ArticleBMC pregnancy and childbirth2026
Ferroptosis-related gene BACH1 is associated with preeclampsia and shows potential as a biomarker.
Article in BMC pregnancy and childbirth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPreeclampsia (PE) is a pregnancy-specific syndrome with unclear pathogenesis. Emerging evidence suggests that ferroptosis-related molecular pathways may be associated with oxidative stress and placental dysfunction observed in PE. In this study, we developed a PE classification model based on ferroptosis-related genes using machine learning and identified potential biomarkers.
methodsWe downloaded bulk and single-cell transcriptomic data of PE and normal placental tissues from the Gene Expression Omnibus (GEO) database. Ferroptosis-related genes were identified using weighted gene co-expression network analysis (WGCNA), differential expression analysis, and LASSO regression. Based on these genes, we developed a machine learning model to predict PE. Key marker genes were further selected using the random forest algorithm and validated in single-cell transcriptomic data.
resultsA total of 27 ferroptosis-related genes associated with PE were identified by overlapping 1,434 differentially expressed genes (DEGs) in PE, 268 known ferroptosis-related genes, and 1,151 PE-related genes. LASSO regression further selected 11 key genes with potential predictive value. Among them, BACH1 was significantly upregulated in PE and showed the strongest predictive performance as a single gene in the blood sample dataset (GSE48424). The machine learning classification model based on the selected genes exhibited strong discriminative performance, with the random forest classifier achieving an AUC of 0.87 in the test dataset (GSE75010). In the independent validation datasets (GSE149437, GSE25906, and GSE48424), the AUC values were 0.7, 0.78, and 0.81. Validation using the single-cell dataset GSE173193 confirmed that BACH1 was predominantly expressed in neutrophils from PE patients.
conclusionsThis study established a transcriptomic classification model for preeclampsia based on ferroptosis-related genes and identified BACH1 as a ferroptosis-related gene associated with preeclampsia, with potential as a blood-based biomarker pending prospective validation.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.