Predictive RNA profiles for early and very early spontaneous preterm birth

Joan Camunas-Soler, Elaine P.S. Gee, Mitsu Reddy, Jia Dai Mi, Mainou Thao, Tiffany Brundage, Farooq Siddiqui, Natasha L. Hezelgrave, Andrew H. Shennan, Eugeni Namsaraev, Carrie Haverty, Maneesh Jain, Michal A. Elovitz, Morten Rasmussen*, Rachel M. Tribe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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.

Original languageEnglish
JournalAmerican Journal of Obstetrics and Gynecology
Publication statusAccepted/In press - 2022


  • Biomarker
  • cell-free RNA
  • cervical remodeling
  • midtrimester loss
  • pregnancy
  • preterm labor
  • spontaneous preterm birth
  • transcriptome


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