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Epigenetic heterogeneity hotspots in human liver disease progression [RNA-seq]

GSE263786 Homo sapiens (human) RNA-seq (gene expression) 243 samples Submitted 2024/07/30 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 243 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
243 samples: 24 conditions, mostly with about 10 replicates eachGroups 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.
Paper
Epigenetic heterogeneity hotspots in human liver disease progressionHepatology (Baltimore, Md.) · 2025
Summary (written by the submitters)
Disruption of the epigenome is a hallmark of human disease including liver cirrhosis and hepatocellular carcinoma (HCC). While genetic heterogeneity is an established effector of pathologic phenotypes, epigenetic heterogeneity is less well understood. Environmental exposures including hepatitis C viral (HCV) infection and metabolic syndrome alter the liver-specific DNA methylation landscape and influence the onset of liver cancer.
Read the full summary ↓
Given that currently available treatments are unable to target the most frequently mutated genes in HCC, there is an unmet need for novel therapeutic strategies to prevent or reverse hepatic tumorigenesis, which the epigenome may provide. We performed genome-wide profiling of DNA methylation, copy number, and gene expression landscapes from multiple liver regions from 31 patients with liver disease to examine their crosstalk and define the individual and combinatorial contribution of these processes to liver disease progression. We identify epigenetic heterogeneity hotspots that are conserved across patients. Elevated epigenetic heterogeneity associates with increases in gene expression heterogeneity. Cirrhotic regions comprise two distinct cohorts, one exclusively epigenetic, and a second where epigenetic and copy number variations collaborate. Epigenetic heterogeneity hotspots are enriched for genes central to liver function (e.g., HNF1A) and known tumor suppressors (e.g., RASSF1A). These hotspots encompass genes including ACSL1, ACSL5, MAT1A, and ELFN1, which have phenotypic effects in cell line functional screens, supporting their relevance to hepatocarcinogenesis. Substantial epigenetic heterogeneity arises early in liver disease development, targeting key pathways in the progression and initiation of both cirrhosis and HCC. Integration of epigenetic and transcriptional heterogeneity unveils putative epigenetic regulators of hepatocarcinogenesis.
Published in
Epigenetic heterogeneity hotspots in human liver disease progression
Hlady RA, Zhao X, El Khoury LY et al. · Hepatology (Baltimore, Md.) 2025 · PMID 39028883 · doi:10.1097/HEP.0000000000001023
This dataset
Open GSE263786 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 GSE263786_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 PRJNA1099239 and SRA study SRP501225. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39028883) ↗
Study design
24 conditions, mostly with about 10 replicates each
Disease Liver R20 ×25 Disease Liver R14 ×19 Disease Liver R55 ×17 Disease Liver R2 ×12 Disease Liver R16 ×11 Disease Liver R8 ×11 Disease Liver R11 ×10 Disease Liver R19 ×10 +18 more

Supports a case/control comparison: 216 samples read as cases, 27 as controls.

How we read this: 24 replicated groups read from 243 sample titles; they account for 241 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

+ 203 more — browse all 243 samples with per-sample file links →

Datasets on a similar topic

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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.