Maternal plasma cell-free RNA as a predictive test for fetal lung maturation

Maternal plasma cell-free RNA as a predictive test for fetal lung maturation

BMC Medicine

10.1186/s12916-025-04256-y

https://doi.org/10.1186/s12916-025-04256-y

Sean W. D. Carter, Kay Yi Michelle Seah, Si En Poh, Winston Koh, Haruo Usuda, Erin L. Johnson, Yusaku Kumagai, Tsukasa Takahashi, Lara J. Monteiro, Reyna Peñailillo, Gino Nardocci, Hannah R. S. Watson, Masatoshi Saito, Mahesh A. Choolani, Sebastián E. Illanes, Matthew W. Kemp

Cell-free RNA,  Preterm birth, Lung maturation, Antenatal corticosteroids, Predictive test, gene expression

28 July 2025

Carter, S. W. D., Seah, K. Y. M., Poh, S. E., Koh, W., Usuda, H., Johnson, E. L., Kumagai, Y., Takahashi, T., Monteiro, L. J., Peñailillo, R., Nardocci, G., Watson, H. R. S., Saito, M., Choolani, M. A., Illanes, S. E., Kemp, M. W. (2025). Maternal plasma cell-free RNA as a predictive test for fetal lung maturation. BMC Medicine, 23(1). https://doi.org/10.1186/s12916-025-04256-y

Background A lack of tests to assess fetal development impacts decision making around antenatal steroid use in women at risk of preterm birth. We analyzed the expression of 21 cfRNA targets related to human fetal lung maturation. Discovery studies were performed using maternal and fetal sheep plasma, with results compared to fetal lung mRNA expression. These findings were then validated in first, second, and third trimester human maternal plasma samples. Methods Discovery studies utilized a preterm sheep model of pregnancy. Date mated ewes received saline (control n=6), or antenatal steroids (dexamethasone n=12) (betamethasone n=11) prior to delivery and ventilation. We analyzed the expression of 21 human cfRNA targets related to lung maturation in maternal and fetal sheep plasma and compared this to mRNA expression in fetal lung tissue. Findings were first validated in a separate cohort of sheep exposed to betamethasone (n=8), intraamniotic LPS endotoxin for lung maturation (n=6), or untreated term animals (n=6). Findings were further validated in maternal plasma from a human cohort of uncomplicated term pregnancies (n=10). Delivery and ventilation data were analyzed with ANOVA, Tukey HSD, and Dunnett T3 tests. A Random Forest algorithm identified genes that separated mature from immature fetal lung subgroups and determined AUC values for maternal and fetal cell-free RNA (cfRNA) feature sets to predict fetal lung maturation. Results We demonstrate that the analysis of 21 human cfRNA targets in maternal plasma is highly predictive of fetal lung maturation status across antenatal steroid induced (Dexamethasone AUC=0.93; Betamethasone AUC=1) and physiological (AUC=1) lung development models. Maternal plasma cfRNA expression in the dexamethasone antenatal steroid group closely resembled direct fetal lung tissue mRNA expression. These findings were then validated in human maternal plasma samples (1st vs. 3rd trimester AUC=0.96; 2nd vs. 3rd trimester AUC=1). Conclusions Further development of this technology may provide a rapid, minimally invasive, and cost-effective clinical tool to optimize patient selection for initial and repeat courses of antenatal steroids, along with insights into the molecular mechanisms underlying fetal lung development.

Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

This work was supported by a 2021 Seed grant from the Ministry of Education, Government of Singapore (NUHSRO/2021/109/T1/Seed-Sep/02).

©The Author(s) 2025.Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by-nc-nd/4.0/

https://oar.a-star.edu.sg/communities-collections/articles/22145

1741-7015

Bioinformatics Institute

https://doi.org/10.1186/s12916-025-04256-y