Gene expression profiling of maternal plasma, urine, and blood reveals molecular signatures of placenta accreta and low-lying placenta

Abstract

Placenta accreta spectrum (PAS) and low-lying placenta (LLP) are major obstetric disorders associated with life-threatening hemorrhage and adverse maternal outcomes. Despite advances in imaging, accurate prenatal differentiation between these conditions remains challenging. Liquid biopsy approaches using maternal biofluids may offer novel, non-invasive biomarkers of placental pathology. We conducted high-throughput RNA sequencing across urine, plasma, and peripheral blood from pregnancies complicated by confirmed PA (n = 3), LLP (n = 5), and gestationally matched controls (n = 3). Differential gene expression (DGE) analysis was performed for each biofluid type using DESeq2, followed by functional enrichment analysis using Gene Ontology (GO) and KEGG pathways. Cross-biofluid comparisons were also conducted to identify shared and compartment-specific molecular signatures. In PA, we identified 575, 74, and 7 differentially expressed genes in plasma, urine, and blood, respectively, compared to the controls. In LLP patients, 687, 99, and 4 genes were differentially expressed in plasma, urine, and blood, respectively, compared to controls. Cross-condition comparison with LLP revealed a PA-specific transcriptomic signature comprising 416 plasma DEGs, 53 urinary DEGs, and 7 peripheral-blood DEGs. PA also shared 159 plasma DEGs and 21 urinary DEGs with LLP, whereas no overlapping DEGs were observed in peripheral blood. Gene Ontology and KEGG analyses demonstrated significant enrichment of immune–inflammatory, extracellular matrix, angiogenic signaling, and metabolic pathways, with extensive pathway-level dysregulation in plasma and complementary immune-metabolic and epithelial signatures in urine, supporting invasive placentation and systemic immune–metabolic reprogramming. This triple-biofluid transcriptomic analysis reveals both shared and distinct molecular landscapes of PA and LLP, supporting a disease-spectrum model of abnormal placentation. Our findings provide a proof of concept for the use of multi-biofluid gene expression profiling for early diagnosis and stratification of abnormal placentation. This exploratory study requires validation in larger cohorts.