@article{1d9ec7226dbb40208dfb286cb27e078b,
title = "Predictive RNA profiles for early and very early spontaneous preterm birth",
abstract = "Background: Spontaneous preterm birth remains the main driver of childhood morbidity and mortality. Because of an incomplete understanding of the molecular pathways that result in spontaneous preterm birth, accurate predictive markers and target therapeutics remain elusive. Objective: This study sought to determine if a cell-free RNA profile could reveal a molecular signature in maternal blood months before the onset of spontaneous preterm birth. Study Design: Maternal samples (n=242) were obtained from a prospective cohort of individuals with a singleton pregnancy across 4 clinical sites at 12–24 weeks (nested case-control; n=46 spontaneous preterm birth <35 weeks and n=194 term controls). Plasma was processed via a next-generation sequencing pipeline for cell-free RNA using the Mirvie RNA platform. Transcripts that were differentially expressed in next-generation sequencing cases and controls were identified. Enriched pathways were identified in the Reactome database using overrepresentation analysis. Results: Twenty five transcripts associated with an increased risk of spontaneous preterm birth were identified. A logistic regression model was developed using these transcripts to predict spontaneous preterm birth with an area under the curve =0.80 (95% confidence interval, 0.72–0.87) (sensitivity=0.76, specificity=0.72). The gene discovery and model were validated through leave-one-out cross-validation. A unique set of 39 genes was identified from cases of very early spontaneous preterm birth (<25 weeks, n=14 cases with time to delivery of 2.5±1.8 weeks); a logistic regression classifier on the basis of these genes yielded an area under the curve=0.76 (95% confidence interval, 0.63–0.87) in leave-one-out cross validation. Pathway analysis for the transcripts associated with spontaneous preterm birth revealed enrichment of genes related to collagen or the extracellular matrix in those who ultimately had a spontaneous preterm birth at <35 weeks. Enrichment for genes in insulin-like growth factor transport and amino acid metabolism pathways were associated with spontaneous preterm birth at <25 weeks. Conclusion: Second trimester cell-free RNA profiles in maternal blood provide a noninvasive window to future occurrence of spontaneous preterm birth. The systemic finding of changes in collagen and extracellular matrix pathways may serve to identify individuals at risk for premature cervical remodeling, with growth factor and metabolic pathways implicated more often in very early spontaneous preterm birth. The use of cell-free RNA profiles has the potential to accurately identify those at risk for spontaneous preterm birth by revealing the underlying pathophysiology, creating an opportunity for more targeted therapeutics and effective interventions.",
keywords = "Biomarker, cell-free RNA, cervical remodeling, midtrimester loss, pregnancy, preterm labor, spontaneous preterm birth, transcriptome",
author = "Joan Camunas-Soler and Gee, {Elaine P.S.} and Mitsu Reddy and Mi, {Jia Dai} and Mainou Thao and Tiffany Brundage and Farooq Siddiqui and Hezelgrave, {Natasha L.} and Shennan, {Andrew H.} and Eugeni Namsaraev and Carrie Haverty and Maneesh Jain and Elovitz, {Michal A.} and Morten Rasmussen and Tribe, {Rachel M.}",
note = "Funding Information: Samples from the “Insight: investigation into biomarkers for the prediction of spontaneous preterm birth” study were collected with support from Tommy's Charity (number 1060508), The National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London and/or the NIHR Clinical Research Facility, the Rosetrees Trust (charity number 298582) (M303-CD1), Action Medical Research and The Borne Foundation (GN2666), and an NIHR Doctoral Research Fellowship (DRF-2013-06-171) to N.H. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.Samples from the “Insight” study were collected with support from Tommy's Charity (number 1060508), the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas{\textquoteright} National Health Service Foundation Trust, the Rosetrees Trust (charity number 298582) (M303-CD1), an NIHR Doctoral Research Fellowship (DRF-2013-06-171) to Natasha L Hezelgrave and support from Borne (charity number 1167073) and Action Medical Research (GN2666). The authors also want to acknowledge all the participants and centers that provided samples for this study. Funding Information: Samples from the “Insight: investigation into biomarkers for the prediction of spontaneous preterm birth” study were collected with support from Tommy{\textquoteright}s Charity (number 1060508), The National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London and/or the NIHR Clinical Research Facility, the Rosetrees Trust (charity number 298582) (M303-CD1), Action Medical Research and The Borne Foundation (GN2666), and an NIHR Doctoral Research Fellowship (DRF-2013-06-171) to N.H. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Funding Information: Samples from the “Insight” study were collected with support from Tommy{\textquoteright}s Charity (number 1060508), the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy{\textquoteright}s and St Thomas{\textquoteright} National Health Service Foundation Trust, the Rosetrees Trust (charity number 298582) (M303-CD1), an NIHR Doctoral Research Fellowship (DRF-2013-06-171) to Natasha L Hezelgrave and support from Borne (charity number 1167073) and Action Medical Research (GN2666). The authors also want to acknowledge all the participants and centers that provided samples for this study. Publisher Copyright: {\textcopyright} 2022 The Authors",
year = "2022",
doi = "10.1016/j.ajog.2022.04.002",
language = "English",
journal = "American Journal of Obstetrics and Gynecology",
issn = "0002-9378",
publisher = "Mosby Inc.",
}