Comprehensive identification of immune-related biomarkers and therapeutic targets in preeclampsia: integrative bioinformatics and experimental validation

Abstract

Background: Preeclampsia (PE) is a serious hypertensive complication during pregnancy characterized by immune dysregulation and vascular dysfunction, however, the precise molecular mechanisms and effective therapeutic strategies remain unclear. This study focused on identifying immune-related differentially expressed genes (IRDEGs) in PE, investigate their biological significance and regulatory networks, and establish robust diagnostic models through integrated bioinformatics and experimental analyses. Methods: Gene expression data from the GSE75010 dataset were analyzed utilizing the R-based "limma" package to determine differentially expressed genes (DEGs), which were intersected with immune-related genes (IRGs) to obtain IRDEGs. Functional enrichment was assessed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) analyses. Hub genes were identified via Random Forest (RF) and LASSO regression algorithms, and their diagnostic performance was assessed via receiver operating characteristic (ROC) curve evaluation in both training (GSE75010) and validation (GSE44711) cohorts. Immune cell composition and its association with hub genes were explored using CIBERSORT. Regulatory networks, including protein–protein interaction (PPI), mRNA-miRNA and mRNA-TF interactions, were constructed using ENCORI and CHIPBase databases. Analysis of potential pharmaceutical-gene interactions was performed via DGIdb platform interrogation, followed by experimental validation in placental tissue and trophoblast cells. Results: We identified 354 DEGs, including 49 IRDEGs (25 upregulated and 24 downregulated). Enrichment evaluation demonstrated that IRDEGs were associated with PI3K-AKT signaling, chemokine signaling, and cytokine-cytokine receptor interaction. DO analysis linked IRDEGs to PE, cardiovascular diseases, and reproductive disorders. Four hub genes (FLT1, PIK3CB, KLRD1, and APLN) were identified as PE biomarkers based on their connectivity in the PPI network and performance in machine learning models. The RF-based diagnostic model demonstrated excellent discrimination ability with AUCs of 0.9468 (training cohort) and 0.9844 (validation cohort). Immune infiltration analysis revealed higher levels of eosinophils, plasma cells, and CD8 + T cells in PE, while monocytes and M2 macrophages were reduced. Notably, hub genes showed distinct correlations with immune cell subtypes, such as the positive association observed between FLT1 and plasma cells, contrasting with the inverse relationship documented between APLN and CD8 + T cells. Network analysis identified 128 mRNA-miRNA and 31 mRNA-TF interaction pairs. Drug-gene interaction analysis showed cyclooxygenase inhibitors, such as aspirin, targeted APLN, while TNF-α inhibitors, such as etanercept, targeted KLRD1. Experimental validation confirmed consistent expression trends across clinical specimens and in vitro models: FLT1 and PIK3CB were significantly upregulated while KLRD1 and APLN were significantly downregulated in both preeclamptic placental tissues and hypoxia-exposed trophoblast cells. Conclusions: Our study identified four hub IRDEGs that may serve as potential diagnostic indicators and therapeutic targets for PE. These findings suggest an important role of immune dysregulation in PE pathogenesis and offer new perspectives for treatment strategies. By integrating computational predictions with experimental evidence, our work contributes to the foundation for future clinical applications, though further research including early-stage PE is needed to validate these observations.