What question did this study set out to answer?

To investigate the impact of N6-methyladenosine-related SNPs on polycystic ovary syndrome and their regulatory roles.

April 14, 2026Open Access

Integrative eQTL and Multi-Omics Analysis Reveals the Role of N6-Methyladenosine Modification in Polycystic Ovary Syndrome and Predictive Model Construction

Key Points

To investigate the impact of N6-methyladenosine-related SNPs on polycystic ovary syndrome and their regulatory roles.
Analyzed RNA sequencing data from PCOS patients and healthy controls.
Identified significant m6A-SNPs using eQTL analysis.
Conducted enrichment analyses with Gene Ontology and KEGG.
Estimated immune cell infiltration using CIBERSORT.
Developed a logistic-regression model to evaluate diagnostic performance.
Identified 362 differentially expressed genes related to PCOS.
Found 45 candidate m6A-SNPs associated with PCOS.
Enrichment analysis highlighted roles in cell cycle dysregulation and interferon signaling.
Determined that immune infiltration correlated with specific immune subsets.
Achieved an AUC of 0.836 in the predictive model integrating gene expression and immune data.

Abstract

Mengqi Jin, Sujuan Xi, Lin Ma The Reproductive Medical Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, Peopleâs Republic of ChinaCorrespondence: Lin Ma, The Reproductive Medical Center, The Seventh Affiliated Hospital, Sun Yat-sen University, No. 628, Zhenyuan Road, Xinhu Street, Guangming District, Shenzhen, Guangdong, Peopleâs Republic of China, Email malin8@mail.sysu.edu.cnObjective: Polycystic ovary syndrome (PCOS) is a heterogeneous disorder with incompletely understood epigenetic regulation. We investigated the role of N6-methyladenosine (m6A)-related single nucleotide polymorphisms (m6A-SNPs) in PCOS.Methods: Bulk RNA sequencing (RNA-seq) data from the GSE277906 dataset, comprising 23 PCOS patients and 17Â healthy controls, was analyzed for differential expression. m6A-SNPs with significant expression quantitative trait locus (eQTL) signals were obtained by integrating eQTLGen and RMVar. Gene Ontology, KEGG, and Reactome supported enrichment analyses. Immune infiltration was estimated with CIBERSORT. Logistic-regression models were built based on the entire cohort without data splitting and evaluated using receiver operating characteristic (ROC) curves.Results: A total of 362 differentially expressed genes and 45 PCOS-related candidate m6A-SNPs were identified. Enrichment analysis revealed that these genes were mainly involved in cell cycle dysregulation and interferon-Î±/Î² signaling pathways. Immune infiltration analysis showed no extensive remodeling of the overall immune landscape in PCOS, but correlation analysis identified significant associations between key genes and specific immune subsets, including monocytes, dendritic cells, and M2 macrophages. The predictive model integrating gene expression and immune cell infiltration achieved the highest diagnostic value (AUC = 0.836), outperforming models based on single features. Moreover, genomic annotation of the core gene SECTM1 indicated an open chromatin state and potential regulation by a complex transcriptional network.Conclusion: m6A-SNPs likely contribute to PCOS pathogenesis via gene-regulatory effects. The integrative model shows high diagnostic promise, and SECTM1 emerges as a potential candidate for further functional validation and diagnostic exploration.Keywords: polycystic ovary syndrome, m6A-SNPs, immune microenvironment

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Cite This Study

Jin et al. (Wed,) studied this question.

synapsesocial.com/papers/69ddd8eee195c95cdefd66ab https://doi.org/https://doi.org/10.2147/ijwh.s594655

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