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NanoCMSer: A Clinically Applicable Tool for Stratification of Colorectal Cancer Samples [March2024.MATCH]

GSE262671 Homo sapiens (human) RNA-seq (gene expression) 113 samples Submitted 2025/05/22 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 113 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
113 samples: 113 × Primary CRC tumor samples (FF), PGroups 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
NanoCMSer: a consensus molecular subtype stratification tool for fresh-frozen and paraffin-embedded colorectal cancer samplesMolecular oncology · 2025
Summary (written by the submitters)
Colorectal cancer (CRC) is a significant contributor to global cancer-related mortality, emphasizing the critical need for advanced predictive biomarkers to guide treatment decisions. In our pursuit of identifying novel biomarkers, as part of an international consortium, we have previously categorized CRCs into four transcriptome-based consensus molecular subtypes (CMS1-4).
Read the full summary ↓
These subtypes show promise for predicting outcomes and facilitating biological investigations. To streamline the integration of CMS classification into clinical practice, where routinely formalin-fixed paraffin-embedded (FFPE) samples are used, we developed NanoCMSer. This NanoString-based CMS classifier reliably stratifies both fresh-frozen (FF) and FFPE samples using a concise panel of just 55 genes. Employing domain adaptation methods during training enhanced the generalizability of the classifier, resulting in high accuracy for both FF and FFPE samples. For FF NanoString-based datasets, an accuracy of 95% and for FFPE NanoString-based datasets an accuracy of 92% was achieved, which is the highest reported for FFPE tissues. Furthermore, using the NanoCMSer, a 96% accuracy across a comprehensive collection of 1,976 RNAseq-based samples from 23 datasets aggregated in this study was determined. Additionally, our findings highlight that even with a limited number of genes, the biological relevance of predictions at the individual sample level can be still identified, with distinct CMS biology readily recognizable upon conducting enrichment analysis. Furthermore, our observations revealed a substantial difference in 5-year recurrence free survival (RFS) for CMS2 and CMS3 patients in stage III compared to II (CMS2 21% more recurrences, CMS3 31% more recurrences), while this difference is less pronounced for CMS1 and CMS4 with 11% and 9%, respectively. We posit NanoCMSer as a robust tool with potential applications to assess the CMS status of CRCs in both tumor biology and clinical practice, accessible through the NanoCMSer R package.
Published in
NanoCMSer: a consensus molecular subtype stratification tool for fresh-frozen and paraffin-embedded colorectal cancer samples
Torang A, van de Weerd S, Lammers V et al. · Molecular oncology 2025 · PMID 39720854 · doi:10.1002/1878-0261.13781
This dataset
Open GSE262671 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 GSE262671_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 PRJNA1092888. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39720854) ↗
Study design
113 × Primary CRC tumor samples (FF), P

Supports a between-group comparison across 113 samples.

How we read this: 1 replicated groups read from 113 sample titles; they account for 113 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

+ 73 more — browse all 113 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.