GEO series

Activated Interferon Signaling Suppresses Age-Dependent Liver Cancer [RNAseq_WholeLiver]

GSE313644 Mus musculus (mouse) Single-cell RNA-seq 18 samples Submitted 2026/02/11 Platform GPL19057

At a glance

Single-cell RNA-seq · mouse · 18 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 18 samples: 5 × Young high fat diet vs 5 × Old normal diet vs 4 × Young normal diet vs 4 × Old high fat dietGroups 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: Rapamycin Reverses the Hepatic Response to Diet-Induced Metabolic Stress That Is Amplified by AgingAging cell · 2026

Summary (written by the submitters)

Age is a major risk factor for liver cancer, as is the case for most adult human cancers. However, the underlying mechanisms are not well defined. A better understanding of the role of aging in liver and other cancers can facilitate approaches for risk assessment, early detection and prevention. We hypothesize that age-driven changes render aged liver more sensitive to oncogenic stress and hence tumorigenesis.

Read the full summary ↓

To investigate how the liver changes with age, we documented the immune profile, transcriptome and epigenome of healthy livers from both young and aged mice, revealing pronounced alterations with aging. Notably, in aged hepatocytes, we identified heightened interferon (IFN) signaling, as well as simultaneous tumor suppressor and oncogene signaling at both bulk and single cell level, suggestive of an aged liver that is poised for neoplasia. To challenge this seemingly poised state, we employed adeno-associated virus (AAV)-mediated expression of a c-Myc oncogene in young and aged mouse liver hepatocytes in vivo. Analysis of aged hepatocytes expressing c-Myc revealed further elevated expression of IFN Stimulated Genes (ISGs). This ISG upregulation was evident in multiple models of oncogenic stress and transformation in older mice and also observed in aged humans with Metabolic dysfunction-Associated Steatohepatitis (MASH). We determined that Stat1 is both necessary and sufficient for the age specific elevated ISG expression in old wild type mice. Remarkably, inhibiting Jak/Stat signaling alongside ectopic c-Myc expression led to high-grade hepatocyte dysplasia and tumor formation, selectively in aged mice. Together, these results suggest that an aged liver is in a state of “precarious balance”, due to concurrent activation of oncogenic and tumor suppressor pathways, but protected against neoplastic progression by IFN-signaling. Age-dependent activation of IFN signaling has been observed in many tissues and recent studies have demonstrated its detrimental consequences on aging, raising the question as to why IFN-signaling is activated during aging. We propose that aged tissues are intrinsically at higher risk of cancer and age-dependent activation of IFN-signaling is an adaptive process to protect from tumorigenesis, but one that also has maladaptive consequences.

Published in

Rapamycin Reverses the Hepatic Response to Diet-Induced Metabolic Stress That Is Amplified by Aging

Havas A, Rajesh A, Lei X et al. · Aging cell 2026 · PMID 41653162 · doi:10.1111/acel.70395

This dataset

Open GSE313644 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 GSE313644_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1380452 and SRA study SRP654870. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41653162) ↗

Study design

5 × Young high fat diet vs 5 × Old normal diet vs 4 × Young normal diet vs 4 × Old high fat diet

Supports a between-group comparison across 18 samples.

How we read this: 4 replicated groups read from 18 sample titles; they account for 18 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

GSM9371644 Young high fat diet replicate 1 Young high fat diet
GSM9371645 Young high fat diet replicate 2 Young high fat diet
GSM9371646 Young high fat diet replicate 3 Young high fat diet
GSM9371647 Young high fat diet replicate 4 Young high fat diet
GSM9371648 Young high fat diet replicate 5 Young high fat diet
GSM9371649 Young normal diet replicate 1 Young normal diet
GSM9371650 Young normal diet replicate 2 Young normal diet
GSM9371651 Young normal diet replicate 3 Young normal diet
GSM9371652 Young normal diet replicate 4 Young normal diet
GSM9371653 Old high fat diet replicate 1 Old high fat diet
GSM9371654 Old high fat diet replicate 2 Old high fat diet
GSM9371655 Old high fat diet replicate 3 Old high fat diet
GSM9371656 Old high fat diet replicate 4 Old high fat diet
GSM9371657 Old normal diet replicate 1 Old normal diet
GSM9371658 Old normal diet replicate 2 Old normal diet
GSM9371659 Old normal diet replicate 3 Old normal diet
GSM9371660 Old normal diet replicate 4 Old normal diet
GSM9371661 Old normal diet replicate 5 Old normal diet

Datasets on a similar topic

GSE313654 Activated Interferon Signaling Suppresses Age-Dependent Liver Cancer [RNAseq_MycRux] 58 samples shares age-dependent, interferon, suppresses, activated, liver
GSE313646 Activated Interferon Signaling Suppresses Age-Dependent Liver Cancer [RNAseq_Hepatocytes] 19 samples shares age-dependent, interferon, suppresses, activated, liver
GSE237306 Embryo implantation failure with defective luteinization and aberrant activated CD8+T cells in interferon-γ-induced hypophysitis 18 samples shares interferon, activated
GSE293222 High-Fat Diet-Induced Liver-Gut Crosstalk Suppresses Autophagy and Exacerbates Colitis [colon] 12 samples shares suppresses, liver
GSE217807 ARID1A suppresses R-loop mediated STING-Type I Interferon pathway activation of anti-tumor immunity 62 samples shares interferon, suppresses
GSE235036 Notch signaling suppresses neuroendocrine differentiation and alters the tumor immune microenvironment in advanced prostate cancer 13 samples shares suppresses
GSE304521 SEX-DEPENDENT TYPE I INTERFERON SIGNALING EXACERBATES ALZHEIMER'S PATHOLOGY 82 samples shares interferon
GSE309517 Inhibition of the cancer stem cell immune checkpoint SOAT1 suppresses regulatory T cell functions through a trans-cellular 20(S)-Hydroxycholesterol-GPR132 pathway in mice. 45 samples shares suppresses

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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.