Noninvasive preeclampsia prediction using plasma cell–free RNA signatures

Background Preeclampsia is a life-threating pregnancy disorder, especially preterm preeclampsia and early-onset preeclampsia, the heterogeneity and complexity of preeclampsia make it difficult to predict risk and 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. Objective This study aimed to investigate various RNA biotypes associated with preeclampsia in plasma and develop classifiers to predict preterm preeclampsia and early-onset preeclampsia before diagnosis. Study Design We performed a novel cell-free RNA sequencing method named PALM-Seq to investigate the cell-free RNA characteristics of 715 healthy and 202 preeclampsia pregnancies before symptom onset. We explored changes 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. Results We detected 77 differentially expressed genes including mRNA (44%) and miRNA (26%) between healthy and preterm preeclampsia mothers before symptom onset, which could separate preterm preeclampsia from healthy samples and play critical functional roles in preeclampsia physiology. We developed two classifiers for predicting preterm preeclampsia and early-onset preeclampsia before diagnosis based on 13 cell-free RNA signatures and two clinical features (IVF and MAP), respectively. Notably, both classifiers showed enhanced performance compared to 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 PE, 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 PE, n=28; control, n=234). Furthermore, we demonstrated downregulation of miRNAs may play vital roles in preeclampsia through the upregulation of preeclampsia-relevant target genes. Conclusion Our cohort study presented the first comprehensive transcriptomic landscape of different RNA biotypes in preeclampsia and developed two advanced classifiers with significant clinical importance for preterm preeclampsia and early-onset preeclampsia prediction before symptom onset. We demonstrated for the first time that messenger RNA, microRNA, and long noncoding RNA can simultaneously serve as potential biomarkers of preeclampsia, holding the promise of prevention of preeclampsia in advance. Abnormal cell-free messenger RNA, microRNA and long noncoding RNA molecular changes help to elucidate the pathogenic determinants of preeclampsia, and open new therapeutic windows to effectively reduce pregnancy complications and fetal morbidity. Preeclampsia is a life-threating pregnancy disorder, especially preterm preeclampsia and early-onset preeclampsia, the heterogeneity and complexity of preeclampsia make it difficult to predict risk and 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 develop classifiers to predict preterm preeclampsia and early-onset preeclampsia before diagnosis. We performed a novel cell-free RNA sequencing method named PALM-Seq to investigate the cell-free RNA characteristics of 715 healthy and 202 preeclampsia pregnancies before symptom onset. We explored changes 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 differentially expressed genes including mRNA (44%) and miRNA (26%) between healthy and preterm preeclampsia mothers before symptom onset, which could separate preterm preeclampsia from healthy samples and play critical functional roles in preeclampsia physiology. We developed two classifiers for predicting preterm preeclampsia and early-onset preeclampsia before diagnosis based on 13 cell-free RNA signatures and two clinical features (IVF and MAP), respectively. Notably, both classifiers showed enhanced performance compared to 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 PE, 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 PE, n=28; control, n=234). Furthermore, we demonstrated downregulation of miRNAs may play vital roles in preeclampsia through the upregulation of preeclampsia-relevant target genes. Our cohort study presented the first comprehensive transcriptomic landscape of different RNA biotypes in preeclampsia and developed two advanced classifiers with significant clinical importance for preterm preeclampsia and early-onset preeclampsia prediction before symptom onset. We demonstrated for the first time that messenger RNA, microRNA, and long noncoding RNA can simultaneously serve as potential biomarkers of preeclampsia, holding the promise of prevention of preeclampsia in advance. Abnormal cell-free messenger RNA, microRNA and long noncoding RNA molecular changes help to elucidate the pathogenic determinants of preeclampsia, and open new therapeutic windows to effectively reduce pregnancy complications and fetal morbidity.