Integrated bioinformatics and single-cell transcriptomics identify mitochondrial dysfunction-driven biomarkers for preeclampsia diagnosis.

Abstract

Background: Preeclampsia is a complex pregnancy disorder characterized by hypertension and proteinuria that pose significant risks to maternal and fetal health. Current diagnostic and treatment methods are limited, necessitating the identification of effective biomarkers and therapeutic targets.

Methods: This study utilized bioinformatics methods, including differential expression analysis, weighted gene co-expression network analysis, protein-protein interaction networks, and machine learning algorithms, to identify critical mitochondrial genes associated with preeclampsia. Enrichment analysis was performed using gene set enrichment analysis, gene set variation analysis, gene ontology, and the Kyoto Encyclopedia of Genes and Genomes. Receiver operating characteristic analysis and SHapley Additive exPlanations analysis were used to evaluate the diagnostic performance of key genes and diagnostic models. Single-cell analysis provided insights into cellular heterogeneity and interactions. The identified key genes were validated via RT-qPCR.

Results: Six mitochondrial key genes (SOD1, ACSF2, ABAT, ACSS1, ALDH4A1, PDHA1) and diagnostic models were identified, demonstrating robust diagnostic accuracy. Functional enrichment implicated these genes in oxidative phosphorylation, TCA cycle, and fatty acid metabolism. Single-cell analysis revealed trophoblast subpopulations as key sites of mitochondrial dysregulation. RT-qPCR confirmed significant downregulation of ABAT, PDHA1, and SOD1 in PE placentas.

Conclusion: Genes and pathways related to preeclampsia identified based on bioinformatic analyses may have diagnostic and therapeutic utility. Future research should confirm their clinical potential in larger groups of individuals.