SERPINA3 and NDRG1 are critical diagnostic immune genes associated with macrophages in preeclampsia

Objective: The immune system plays a role in the occurrence and progression of numerous pregnancy complications, particularly preeclampsia (PE). This study aims to identify critical immune biomarkers via machine learning and assess their predictive ability. Methods: We acquired mRNA expression profiles from the GSE75010 dataset of the Gene Expression Omnibus (GEO) and immune-related genes from the ImmPort database (https://www.immport.org). Immune genes associated with PE were selected for differential expression analysis. Different immune-related genes (DIRGs) were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein‒protein interaction (PPI) networks were utilized to explore the connections among various DIRGs. The candidate biomarkers were identified via least absolute shrinkage and selection operator (LASSO) and multiple support vector machine recursive feature elimination (mSVM-RFE) analyses for PE. Receiver operating characteristic (ROC) curves were utilized to validate the diagnostic ability of the candidate genes. A Nomo plot was used to evaluate the performance of the predictive models. We further confirmed the expression levels and diagnostic values of key genes by analyzing additional GEO datasets (GSE54618, GSE74341, and GSE14776). Further, by performing RT-PCR, Western Blot (WB), and immunohistochemistry (IHC) in placental tissue. We developed a nomogram to predict PE utilizing two immune-related genes. The compositional patterns of the infiltration of 22 immune cell types in PE were analyzed via the CIBERSORT algorithm. Results: We identified 66 differentially expressed genes (DEGs) and 10 DIRGs between PE pregnancies and normotensive pregnancies. The GO analyses revealed that the DIRGs were enriched in gonadotropin secretion, the regulation of gonadotropin secretion, and the regulation of endocrine processes. The results of the KEGG enrichment analysis were predominantly associated with cytokine-cytokine receptor interactions and neuroactive ligand‒receptor interactions. The SERPINA3 and NDRG1 genes were identified as diagnostic biomarkers of PE (AUC = 0.812, AUC=0.866) and further verified in additional GEO datasets (AUC = 0.795, AUC=0.781). Elevated levels of SERPINA3 and NDRG1 expression were found to be correlated with PE in clinical samples. In addition, immune microenvironment analysis revealed that SERPINA3 and NDRG1 were negatively correlated with M2 macrophages (P < 0.05). Conclusion: Immunity is a crucial factor in the pathogenesis of PE. SERPINA3 and NDRG1 can be identified as key immune genes associated with M2 macrophages, and these findings provide novel perspectives for the diagnosis and pathogenesis of PE.