Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.

ABSTRACT:

Background

Preeclampsia, a hypertensive disorder during pregnancy affecting 2-8% of pregnancies globally, remains a leading cause of maternal and fetal morbidity. Current diagnostic reliance on late-onset clinical features and suboptimal biomarkers underscores the need for early molecular predictors. Ribosome biogenesis, critical for cellular homeostasis, is hypothesized to drive placental dysfunction in PE, though its role remains underexplored.

Methods

We integrated placental transcriptomic data from two datasets (GSE75010, GSE10588) to systematically investigate ribosome biogenesis dysregulation in preeclampsia. Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome b

SUBMITTER: Chen J 

PROVIDER: S-EPMC12183210 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature