Integrative analysis of hub genes for recurrent pregnancy loss with antiphospholipid syndrome: integrated bioinformatics analysis, machine learning and experimental validation

Background: Recurrent Pregnancy Loss (RPL) is significantly associated with Antiphospholipid Syndrome (APS), yet the shared pathogenic mechanism remains unclear. This study identifies potential hub genes for APS-related RPL, providing insights into its pathogenesis and potential diagnostic strategies. Methods: We retrieved APS and RPL datasets from the GEO database and performed differential expression analysis to identify differentially expressed genes. Thereafter, Gene Ontology (GO) analysis, pathway enrichment analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) analysis were carried out. For hub gene screening, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Gaussian mixture model (GMM), and RandomForest (RF) algorithms were applied to analyze the gene expression profiles of RPL. The CIBERSORT tool was utilized to assess the immune cell infiltration levels in samples from RPL and APS patients, while the "clusterProfiler" R package was employed to perform single-gene Gene Set Enrichment Analysis (GSEA) for each hub gene in both cohorts. Three hub genes were validated for diagnostic performance via receiver operating characteristic (ROC) curves, with a nomogram model subsequently constructed and its efficacy assessed using ROC and calibration curves. Additionally, the Comparative Toxicogenomics Database was used to explore the associations between these hub genes and pregnancy-related diseases. Finally, quantitative polymerase chain reaction (qPCR) and cell function experiments were conducted to validate the expression and functional characteristics of the hub genes. Results: In this study, RPL and APS datasets were obtained from Gene Expression Omnibus (GEO) database, 10 common differentially expressed genes(DEGs)were identified, including eight downregulated and two upregulated genes. The analysis of these shared DEGs indicated that the imbalance of immune system-associated cells and molecules might represent a common characteristic in the pathophysiological processes of both APS and RPL. Through machine learning and the construction of a nomogram, we identified NAA30, ARHGAP44, and SUGT1 as hub genes. Further experiments revealed that SUGT1 was downregulated in RPL with APS and could influence the biological behavior of trophoblast cells. Conclusion: The present study provides valuable insights into the molecular mechanisms underlying RPL with APS, while also identifying potential biomarkers and therapeutic targets for this disease.