Multiomics integrative bioinformatics analysis of gene expression characteristics and molecular mechanisms in preeclampsia placental tissue.

Authors

Ni Li, Lin Tian, Haiyun Wei

Published in

Artificial cells, nanomedicine, and biotechnology. Volume 54. Issue 1. Pages 358-373. Epub Jul 20, 2026.

Abstract

Preeclampsia (PE) is a severe pregnancy-specific complication characterized by new-onset hypertension and proteinuria after 20 weeks of gestation, which can cause multi-organ damage and life-threatening outcomes for both mothers and foetuses. Its pathogenesis remains incompletely elucidated, with placental dysfunction widely recognized as a core pathogenic factor. This study integrated multiple placental transcriptome and single-cell sequencing datasets from the Gene Expression Omnibus (GEO) database, employing a multi-dimensional bioinformatics approach - including differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, molecular subtype clustering, single-cell resolution analysis, and intercellular communication analysis - to systematically identify PE-related key genes, construct a diagnostic model, define molecular subtypes, and explore potential molecular mechanisms. Results showed 10 differentially expressed genes (DEGs) were identified in PE placental tissues; WGCNA pinpointed the turquoise module as the core PE-associated module. Further screening using 11 machine learning algorithms identified 9 feature genes with high diagnostic value (DDR1, DIO2, FSTL3, HK2, HTRA4, LEP, SERPINA3, TMEM45A, TREM1). A diagnostic model built with the 'Stepglm[forward]' algorithm exhibited excellent performance in both training and validation sets (average AUC = 0.865). Molecular subtype analysis classified PE samples into two subtypes (C1, C2) with significantly distinct immune infiltration profiles, where the C1 subtype showed higher immune cell infiltration. Single-cell analysis identified 11 cell types in PE placental tissue and highlighted TMEM45A as a key DEG. Intercellular communication analysis revealed the VEGF signalling pathway as the core driver of abnormal cellular crosstalk in PE, primarily mediating signal transduction between villous cytotrophoblast cells (VCT), extravillous trophoblast cells (EVT), and endothelial cells. Hypoxia scoring analysis demonstrated significantly higher hypoxia levels in the PE group compared to normal controls, with TMEM45A expression positively correlated with hypoxia scores. This study provides novel insights into the molecular pathogenesis of PE and offers potential biomarkers and a theoretical basis for its early diagnosis and targeted therapy.

PMID:
42473750
Bibliographic data and abstract were imported from PubMed on 20 Jul 2026.

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