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Enterocyte like differentiation defines metabolic gene signatures of CMS3 colorectal cancers and provides therapeutic vulnerability

GSE223323 Mus musculus (mouse) RNA-seq (gene expression) 148 samples Submitted 2024/11/14 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 148 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
148 samples: 31 conditions, mostly in triplicateGroups 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
Enterocyte-like differentiation defines metabolic gene signatures of CMS3 colorectal cancers and provides therapeutic vulnerabilityNature communications · 2025
Summary (written by the submitters)
Colorectal cancer (CRC) is stratified into four consensus molecular subtypes (CMS1-4). CMS3 represents the metabolic subtype, but its wiring remains largely undefined. To identify the underlying tumorigenesis of CMS3, organoids derived from 16 genetically engineered mouse models are analyzed. Upon in vitro Cre-recombinase activation, transformation is established and transcriptional profiling reveals that distinct CMSs (CMS2-4) are modeled with different organoids.
Read the full summary ↓
CMS3-like, metabolic signature-positive, organoids are induced by KRAS mutations. Interestingly, metabolic signatures are subsequently shown to result from enterocyte-like differentiation both in organoids and human cancers. Further analysis reveals carbamoyl-phosphate synthase 1 (CPS1) and sucrase-isomaltase (SI) as signature proteins. More importantly, CPS1 is crucial for de novo pyrimidine synthesis in CMS3 and its inhibition targets proliferation and stemness, facilitating enterocyte-like differentiation, while CMS2 and CMS4 models are not affected. Our data point to an enterocyte-like differentiation of CMS3 CRCs and reveal a selective vulnerability of this subtype through CPS1 inhibition. To investigate the effect of single and combined mutations in colon and small intestine of mouse models, we have generated a large set of organoids across 16 different genotypes zooming in on microsatellite stable CRC (CMS2-4). This set of organoids includes villinCreER Apcfl/fl (A), villinCreER Apcfl/fl Trp53fl/fl (AP), villinCreER Apcfl/fl Trp53R172H/fl (AP2), villinCreER Apcfl/fl KrasG12D/+ Trp53fl/fl (AKP), villinCreER Apcfl/fl KrasG12D/+ Trp53fl/fl Smad4fl/+ (AKPS), villinCreER KrasG12D/+ (K), villinCreER KrasG12D/+ Trp53fl/fl (KP), villinCreER KrasG12D/+ Trp53fl/fl Rosa26N1ind/+ (KPN), villinCreER BrafV600E/+ Trp53fl/fl Rosa26N1ind/+ (BPN), villinCreER BrafV600E/+ Trp53fl/fl Rosa26N1ind/+ Alk5fl/fl (BPNA2), villinCreER Apcfl/fl KrasG12D/+ (AK), villinCreER Apcfl/fl KrasG12D/+ Trp53R172H/fl (AKP2), villinCreER Apcfl/fl KrasG12D/+ Trp53fl/fl Rosa26N1ind/+ (AKPN), villinCreER Apcfl/fl Trp53fl/fl Rosa26N1ind/+ (APN), villinCreER BrafV600E/+ Trp53fl/fl (BP), and villinCreER KrasG12D/+ Trp53fl/fl Rosa26N1ind/+ Alk5fl/fl (KPNA2).
Published in
Enterocyte-like differentiation defines metabolic gene signatures of CMS3 colorectal cancers and provides therapeutic vulnerability
Torang A, Kirov AB, Lammers V et al. · Nature communications 2025 · PMID 39747069 · doi:10.1038/s41467-024-55574-3
This dataset
Open GSE223323 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 GSE223323_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 PRJNA925804 and SRA study SRP418407. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39747069) ↗
Study design
31 conditions, mostly in triplicate
F1.K.SIP.a ×4 F1.AP2.CO.a ×3 F1.AP2.SIP.a ×3 F1.AP2.CO.b ×3 F1.AP2.SIP.b ×3 F1.AKP.CO.a ×3 F1.AKP.SIP.a ×3 F1.AKP.SIP.b ×3 +85 more

Supports a between-group comparison across 86 samples.

How we read this: 31 replicated groups read from 148 sample titles; they account for 86 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

+ 108 more — browse all 148 samples with per-sample file links →

Datasets on a similar topic

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