ArticleBMC pregnancy and childbirth2026
PPP1R1C: a specific placental mRNA biomarker for distinguishing preeclampsia from healthy pregnancies.
Article in BMC pregnancy and childbirth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundPreeclampsia (PE) poses a serious threat to maternal and fetal health, and its early, reliable diagnosis remains challenging.
methodsWe 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.
resultsMeta-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.
conclusionPlacental downregulation of PPP1R1C mRNA effectively distinguishes PE patients from healthy controls, indicating its promise as a diagnostic biomarker.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.