Noninvasive preeclampsia prediction using plasma cell–free RNA signatures

• Published in: American journal of obstetrics and gynecology 2023-11, Vol.229 (5), p.553.e1-553.e16
• Main Authors: Zhou, Si, Li, Jie, Yang, Wenzhi, Xue, Penghao, Yin, Yanning, Wang, Yunfang, Tian, Peirun, Peng, Huanhuan, Jiang, Hui, Xu, Wenqiu, Huang, Shang, Zhang, Rui, Wei, Fengxiang, Sun, Hai-Xi, Zhang, Jianguo, Zhao, Lijian
• Format: Article
• Language: English
• Subjects: biomarker; Biomarkers; cell-free RNA; Cohort Studies; early-onset preeclampsia; Female; Humans; Infant, Newborn; MicroRNAs - genetics; Placenta; Pre-Eclampsia - diagnosis; Pre-Eclampsia - genetics; prediction; preeclampsia; Pregnancy; preterm preeclampsia; RNA, Long Noncoding; RNA, Messenger
• ISSN: 0002-9378; 1097-6868
• DOI: 10.1016/j.ajog.2023.05.015

Preeclampsia, especially preterm preeclampsia and early-onset preeclampsia, is a life-threating pregnancy disorder, and the heterogeneity and complexity of preeclampsia make it difficult to predict risk and to develop treatments. Plasma cell-free RNA carries unique information from human tissue and may be useful for noninvasive monitoring of maternal, placental, and fetal dynamics during pregnancy. This study aimed to investigate various RNA biotypes associated with preeclampsia in plasma and to develop classifiers to predict preterm preeclampsia and early-onset preeclampsia before diagnosis. We performed a novel, cell-free RNA sequencing method termed polyadenylation ligation-mediated sequencing to investigate the cell-free RNA characteristics of 715 healthy pregnancies and 202 pregnancies affected by preeclampsia before symptom onset. We explored differences in the abundance of different RNA biotypes in plasma between healthy and preeclampsia samples and built preterm preeclampsia and early-onset preeclampsia prediction classifiers using machine learning methods. Furthermore, we validated the performance of the classifiers using the external and internal validation cohorts and assessed the area under the curve and positive predictive value. We detected 77 genes, including messenger RNA (44%) and microRNA (26%), that were differentially expressed in healthy mothers and mothers with preterm preeclampsia before symptom onset, which could separate participants with preterm preeclampsia from healthy samples and that played critical functional roles in preeclampsia physiology. We developed 2 classifiers for predicting preterm preeclampsia and early-onset preeclampsia before diagnosis based on 13 cell-free RNA signatures and 2 clinical features (in vitro fertilization and mean arterial pressure), respectively. Notably, both classifiers showed enhanced performance when compared with the existing methods. The preterm preeclampsia prediction model achieved 81% area under the curve and 68% positive predictive value in an independent validation cohort (preterm, n=46; control, n=151); the early-onset preeclampsia prediction model had an area under the curve of 88% and a positive predictive value of 73% in an external validation cohort (early-onset preeclampsia, n=28; control, n=234). Furthermore, we demonstrated that downregulation of microRNAs may play vital roles in preeclampsia through the upregulation of preeclampsia-relevant target genes. In this cohort study, a