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

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 biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. Immune microenvironment profiling and regulatory network analyses elucidated mechanistic links. Finally, qRT-PCR confirmed the differential expression of key genes in clinical samples. Results: We identified 25 ribosome biogenesis-related differentially expressed genes, which were significantly enriched in RNA degradation and rRNA processing. Weighted gene co-expression network analysis prioritized seven hub genes. A random forest model incorporating six key feature genes ( Conclusion: This study identifies ribosome biogenesis as one of the pivotal molecular mechanisms to PE pathogenesis, leveraging SHAP-interpretable machine learning to pinpoint six biomarkers. Future research is requisite for the validation of CRISPR and the integration of multi-omics to translate the findings into clinical diagnosis and targeted therapy.