The following is a summary of “Noninvasive preeclampsia prediction using plasma cell–free RNA signatures,” published in the NOVEMBER 2023 issue of Obstetrics and Gynecology by Zhou, et al.
Preeclampsia stands as a formidable pregnancy complication, with variants like preterm preeclampsia and early-onset preeclampsia posing significant life-threatening risks. The multifaceted nature of preeclampsia, marked by its heterogeneity and complexity, has rendered risk prediction and therapeutic development challenging. Given this context, the study focuses on plasma cell-free RNA, which holds distinct genetic information from human tissues, offering the potential for noninvasive monitoring across maternal, placental, and fetal dynamics throughout pregnancy. For a study, researchers sought to scrutinize the distinct RNA biotypes in plasma that are closely linked with preeclampsia. Second, to formulate predictive classifiers targeting preterm preeclampsia and early-onset preeclampsia, enabling early detection before clinical diagnosis.
Employing an innovative approach, they utilized a cutting-edge cell-free RNA sequencing technique termed “polyadenylation ligation-mediated sequencing.” The method enabled a comprehensive exploration of cell-free RNA profiles across a vast cohort, encompassing 715 pregnancies categorized as healthy and an additional 202 pregnancies diagnosed with preeclampsia before symptom manifestation. The investigative efforts focused on delineating variations in RNA biotype concentrations within plasma samples from healthy and preeclampsia-affected pregnancies. To operationalize the findings, they leveraged advanced machine learning methodologies to craft prediction classifiers specific to preterm preeclampsia and early-onset preeclampsia. Crucially, the robustness and reliability of these classifiers underwent rigorous validation exercises, encompassing both internal and external validation cohorts. Performance metrics, including the area under the curve and positive predictive value, were meticulously assessed to ensure clinical relevance and accuracy.
Through their investigative efforts, they identified 77 distinct genes, with 44% representing messenger RNA and 26% microRNA, showcasing differential expression patterns between healthy mothers and those diagnosed with preterm preeclampsia even before the onset of symptoms. These genes distinguished participants affected by preterm preeclampsia from their healthy counterparts and played pivotal functional roles in the physiological dynamics of preeclampsia. Building upon the findings, they successfully formulated two predictive classifiers tailored for forecasting preterm preeclampsia and early-onset preeclampsia before clinical diagnosis.
These classifiers were grounded on 13 distinct cell-free RNA signatures and integrated with two critical clinical parameters: in vitro fertilization and mean arterial pressure. Encouragingly, both classifiers outperformed existing diagnostic methodologies regarding accuracy and reliability. Specifically, the preterm preeclampsia prediction model achieved an impressive 81% area under the curve and a positive predictive value of 68% in an independent validation cohort (comprising 46 preterm cases and 151 controls). Similarly, the early-onset preeclampsia prediction model demonstrated robust performance metrics with an area under the curve of 88% and a commendable positive predictive value of 73% in an external validation cohort (28 early-onset preeclampsia cases versus 234 controls). Furthermore, the study shed light on the potential mechanistic roles of downregulated microRNAs in contributing to the pathogenesis of preeclampsia by modulating the expression of relevant target genes associated with the condition.
The cohort study offered an intricate transcriptomic profile delineating various RNA biotypes implicated in preeclampsia. We successfully devised two state-of-the-art classifiers with significant clinical promise, specifically targeting preterm preeclampsia and early-onset preeclampsia prediction before symptom manifestation. The findings underscored the potential of messenger RNA, microRNA, and long noncoding RNA as prospective biomarkers for preeclampsia. Such advancements paved the way for potential preventive measures against preeclampsia and illuminate the underlying molecular aberrations driving its pathogenesis. Consequently, the research opened avenues for innovative therapeutic strategies aiming to mitigate pregnancy complications and fetal morbidity associated with preeclampsia.