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Chemotherapy Modulation by a Cancer-Associated Microbiota Metabolite

GSE263706 Homo sapiens (human) RNA-seq (gene expression) 56 samples Submitted 2025/09/15 Platform GPL18573Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 56 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)
Samples
56 samples: 14 conditions, mostly with about 4 replicates eachGroups 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
Chemotherapy modulation by a cancer-associated microbiota metaboliteCell systems · 2025
Summary (written by the submitters)
The microbiota plays a major role in cancer. How the microbiota interacts with nutrients to produce regulatory metabolites is of significance for cancer therapy. Using a host-microbe-drug-nutrient 4-way screening approach, we evaluated the role of nutrition at the molecular level in the context of 5-fluorouracil toxicity. Notably, we identified the metabolite 2-methylisocitrate to be produced and enriched in human tumor-associated microbiomes.
Read the full summary ↓
2-methylisocitrate exhibits anti-proliferative properties across genetically- and tissue-diverse cancer cell lines, 3D spheroids, and an in vivo Drosophila gut tumor model, where it reduced tumor dissemination and increased survival. Drug-metabolite screening traced the chemotherapeutic signatures indicating synergy between 5-fluorouracil and 2-methylisocitrate, and multi-omic analyses revealed that 2-methylisocitrate acts via multiple cellular pathways linking metabolism and DNA damage to regulate chemotherapy. Finally, building on nature’s template, we altered the chemical structure of 2-methylisocitrate, enhancing its potency. This work highlights the great impact of microbiome-derived metabolites on tumor proliferation, and their potential as promising co-adjuvants for cancer treatment.
Published in
Chemotherapy modulation by a cancer-associated microbiota metabolite
Martinez-Martinez D, Peres TV, Gehling K et al. · Cell systems 2025 · PMID 40934913 · doi:10.1016/j.cels.2025.101397
This dataset
Open GSE263706 in finder → Find more human RNA-seq datasets →
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 GSE263706_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 PRJNA1098747 and SRA study SRP500824. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40934913) ↗
Study design
14 conditions, mostly with about 4 replicates each
HCT116_C ×4 HCT116_M ×4 DLD1_C ×4 DLD1_M ×4 LoVo_C ×4 LoVo_M ×4 SW948_C ×4 SW948_M ×4 +6 more

Supports a between-group comparison across 56 samples.

How we read this: 14 replicated groups read from 56 sample titles; they account for 56 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

+ 16 more — browse all 56 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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