Molecular subtype identification and predictive power of N6-methyladenosine regulator in unexplained recurrent pregnancy loss

Abstract

The etiology of recurrent pregnancy loss (RPL) is complicated and effective clinical preventive measures are lacking. Identifying biomarkers for RPL has been challenging, and to date, little is known about the role of N6-methyladenosine (m6A) regulators in RPL. Expression data for m6A regulators in 29 patients with RPL and 29 healthy controls were downloaded from the Gene Expression Omnibus (GEO) database. To establish a diagnostic model for unexplained RPL, differential gene expression analysis was conducting for 36 m6A regulators using least absolute shrinkage and selection operator (LASSO) regression. Unsupervised cluster analysis was conducted on hub genes, and probable mechanisms were explored using gene set enrichment analysis (GSEA) and gene ontology (GO) analysis. Correlations between m6A-related differentially expressed genes and immune infiltration were analyzed using single-sample GSEA. A total of 18 m6A regulators showed significant differences in expression in RPL: 10 were upregulated and eight were downregulated. Fifteen m6A regulators were integrated and used to construct a diagnostic model for RPL that had good predictive efficiency and robustness in differentiating RPL from control samples, with an overall area under the curve (AUC) value of 0.994. Crosstalk was identified between 10 hub genes, miRNAs, and transcription factors (TFs). For example, YTHDF2 was targeted by mir-1-3p and interacted with embryonic development-related TFs such as FOXA1 and GATA2. YTHDF2 was also positively correlated with METTL14 (r = 0.5983, p < 0.001). Two RPL subtypes (Cluster-1 and Cluster-2) with distinct hub gene signatures were identified. GSEA and GO analysis revealed that the differentially expressed genes were mainly associated with immune processes and cell cycle signaling pathway (normalized enrichment score, NES = -1.626, p < 0.001). Immune infiltration was significantly higher in Cluster-1 than in Cluster-2 (p < 0.01). In conclusion, we demonstrated that m6A modification plays a critical role in RPL. We also developed and validated a diagnostic model for RPL prediction based on m6A regulators. Finally, we identified two distinct RPL subtypes with different biological processes and immune statuses.