← BioTransfer GEO Dataset Finder
GEO series

Pooled single-cell screening in colorectal cancer identifies transcriptional modules of clinical relevance unlocked by oncogenes

GSE299651 Homo sapiens (human) Single-cell RNA-seq 20 samples Submitted 2026/01/12 Platform GPL34281
At a glance

Single-cell RNA-seq · human · 20 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Topic
Colorectal cancerThis series is part of our curated briefing on it.
Samples
20 samples: 20 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
Pooled single-cell screen in colorectal cancer defines transcriptional modules linked to oncogenesMolecular systems biology · 2026
Summary (written by the submitters)
While oncogenic mutations shape colorectal cancer biology and therapy response, their prognostic value remains low. Cluster-based classification of patient cancer transcriptomes has shown greater promise for prognosis, yet these systems do not account for the roles of oncogenes in establishing cancer phenotypes. Here, we create and validate a prognostic classifier for colorectal cancer based on transcriptional programs induced by oncogenes.
Read the full summary ↓
To systematically investigate oncogenic drivers, we employed a barcoded library of colorectal cancer-associated oncogene variants across a panel of genetically diverse colorectal cancer cell lines. We profiled the transcriptomes of over 100,000 transgenic cells and used machine learning to define transcriptional modules capturing key functional traits. Our analysis revealed heterogeneity on the cell-to-cell level and context-dependent gene expression patterns induced by oncogenes. We identified overarching gene expression modules reflecting core oncogenic processes, including cancer cell plasticity, inflammatory response, replicative stress, and epithelial-to-mesenchymal transition. These modules enabled a functional classification that linked oncogenic signalling states to distinct transcriptional profiles. We demonstrated their prognostic value by stratifying clinical colorectal cancer cohorts into high- and low-risk groups. Although partially correlated with established clinical parameters, the modules provided additional prognostic information, improving survival prediction and therapy stratification beyond existing classification systems. In summary, our study establishes a framework that connects oncogenic mutations to core transcriptional modules. By integrating experimental models with clinical data, we provide a resource for investigating colorectal cancer progression and oncogene-specific vulnerabilities, facilitating future research and precision oncology.
Published in
Pooled single-cell screen in colorectal cancer defines transcriptional modules linked to oncogenes
Hollek V, Böhning F, Florez Vargas C et al. · Molecular systems biology 2026 · PMID 41555096 · doi:10.1038/s44320-025-00186-2
This dataset
Open GSE299651 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 GSE299651_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 PRJNA1275837 and SRA study SRP591630. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41555096) ↗
Study design
20 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 20 sample titles: 20 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
More Colorectal cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.