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

GSE313646 Mus musculus (mouse) Single-cell RNA-seq 19 samples Submitted 2026/02/18 Platform GPL34290
At a glance

Single-cell RNA-seq · mouse · 19 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
19 samples: 19 samples, each with a distinct title — no replicated groupsGroups 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
Aging-induced hepatocyte CD44 drives IL6/STAT3 signaling and associates with impaired neighboring T cell functionbioRxiv : the preprint server for biology · 2025
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
Aging-induced hepatocyte CD44 drives IL6/STAT3 signaling and associates with impaired neighboring T cell function
Gandhi A, Lande K, Li Y et al. · bioRxiv : the preprint server for biology 2025 · PMID 41509421 · doi:10.64898/2025.12.20.695732
This dataset
Open GSE313646 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 GSE313646_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 PRJNA1380458 and SRA study SRP654872. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41509421) ↗
Study design
19 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 19 sample titles: 19 distinct titles with little repetition. 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
Datasets on a similar topic

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