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Transcriptomic Characterization of Liver Tissue Changes in Human and Mice with Acute-on-Chronic Liver Failure and Liver Cirrhosis/Fibrosis

GSE298435 Mus musculus (mouse) RNA-seq (gene expression) 76 samples Submitted 2026/02/13 Platform GPL24676Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 76 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
Mus musculus (mouse)
Samples
76 samples: 8 conditions, mostly with about 5 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
Cross-species hepatic transcriptomics identify conserved immune-metabolic reprogramming in acute-on-chronic liver failure progressionFrontiers in immunology · 2026
Summary (written by the submitters)
This study aims to investigate gene expression differences at the transcriptional level in liver tissue from acute-on-chronic liver failure (ACLF) compared to liver cirrhosis (CLD, human) or Fibrosis (mice) and healthy control groups.For human samples, they were categorized into ACLF, CLD, and healthy control (HC) groups. For mouse samples, they were divided into ACLF, Fibrosis, and HC groups.
Published in
Cross-species hepatic transcriptomics identify conserved immune-metabolic reprogramming in acute-on-chronic liver failure progression
Chen P, Song Y, Lin X et al. · Frontiers in immunology 2026 · PMID 41816324 · doi:10.3389/fimmu.2026.1702689
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 GSE298435_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 PRJNA1269436 and SRA study SRP588762. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly with about 5 replicates each
CLD ×15 ACLF ×12 HC ×9 ACLF_48h ×5 ACLF_60h ×5 ACLF_72h ×5 Fibrosis ×4 ACLF_84h ×4 +17 more

Supports a between-group comparison across 59 samples.

How we read this: 8 replicated groups read from 76 sample titles; they account for 59 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

+ 36 more — browse all 76 samples with per-sample file links →

Datasets on a similar topic

Search all 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.