Recurrent Miscarriage: A machine learning approach to uncover key genes and immune Infiltration

Abstract

Abstract Objective: Recurrent miscarriage (RM), defined as the failure to maintain more than two clinical pregnancies beyond the 20th week of gestation, remains a complex pathological condition with unclear underlying mechanisms. This study aimed to elucidate potential biomarkers and explore the extent of immune infiltration in RM, to inform and facilitate effective clinical treatments. Methods: Leveraging the GSE76862 and GSE26787 datasets from the Gene Expression Omnibus (GEO) database, we implemented Weighted Gene Co-expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) networks to identify five key genes (F2, EGF, NGF, IL13, and FOXP3). These genes showed a robust correlation with RM. Receiver Operating Characteristic (ROC) curve analysis, coupled with validation from external datasets (GSE26787 and GSE22490), demonstrated the high diagnostic accuracy of these key genes for RM. RT-PCR was employed to validate the expression of these key genes in RM samples. We further evaluated immune cell infiltration in RM tissues using the CIBERSORT package and examined the relationship between the expression levels of the five key genes and immune cell infiltration. Furthermore, we interrogated correlations between key genes and immune factors from the TISIDB database to unravel the roles of these key genes in the immune mediation of RM. Finally, through Gene Set Variation Analysis (GSVA), Circos analysis, and GeneMANIA, we delved into the roles of the key genes, anticipated gene interactions, and gained insights into the molecular mechanisms driving RM. Conclusion: Our findings underscore that the five identified key genes (F2, EGF, NGF, IL13, and FOXP3) have intricate links with RM and could play pivotal roles in deciphering the molecular mechanisms underlying RM.