

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DOI https://dx.doi.org/10.2174/1386207321666210602170054	Print ISSN 1386-2073
Publisher Name Bentham Science Publisher	Online ISSN 1875-5402

Combinatorial Chemistry & High Throughput Screening

3 CpG Methylation Biomarkers for the Diagnosis of Polycystic Ovary Syndrome (PCOS) in Blood Samples

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