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Predicting patient outcome based on gene-expression biomarkers learned from colorectal cancer organoids and cell lines

GSE251958 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2025/01/08 Platform GPL21697
At a glance

RNA-seq (gene expression) · human · 21 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
21 samples: 7 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
Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell linesFrontiers in molecular biosciences · 2025
Summary (written by the submitters)
Colorectal cancer is characterized by an extremely high mortality rate, mainly caused by the high metastatic potential of this type of cancer. To date chemotherapy remains the backbone of the treatment of metastatic colorectal cancer. Three main chemotherapeutic drugs used for treatment of metastatic colorectal cancer are 5-FU, oxaliplatin and irinotecan which is metabolized to an active compound SN-38.
Read the full summary ↓
The main goal of this study was to find the genes connected to the resistance to the aforementioned drugs and to construct predictive gene expression-based classifier to separate responders and non-responders. In this study we used 7 established colorectal cancer organoids to conduct correlational analysis of the expression and drug resistance. We also included in the study publicly available datasets of colorectal cancer cell lines. The obtained results demonstrated that the expression of just a small group of genes consistently correlate with resistance to standard chemotherapeutic drugs in different datasets. Some of these genes have been previously connected to prognosis or response to anticancer drugs, but some of them were linked to the drug resistance for the first time. The classifier constructed on the expression levels of some of these genes allows to discriminate between patients with favorable and unfavorable 5-year OS prognosis and can be further validated in clinic to improve the results of the treatment.
Published in
Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell lines
Razumovskaya A, Silkina M, Poloznikov A et al. · Frontiers in molecular biosciences 2025 · PMID 39886381 · doi:10.3389/fmolb.2025.1531175
This dataset
Open GSE251958 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 GSE251958_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 PRJNA1056027 and SRA study SRP479754. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39886381) ↗
Study design
7 conditions, mostly in triplicate
TO Patient1 Control ×3 TO Patient2 Control ×3 TO Patient3 Control ×3 TO Patient4 Control ×3 TO Patient5 Control ×3 TO Patient6(1) Control ×3 TO Patient6(2) Control ×3

Supports a between-group comparison across 21 samples.

How we read this: 7 replicated groups read from 21 sample titles; they account for 21 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

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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.