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已发表论文
维生素 D 代谢相关基因在复发性流产中的作用及其免疫微环境变化
Received 20 May 2025
Accepted for publication 21 October 2025
Published 21 November 2025 Volume 2025:18 Pages 7627—7645
DOI https://doi.org/10.2147/JMDH.S541670
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr David C. Mohr
Jiangmei He, Hongmei Liu, Jingru Ji
Department of Eugenics and Genetics, First Hospital of Shanxi Medical University, Taiyuan City, Shanxi Province, People’s Republic of China
Correspondence: Jiangmei He, Email acc0351@163.com
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.
Keywords: recurrent pregnancy loss, vitamin D metabolism, machine learning, single-cell analysis
Department of Eugenics and Genetics, First Hospital of Shanxi Medical University, Taiyuan City, Shanxi Province, People’s Republic of China
Correspondence: Jiangmei He, Email acc0351@163.com
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.
Keywords: recurrent pregnancy loss, vitamin D metabolism, machine learning, single-cell analysis