Multi-omics profiling identifies molecular associations in recurrent pregnancy loss

Recurrent pregnancy loss (RPL) is a heterogeneous reproductive disorder in which dysregulation of maternal–fetal immune tolerance, aberrant decidual immune remodeling, and altered inflammasome signaling have been implicated within a complex multi-omics landscape. Multi-omics profiling (genomics, epigenomics, single-cell/spatial transcriptomics, proteomics, metabolomics, microbiome analyses, and immunomics) is increasingly being used to characterize mechanistic heterogeneity in RPL and to support biomarker discovery and immune-informed stratification. Genomic studies have associated chromosomal abnormalities and pathogenic variants with early embryonic developmental failure, while epigenomic profiling has highlighted aberrant methylation patterns and imprinting disturbances. Single-cell and spatial transcriptomics have revealed altered cellular composition and disrupted communication among decidual stromal cells, uterine natural killer (uNK) cells, macrophages, regulatory T cells (Treg), T helper 17 (Th17) cells, and trophoblast lineages. Proteomic and metabolomic studies have further identified immune–metabolic signatures associated with impaired trophoblast function and vascular remodeling, while emerging microbiome studies suggest a gut–reproductive axis that may modulate systemic immune homeostasis. Integration of multi-omics datasets with computational frameworks (e.g., weighted gene co-expression network analysis (WGCNA), multi-omics factor analysis (MOFA), and deep-learning models may improve RPL subtype classification, risk prediction, and the identification of potentially actionable pathways. However, current studies remain limited by small cohort sizes, especially in single-cell datasets, cross-platform heterogeneity, insufficient longitudinal validation, and a lack of multicenter reproducibility. Future work should prioritize standardized multi-omics pipelines, clearer evidence stratification, and immune-centric analytical frameworks to improve the robustness and translational relevance of RPL research. These advances may ultimately support immune-informed risk assessment and contribute to the gradual development of more individualized management strategies for RPL.