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Integrative proteo-transcriptomic characterization of advanced fibrosis in chronic liver disease across etiologies

GSE276114 Homo sapiens (human) RNA-seq (gene expression) 177 samples Submitted 2025/01/19 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 177 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)
Topic
Hepatocellular carcinomaThis series is part of our curated briefing on it.
Samples
177 samples: 177 × Liver sampleGroups 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
Integrative proteo-transcriptomic characterization of advanced fibrosis in chronic liver disease across etiologiesCell reports. Medicine · 2025
Summary (written by the submitters)
While technological advancements have improved our understanding of fibrogenesis, identifying advanced fibrosis in the general population remains challenging due to clinical heterogeneity influenced by underlying causes, comorbidities, and lifestyle factors. A systems-level characterization of metabolic and signaling dysregulation could effectively capture the signatures driving liver fibrosis across different etiologies.
Read the full summary ↓
In this study, we utilized a data-driven multi-omics approach, encompassing liver transcriptomics and plasma proteomics, to thoroughly characterize patients across the pathological spectrum, from early-stage fibrosis to cirrhosis and associated HCC.
Published in
Integrative proteo-transcriptomic characterization of advanced fibrosis in chronic liver disease across etiologies
Yang H, Atak D, Yuan M et al. · Cell reports. Medicine 2025 · PMID 39889710 · doi:10.1016/j.xcrm.2025.101935
This dataset
Open GSE276114 in finder → Find more human RNA-seq datasets → Hepatocellular carcinoma landscape →
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 GSE276114_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 PRJNA1154708 and SRA study SRP529752. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39889710) ↗
Study design
177 × Liver sample

Supports a between-group comparison across 177 samples.

How we read this: 1 replicated groups read from 177 sample titles; they account for 177 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

+ 137 more — browse all 177 samples with per-sample file links →

More Hepatocellular carcinoma datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.