Bioinformatic Analysis Identifies Potential Extracellular Matrix Related Genes in the Pathogenesis of Early Onset Preeclampsia.

Abstract

Early-onset preeclampsia (EOPE) is a complex pregnancy complication that poses significant risks to the health of both mothers and fetuses, and research on its pathogenesis and pathophysiology remains insuffcient. This study aims to explore the role of candidate genes and their potential interaction mechanisms in EOPE through bioinformatics analysis techniques. Two gene expression datasets, GSE44711 and GSE74341, were obtained from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) between EOPE and gestational age-matched preterm control samples. Functional enrichment analysis was performed utilizing the kyoto encyclopedia of genes and genomes (KEGG), gene ontology (GO), and gene set enrichment analysis (GSEA). A protein-protein interaction (PPI) network was constructed using the STRING database, and hub DEGs were identified through Cytoscape software and comparative toxicogenomics database (CTD) analysis. Furthermore, a diagnostic logistic model was established using these hub genes, which were confirmed through reverse transcription polymerase chain reaction (RT-PCR). Finally, immune cell infiltration was analyzed using CIBERSORT. In total, 807 DEGs were identified in the GSE44711 dataset (451 upregulated genes and 356 downregulated genes), and 787 DEGs were identified in the GSE74341 dataset (446 upregulated genes and 341 downregulated genes). These DEGs were significantly enriched in various molecular functions such as extracellular matrix structural constituent, receptor-ligand activity binding, cytokine activity, and platelet-derived growth factor. KEGG and GSEA annotation revealed significant enrichment in pathways related to ECM-receptor interaction, PI3K-AKT signaling, and focal adhesion. Ten hub genes were identified through the CytoHubba plugin in Cytoscape. Among these hub genes, three key DEGs (COL1A1, SPP1, and THY1) were selected using CTD analysis and various topological methods in Cytoscape. The diagnostic logistic model based on these three genes exhibited high efficiency in predicting EOPE (AUC = 0.922). RT-PCR analysis confirmed the downregulation of these genes in EOPE, and immune cell infiltration analysis suggested the significant role of M1 and M2 macrophages in EOPE. In conclusion, this study highlights the association of three key genes (COL1A1, SPP1, and THY1) with EOPE and their contribution to high diagnostic efficiency in the logistic model. Additionally, it provides new insights for future research on EOPE and emphasizes the diagnostic value of these identified genes. More research is needed to explore their functional and diagnostic significance in EOPE.