GEO series

Fibrogenic gene signature as early prediction for the efficacy of pharmacological interventions for MASH-associated fibrosis, part2

GSE290237 Mus musculus (mouse) RNA-seq (gene expression) 32 samples Submitted 2025/06/18 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 32 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: 32 samples: 8 × part2, C vs 8 × part2, H vs 8 × HCe vs 8 × HPiGroups 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: Fibrogenic Gene Signature as Early Prediction for the Efficacy of Pharmacological Interventions for MASH-Associated FibrosisFASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025

Summary (written by the submitters)

Background and aims: The incidence of metabolic dysfunction-associated steatohepatitis (MASH) and associated liver fibrosis is rapidly increasing, while pharmacological treatment options remain limited. Despite great efforts in developing novel MASH therapeutics, many investigative therapeutics that reduced fibrosis in preclinical models ultimately lacked efficacy in clinical trials.

Read the full summary ↓

Therefore, we explored the possibility to predict the efficacy of investigative therapeutics on fibrogenic drive in the early stages of disease development and before effects on pathology become evident. Methods: Ldlr-/-.Leiden mice were fed a high-fat diet (HFD) to induce obesity and MASH. Mice were subsequently treated for four weeks with various therapeutics with established efficacy (obeticholic acid) or lack of efficacy (cenicriviroc, pioglitazone) to study their anti-fibrotic potential. Next, expression of a fibrogenic gene signature was evaluated, which predicts profibrotic processes preceding histopathologic fibrosis. The predictions were validated in parallel with a long-term experiment reaching histological fibrosis endpoints. Results: Cenicriviroc and pioglitazone did not affect HFD-induced fibrosis signature, indicative of no effect of these treatments on active fibrosis processes. Consistently, in the long-term treatment study, both cenicriviroc and pioglitazone did not affect HFD-induced histologically measured fibrosis. In contrast, obeticholic acid improved the early fibrogenic gene signature indicating a less fibrotic state compared with untreated HFD controls. These early gene expression changes aligned with long-term histological fibrosis endpoints and clinical data on these investigative therapeutics. Conclusions: This study highlights the potential of using short-term studies and applying a fibrogenic gene signature as an early screening tool to investigate the efficacy of investigative drugs on MASH-associated fibrosis. This signature, which is based on the active fibrosis processes in humans, may allow rapid screening of therapeutics and their combinations in translational mouse models to save time and resources.

Published in

Fibrogenic Gene Signature as Early Prediction for the Efficacy of Pharmacological Interventions for MASH-Associated Fibrosis

Inia JA, Morrison MC, van Koppen A et al. · FASEB journal : official publication of the Federation of American Societies for Experimental Biology 2025 · PMID 40515541 · doi:10.1096/fj.202500718R

This dataset

Open GSE290237 in finder → Find more mouse 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 GSE290237_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 PRJNA1226631 and SRA study SRP565545. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40515541) ↗

Study design

8 × part2, C vs 8 × part2, H vs 8 × HCe vs 8 × HPi

Supports a between-group comparison across 32 samples.

How we read this: 4 replicated groups read from 32 sample titles; they account for 32 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

GSM8808991 part2, C_01 part2, C
GSM8808992 part2, C_02 part2, C
GSM8808993 part2, C_03 part2, C
GSM8808994 part2, C_04 part2, C
GSM8808995 part2, C_05 part2, C
GSM8808996 part2, C_06 part2, C
GSM8808997 part2, C_07 part2, C
GSM8808998 part2, C_08 part2, C
GSM8808999 part2, H_09 part2, H
GSM8809000 part2, H_10 part2, H
GSM8809001 part2, H_11 part2, H
GSM8809002 part2, H_12 part2, H
GSM8809003 part2, H_13 part2, H
GSM8809004 part2, H_14 part2, H
GSM8809005 part2, H_15 part2, H
GSM8809006 part2, H_16 part2, H
GSM8809007 HCe_25 HCe
GSM8809008 HCe_26 HCe
GSM8809009 HCe_27 HCe
GSM8809010 HCe_28 HCe
GSM8809011 HCe_29 HCe
GSM8809012 HCe_30 HCe
GSM8809013 HCe_31 HCe
GSM8809014 HCe_32 HCe
GSM8809015 HPi_33 HPi
GSM8809016 HPi_34 HPi
GSM8809017 HPi_35 HPi
GSM8809018 HPi_36 HPi
GSM8809019 HPi_37 HPi
GSM8809020 HPi_38 HPi
GSM8809021 HPi_39 HPi
GSM8809022 HPi_40 HPi

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GSE294996 Modular neuroinflammatory network discovery from large-scale phenotypic screening in genetically heterogeneous mice [part24] 231 samples shares part2

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