ArticleBMC pregnancy and childbirth2025
Transcriptomics reveals preterm birth risk: identification and validation of key genes in monocytes.
Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Molecular Mechanism and Pathways of Spontaneous Preterm Birth in Different Gestational Tissues: A Systematic Review of Transcriptome Studies.International journal of molecular sciences · 2026Pooled it
- Monkeypox virus replication and host response in vaginal and ectocervical epithelial cells.Virulence · 2026Article
- KLF4 orchestrates a Ccl2+ fibroblast-mediated inflammatory network driving preterm birth.Frontiers in medicine · 2025Article
- Blood-based predictive biomarkers for preterm birth: Redefining risk stratification in modern perinatal care.Tzu chi medical journalReview
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Authors and funding
5 authors.
Funding
Abstract
backgroundPreterm birth (PTB) is a leading cause of neonatal mortality and long-term disability worldwide. However, the molecular mechanisms underlying PTB remain incompletely understood, and the etiology of many PTB cases is still largely unexplained. Due to their close association with PTB, monocytes serve as an ideal matrix for identifying peripheral biomarkers predictive of preterm birth risk.
objectiveThis study aims to identify and validate biomarkers that could predict PTB, improving clinical diagnostic accuracy and enhancing preventive measures against PTB.
methodsThis study conducted a comprehensive transcriptomic analysis of monocytes obtained from PTB patients (gestational age = 28-36 weeks) and age-matched healthy controls (HC, gestational age = 37
resultsWe identified 295 significantly differentially expressed genes compared to the control group, and Weighted Gene Co-expression Network Analysis (WGCNA) further revealed genes significantly associated with PTB. These genes are involved in immune pathways such as rheumatoid arthritis, influenza A, and the MAPK signaling pathway. Machine learning analysis and qPCR validation identified two essential genes-CXCL3 and IL-6. Based on these two genes, the diagnostic model achieved an AUC value of 1 in the discovery cohort, distinguishing PTB patients from healthy controls.
conclusionThe immune responses observed in peripheral blood mononuclear cells (PBMCs) may be closely related to the mechanisms underlying PTB. Monocyte-derived genes CXCL3 and IL-6 are promising biomarkers for predicting PTB risk, offering new diagnostic tools for clinical practice. These findings have the potential to enhance PTB prevention and management strategies.
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