Identification of m5C-related genes and subclusters in recurrent pregnancy loss.

Purpose: Recurrent pregnancy loss (RPL), which occurs in 1-5% of couples and nearly half of the cases remain unexplained, is a complex condition influenced by multiple factors. Previous investigations have demonstrated the role of m5C-related genes (MRGs) in cancer prognosis and the significance of epigenetic modifications during pregnancy. However, the connection between MRGs and the pathogenesis of RPL remains elusive. This study endeavors to elucidate this relationship through bioinformatics approaches.

Methods: Data of 48 endometrial tissue samples were obtained through GEO query. Twenty-one MRGs were analyzed. Multiple machine learning (ML) methods were applied to identify biomarkers. A nomogram was constructed, and further analyses like GSEA and scRNA-seq were carried out using the R software.

Results: Five core biomarkers (DNMT1, SMUG1, ZBTB38, MBD4, and TDG) were pinpointed by ML methods, with the prediction model achieving an AUC of 0.953. Based on hub genes, 24 RPL samples were grouped into cluster A (n = 9) and cluster B (n = 15). The study revealed differences in immune cells and microenvironments, and the scRNA-seq analysis confirmed the connection between immune cells and m5C.

Conclusion: This study identified five key m5C-related genes, unraveled their link to immune cells, and developed an accurate RPL diagnostic model. The RPL patients are innovatively divided into two clusters, and the difference of their immune microenvironment is analyzed. This study offers a fresh perspective for examining biomarkers and potential therapeutic targets for RPL.