Preeclampsia (PE) poses a serious threat to maternal and fetal health, and its early, reliable diagnosis remains challenging. 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. 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. Placental downregulation of PPP1R1C mRNA effectively distinguishes PE patients from healthy controls, indicating its promise as a diagnostic biomarker.
Ebrahimi et al. (Mon,) studied this question.