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Tumor-borne ADAMTSL4 drives cancer cachexia via TGFβ signaling pathway

GSE304945 Mus musculus (mouse) RNA-seq (gene expression) 32 samples Submitted 2026/06/16 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: 32 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.
Summary (written by the submitters)
Cancer cachexia (CCx) is a multifactorial wasting disorder in patients with cancer characterized by involuntary body weight loss. Whereas CCx is associated with reduced quality of life and poor survival, no approved pharmacological intervention exists. Here we show that elevated circulating levels of the tumour-derived glycoprotein ADAMTSL4 were associated with weight loss in various preclinical CCx models as well as in cachectic patients with different cancers.
Read the full summary ↓
ADAMTSL4 overexpression in non-cachexia-inducing tumour cells conferred cachexia-inducing properties upon exposure of cultured myocytes and adipocytes as well as implantation in mice. Reversely, genetic ADAMTSL4 deletion in cachexia-inducing cancer cells markedly repressed wasting phenotypes both in vitro and in vivo. Ligand-receptor capture technology identified the latency-associated peptide of TGF-β1 as ADAMTSL4 target on muscle cell surface. ADAMTSL4 mediated muscle atrophy and adipose tissue wasting through local TGF-β1 activation, sensitive to inhibition of TGFβR1 or knockdown of proTGF-β1. Given the conserved correlation between circulating ADAMTSL4 and skeletal muscle TGF pathway component expression in an independent human cancer patient cohort, our data suggest that blockade of ADAMTSL4 function represents a first rational approach towards efficient cancer cachexia pharmacotherapy.
This dataset
Open GSE304945 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 GSE304945_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 PRJNA1303573 and SRA study SRP607586. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
32 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 32 sample titles: 32 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

Search all mouse 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.