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Cardiac Fibrosis in Dilated Cardiomyopathy: Transcriptomics Insights, Histological Correlations, and Organoid Model Verifications [RNA-seq I]

GSE245825 Homo sapiens (human) RNA-seq (gene expression) 58 samples Submitted 2026/06/15 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 58 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
58 samples
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.
Paper
Integrative Transcriptomic-Histological Analysis in Dilated Cardiomyopathy Unveils FGFR1 Inhibition as Anti-Cardiac Fibrotic and Cardioprotective TherapyJACC. Basic to translational science · 2025
Summary (written by the submitters)
Dilated cardiomyopathy (DCM) represents a leading cause of heart failure among younger adults. Despite endomyocardial biopsy (EMB) transcriptome enriching our understanding of DCM, the link between its gene expression and phenotype remains unclear. RNA-seq analysis of 58 DCM samples and 12 publicly available control samples unveiled about 25,000 transcripts.
Read the full summary ↓
A principal component analysis highlighted a distinct DCM-control separation. WGCNA revealed four transcriptome modules strongly associated with DCM. The purple module, which is the DCM-related module, was enriched with fibrosis-related genes and showed FSTL3 as a pivotal DCM-associated gene.
Published in
Integrative Transcriptomic-Histological Analysis in Dilated Cardiomyopathy Unveils FGFR1 Inhibition as Anti-Cardiac Fibrotic and Cardioprotective Therapy
Hata R, Funakoshi S, Makiyama T et al. · JACC. Basic to translational science 2025 · PMID 40934809 · doi:10.1016/j.jacbts.2025.101363
This dataset
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.

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 GSE245825_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1030026. Searching any of these in the dataset finder brings you back here.

Study design
A cohort of 58 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run Hh24 … Hh188 and carry no group information.

Samples in this study

+ 18 more — browse all 58 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.