Integrated Bioinformatics Analysis Reveals Candidate Hub Genes and Regulatory Networks Associated with Preeclampsia
TL;DR
Results provide candidate PE-associated genes and regulatory pathways for further experimental and clinical validation and validate the mRNA expression patterns of candidate hub genes using two independent placental transcriptomic datasets.
Abstract
Preeclampsia (PE) is a severe pregnancy-associated hypertensive disorder and a major contributor to maternal and perinatal morbidity and mortality. The mechanisms of PE pathogenesis are not yet understood. This paper aims to explore candidate PE-associated biomarkers and regulatory mechanisms using bioinformatics analysis of placental transcriptomic datasets. We downloaded placental transcriptomic datasetsGSE203507 and GSE148241 from GEO and investigated differentially expressed genes(DEGs) between PE and control samples. We performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), and protein–protein interaction (PPI) analyses to characterize the potential role, disease associations, and interaction networks of the DEGs. We validated the mRNA expression patterns of candidate hub genes using two independent placental transcriptomic datasets, GSE143966 and GSE114691.We identified 263 DEGs in the discovery analysis and further obtained 150 overlapping DEGs including80 upregulated genes and 70 downregulated genes, for downstream analysis. We then identified eight candidate hub genes: OPRK1, OXGR1, HCAR3, CCR5, HCAR2, CXCL1, FPR3, and SSTR1using Meta scape software(v3.5.20260201). In the validation phase, most candidate hub genes showed broadly consistent mRNA expression trends across GSE114691 and GSE143966, while FPR3 showed weaker cross-dataset consistency. These findings provide candidate PE-associated genes and regulatory pathways for further experimental and clinical validation..