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Transcriptomic signature of Human Cardiac Fibroblast in Hypertrophic Obstructive Cardiomyopathy

GSE337273 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2026/07/15 Platform GPL21697
At a glance

RNA-seq (gene expression) · human · 15 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
15 samples: 15 samples, each with a distinct title — no replicated groupsGroups 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)
Introduction: Hypertrophic Cardiomyopathy (HCM) is a cardiac disorder characterized by an increased interstitial fibrosis and Extracellular Matrix (ECM) remodeling. Cardiac fibroblasts (CFs) have a crucial role in ECM remodeling as well as influencing contractility. There is accumulating evidence that the phenotype of CFs is disease-specific.
Read the full summary ↓
Here, we investigate the transcriptome signature of CFs in Hypertrophic obstructive Cardiomyopathy (HOCM) patients and its potential functional significance. Methods: Primary CFs were isolated from myectomy specimens of 12 clinically phenotyped HOCM patients and 3 controls. RNA libraries were prepared from cell isolates for subsequent whole transcriptome sequencing. Differential expression analysis was conducted using the Tuxedo pipeline. Pathway and Gene Ontology (GO) enrichment were performed using Protein-protein interaction network analysis and functional clustering were performed using Cytoscape StringApp. Expression of candidate genes and proteins was validated using real time quantitative PCR, Immunocytochemistry, immunohistochemistry, cytokines/chemokines profiling and western blotting, in patient-derived CFs extracts, CFs-conditioned media, and myocardial tissue sections. Results: Whole transcriptome analysis identified 265 significant differentially expressed genes (DEGs) (q-value ≤ 0.05 and log fold-change ≥ 1.5) in HOCM fibroblasts compared to controls. The most significant GO terms identified were associated with ECM organization and inflammatory response. The most significant GO terms identified were associated with ECM organization and inflammatory response, with circos plot analysis further highlighting pathway-specific gene overlap within inflammatory and structural signaling clusters. The DEGs encompassed gene families such as collagens, proteases, fibulins, inflammatory and signaling cytokines, integrins and signaling receptors and kinases. Notably, MYC was upregulated alongside chemokine ligands and receptors, including CXCR4, CXCL3, CXCL8, CCL2, CCL4 and CCL11, highlighting a potential MYC-linked chemokine signaling axis within the inflammatory HOCM-CFs phenotype. The protein expression of selected ‘extracellular Matrix organization’ and ‘inflammatory response’ genes confirmed the transcriptome results. Conclusion: Transcriptomic profiling of patient-derived HOCM-CFs identified candidate genes and pathways associated with inflammatory signaling, ECM remodeling, and altered cell–cell and cell–matrix communication. These findings show that advanced HOCM-CFs acquire an inflammatory-remodeling phenotype, with the involvement of ECM genes other than collagens.
This dataset
Open GSE337273 in finder → Find more human 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 GSE337273_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 PRJNA1484610 and SRA study SRP714388. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
15 samples, each with a distinct title — no replicated groups

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

How we read this: Read from 15 sample titles: 15 distinct titles with little repetition. 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
Datasets on a similar topic

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