HLA-DPA1 as a diagnostic biomarker differentiating early- and late-onset preeclampsia

作者：Zhuna Wu, Yajing Xie, Weihong Chen, Zhimei Zhou, Li Huang, Liying Sheng, Yueli Wang, Binbin Chen, Congmei Yang, Yumin Ke · 发表于：Scientific Reports · 年份：2026 · DOI：10.1038/s41598-026-39050-0 · 被引用次数：1 · 研究领域：Pregnancy and preeclampsia studies、Reproductive System and Pregnancy、Sodium Intake and Health

The occurrence and development of a wide range of preeclampsia (PE), especially early-onset preeclampsia (EOPE), is closely associated with the immune system. The objective of this research is to utilize machine learning techniques to discover key immune biomarkers and evaluate their predictive potential. We sourced mRNA expression profiles from the GSE60438 + GSE75010 dataset in the Gene Expression Omnibus (GEO) and retrieved immune-related genes from the ImmPort database. Subsequently, we selected immune genes associated with EOPE and late-onset preeclampsia (LOPE) for differential expression analysis. We then carried out Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on different immune-related genes (DIRGs). Protein‒protein interaction (PPI) networks were employed to investigate the relationships among various DIRGs. Using the least absolute shrinkage and selection operator (LASSO) and multiple support vector machine recursive feature elimination (mSVM-RFE) analyses, we identified candidate biomarkers for EOPE. Receiver operating characteristic (ROC) curves were used to assess the diagnostic capability of the candidate genes, and a nomogram was constructed to evaluate the performance of the predictive models. To further validate our findings, we analyzed additional GEO datasets (GSE22526 + GSE74341 + GSE190639*) and performed immunohistochemistry (IHC) and quantitative real-time PCR (qRT-PCR) on placental tissue to confirm the ex...