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Single-cell gene expression profiles of cells from primary colorectal cancer and adjacent normal tissue [scRNA-Seq]

GSE294300 Homo sapiens (human) Single-cell RNA-seq 36 samples Submitted 2026/07/22 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 36 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
36 samples: 18 × adjacent normal cells, patient vs 18 × colorectal cancer cells, patientGroups 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
A multi-omics dissection of INHBA(+) CAF-mediated epithelial-mesenchymal transition and immune suppression in colorectal cancerJournal of translational medicine · 2026
Summary (written by the submitters)
Colorectal cancer (CRC) is the third malignancy worldwide. RAS mutant CRC has a worse prognosis and resistant to immune therapies. Current research on the tumor microenvironment of RAS-mutant CRC remains limited, with few studies systematically characterizing its cellular composition or functional dynamics.
Read the full summary ↓
A total of 36 clinical surgical samples (tumors and paired adjacent normal tissues) from 18 patients with primary CRC were collected for single-cell transcriptome sequencing.At the same time, each patient underwent genome sequencing, and 18 patients were divided into RAS gene mutation group (n=10) and wild-type group (n=8). Therefore, revealing the functional cell types and mechanism of immune evasion in RAS mutant CRC by scRNA-seq may contribute to discover new therapeutic targets.
Published in
A multi-omics dissection of INHBA(+) CAF-mediated epithelial-mesenchymal transition and immune suppression in colorectal cancer
Wang J, He Q, Hu J et al. · Journal of translational medicine 2026 · PMID 42143353 · doi:10.1186/s12967-026-08232-9
This dataset
Open GSE294300 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 GSE294300_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 PRJNA1248964 and SRA study SRP579713. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42143353) ↗
Study design
18 × adjacent normal cells, patient vs 18 × colorectal cancer cells, patient

Supports a between-group comparison across 36 samples.

How we read this: 2 replicated groups read from 36 sample titles; they account for 36 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
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.