Identification of a key immune gene HLA-DPA1 serves as a potential diagnostic biomarker to differentiate between early-onset and late-onset preeclampsia

Objective 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. Methods We sourced mRNA expression profiles from the GSE60348 + 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) on placental tissue to confirm the expression levels and diagnostic values of key genes. Eventually, we utilized the CIBERSORT algorithm to analyze the compositional patterns of the infiltration of 22 immune cell types in EOPE. Results A total of 13 differentially expressed genes (DEGs) and 4 DIRGs were discovered through a comparison between LOPE and EOPE. Upon conducting GO analyses, it was determined that DIRGs showed significant enrichment in positive regulation of T cell, lymphocyte, and mononuclear cell proliferation. The KEGG enrichment analysis predominantly demonstrated associations with Immune disease, Endocrine and metabolic disease, and Th1 and Th2 cell differentiation. We identified HLA-DPA1 genes as a potential diagnostic biomarker for EOPE, with an area under the curve (AUC) of 0.731 for HLA-DPA1. Clinical sample analysis revealed that decreased expression levels of HLA-DPA1 were associated with EOPE. Moreover, immune microenvironment analysis indicated that the expression of HLA-DPA1 exhibited a negative correlation with regulatory T cells and Dendritic cells activated, a positive correlation with macrophages M1 and Mast cells resting. Conclusion Immunity is a key factor in the pathogenesis of placenta in EOPE. HLA-DPA1 can be identified as a key immune gene associated with immune cells, and these findings provide novel perspectives for the diagnosis and pathogenesis of EOPE