PPP1R1C: a specific placental mRNA biomarker for distinguishing preeclampsia from healthy pregnancies

BACKGROUND: Preeclampsia (PE) poses a serious threat to maternal and fetal health, and its early, reliable diagnosis remains challenging. METHODS: We conducted a two-phase study using a discovery and validation design. Initially, multiple RNA-sequencing datasets from NCBI GEO were aggregated, and differentially expressed genes (DEGs) were identified via meta-analysis. The DEGs were then analyzed with weighted gene co-expression network analysis (WGCNA) to pinpoint the module most associated with PE. A signature biomarker model was developed using binary logistic regression and subsequently validated by real-time PCR (RT-PCR) on placental tissues from 30 PE patients and 30 matched controls. RESULTS: Meta-analysis yielded over 4000 DEGs, from which WGCNA identified a module of 100 genes most correlated with PE. Within this module, 24 genes exhibited |logFC| > 1, and four candidates (FSTL3, PNCK, PPP1R1C, TBC1D26) demonstrated a high discriminative ability as a combined signature biomarker (AUC = 0.90). Subsequent RT-PCR analysis confirmed the overexpression of FSTL3 and downregulation of PPP1R1C, with PNCK and TBC1D26 undetectable. However, FSTL3 did not significantly differentiate PE patients (AUC = 0.6, p > 0.05), whereas PPP1R1C achieved high accuracy (AUC = 0.94, p < 0.0001). Additionally, the alterations of FSTL3 and PPP1R1C were not correlated and combining them did not improve the diagnostic ability compared to individual usage of PPP1R1C. CONCLUSION: Placental downregulation of PPP1R1C mRNA effectively distinguishes PE patients from healthy controls, indicating its promise as a diagnostic biomarker.