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Induction of CDX2 as a differentiation therapy in colorectal cancers

GSE237626 Homo sapiens (human) RNA-seq (gene expression) 57 samples Submitted 2025/09/24 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 57 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
Homo sapiens (human)
Topic
Colorectal cancerThis series is part of our curated briefing on it.
Samples
57 samples: 53 distinct titles, 2 of them repeatedGroups 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
CANDiT: A machine learning framework for differentiation therapy in colorectal cancerCell reports. Medicine · 2025
SuperSeries — this record groups several sub-series.
Summary (written by the submitters)
This SuperSeries is composed of the SubSeries listed below.
Published in
CANDiT: A machine learning framework for differentiation therapy in colorectal cancer
Sinha S, Alcantara J, Perry K et al. · Cell reports. Medicine 2025 · PMID 41118768 · doi:10.1016/j.xcrm.2025.102421
This dataset
Open GSE237626 in finder → Find more human RNA-seq datasets → Colorectal cancer landscape →
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 GSE237626_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 PRJNA996052 and SRA study SRP456658. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41118768) ↗
Study design
53 distinct titles, 2 of them repeated
Control mice_DMSO_Sample ×3 Treated mice_PF compound_Sample ×3

Supports a case/control comparison: 20 samples read as cases, 29 as controls.

How we read this: Read from 57 sample titles: 53 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

+ 17 more — browse all 57 samples with per-sample file links →

More Colorectal cancer datasets

Search all human 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.