ArticleJournal of multidisciplinary healthcare2025
The Role of Vitamin D Metabolism-Related Genes in Recurrent Pregnancy Loss and Their Immune Microenvironmental Changes.
Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The importance of vitamin D metabolism has been confirmed in various pregnancy complications. It is unknown, henceforth how vitamin D metabolism contributes to the occurrence of recurrent pregnancy loss (RPL). This study aimed to elucidate its potential mechanisms through bioinformatics analysis. Methods: Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify module genes linked to vitamin D metabolism after transcriptome datasets were examined to identify differentially expressed genes (DEGs). Machine learning was utilized to refine and identify candidate biomarkers, while Mendelian randomization (MR) assessed their causal relationships with RPL. In addition, the expression was further verified by RT-qPCR and Western blotting. Finally, scRNA-seq uncovered cellular heterogeneity and intercellular communication networks. Results: We identified 379 DEGs in RPL samples. WGCNA revealed two key modules strongly correlated with vitamin D metabolism. The intersection of DEGs and key module genes yielded 27 candidate genes related to vitamin D metabolism. Machine learning identified DOCK11 and ETV2 as biomarkers, showing consistent expression trends across training and validation sets, both demonstrating AUC values greater than 0.7 in ROC analysis. Functional enrichment analysis indicated that DOCK11 and ETV2 were co-enriched in the pathways of inflammatory responses, interferon gamma response, and TNAF signaling via NFKB. Experimental validation yielded the same results. Single-cell analysis revealed 16 distinct cellular clusters with significant enrichment of DOCK11 and ETV2 in Natural Killer cells, highlighting altered immune interactions in RPL through enhanced signaling from NK cells and cytotoxic CD8+ T cells while reducing signals from macrophages. Conclusion: This study identified DOCK11 and ETV2 as biomarkers for RPL, revealing the important involvement of NK cells in RPL and providing new directions for the treatment of RPL.
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