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Amniotic fluid cell-free RNA profiling reveals AGMAT as a biomarker for fetal renal abnormalities

GSE325556 Homo sapiens (human) RNA-seq (gene expression) 28 samples Submitted 2026/03/25 Platform GPL24676Platform GPL24995
At a glance

RNA-seq (gene expression) · human · 28 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
28 samples: 10 × Case vs 10 × Control vs 4 × agmat_KO vs 4 × cas9_controlGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Background Prenatal renal abnormalities comprise a heterogeneous spectrum of congenital kidney and urinary tract phenotypes detected on fetal ultrasonography and contribute substantially to long-term renal morbidity. Current prenatal assessment relies primarily on ultrasonography and genetic testing. However, approximately 60%-70% of fetuses with congenital anomalies remain without a definitive diagnosis.
Read the full summary ↓
In fetuses with renal abnormalities, the diagnostic yield is even lower, with only about 13.1% receiving a confirmed molecular diagnosis. These limitations underscore the need for complementary molecular biomarkers to improve etiologic characterization in prenatal diagnosis. Amniotic fluid (AF) cell-free RNA (cfRNA) reflects contemporaneous, organ-specific fetal transcriptional activity and offers a unique opportunity for biomarker discovery in the prenatal evaluation of renal abnormalities. Methods AF cfRNA transcriptomes were profiled in fetuses with apparent ultrasound abnormalities and in propensity score-matched controls. Differential expression and functional enrichment analyses were performed, followed by weighted gene co-expression network analysis (WGCNA) to identify phenotype-associated modules. Candidate genes were prioritized by intersecting differentially expressed genes (DEGs) with the key module, and subsequently refined through feature selection using least absolute shrinkage and selection operator regression, random forest, and support vector machine recursive feature elimination. Functional validation was performed in a CRISPR/Cas9-generated agmat crispant zebrafish model. Results Analysis of 10 case-control pairs after 1:1 propensity score matching identified 546 DEGs, enriched in pathways related to renal epithelial differentiation and tubular function. Integration of WGCNA with differential expression analysis yielded 103 candidate genes, and convergent machine learning (ML) prioritized three core genes: AGMAT, GPX2, and TIMP1. Among these, AGMAT was selected for further investigation based on its kidney-enriched expression profile and limited prior characterization in renal development. In vivo, genetic perturbation of agmat in a zebrafish model recapitulated the RNA profiling results, leading to disrupted renal morphogenesis and significantly reduced early survival. Conclusions By integrating AF cfRNA transcriptomics with ML analyses, we identified AGMAT as a candidate diagnostic biomarker for prenatally detected renal abnormalities. These results position AF cfRNA as a real-time molecular complement to prenatal imaging and genetic testing and provide a framework for etiologic interpretation, risk stratification, and mechanistic interrogation of fetal renal abnormalities.
This dataset
Open GSE325556 in finder → Find more RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE325556_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1440468 and SRA study SRP685477. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 × Case vs 10 × Control vs 4 × agmat_KO vs 4 × cas9_control

Supports a between-group comparison across 28 samples.

How we read this: 4 replicated groups read from 28 sample titles; they account for 28 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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