Spontaneous preterm birth causes a major burden of childhood morbidity and mortality. The incomplete understanding of the molecular pathways that result in spontaneous preterm birth, makes accurate predictive markers and target therapeutics remain elusive.

A recent study determined if a cell-free RNA profile reveals a molecular signature in maternal blood months before the onset of spontaneous preterm birth.

The study utilized Maternal samples (n=242) obtained from a prospective cohort of individuals with a singleton pregnancy across 4 clinical sites at 12–24 weeks. Plasma was processed through a next-generation sequencing pipeline for cell-free RNA using the Mirvie RNA platform. Differentially expressed Transcripts in next-generation sequencing cases and controls were determined. Enriched pathways were identified in the Reactome database employing overrepresentation analysis.

The study identified 25 transcripts associated with an increased risk of spontaneous preterm birth. These transcripts were used to develop a logistic regression model to predict spontaneous preterm birth with an area under the curve =0.80 (sensitivity=0.76, specificity=0.72). The gene discovery and model were validated via leave-one-out cross-validation. The study also identified a unique set of 39 genes from cases of very early spontaneous preterm birth; a logistic regression classifier based on these genes yielded an area under the curve=0.76 in leave-one-out cross-validation. Pathway analysis for the transcripts associated with spontaneous preterm birth showed 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 showed association with spontaneous preterm birth at <25 weeks.

Thus, the Second trimester cell-free RNA profiles in maternal blood can be used as a noninvasive tool to predict the future occurrence of spontaneous preterm birth. The systemic finding of changes in collagen and extracellular matrix pathways may help identify individuals at risk for premature cervical remodeling, with growth factor and metabolic pathways implicated more frequently in very early spontaneous preterm birth. 

cell-free RNA profiles can accurately identify those at risk for spontaneous preterm birth by revealing the underlying pathophysiology, generating an opportunity for more targeted therapeutics and effective interventions.

Camunas-Soler J, Gee EPS, Reddy M., et al. Predictive RNA profiles for early and very early spontaneous preterm birth. American Journal of Obstetrics and Gynecology. 2022;227(1): 72.e1-72.e16. https://doi.org/10.1016/j.ajog.2022.04.002.