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Obesity promotes inflammatory, metabolic, and proliferative transcriptional programs in orthotopically transplanted murine colon tumors

GSE284066 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2025/10/06 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 15 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
Mus musculus (mouse)
Samples
15 samples: 15 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
Obesity promotes conserved inflammatory and metabolic transcriptional programs in colon tumors: evidence from mouse models and the ColoCare Study Patient CohortThe American journal of clinical nutrition · 2025
Summary (written by the submitters)
The mechanisms driving obesity-associated colon cancer progression are not completely understood. This study leveraged RNA sequencing data from control and obese C57BL/6 mice to identify obesity-associated transcriptomic signatures. Bulk EpCAM+ epithelial tumor transcriptomic analyses revealed obesity-driven enrichment of inflammation, proliferation, and metabolic pathways.
Published in
Obesity promotes conserved inflammatory and metabolic transcriptional programs in colon tumors: evidence from mouse models and the ColoCare Study Patient Cohort
Glenny EM, Lin T, Bandera VM et al. · The American journal of clinical nutrition 2025 · PMID 40998019 · doi:10.1016/j.ajcnut.2025.09.031
This dataset
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.

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 GSE284066_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1196938 and SRA study SRP550871. Searching any of these in the dataset finder brings you back here.

Study design
15 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 15 sample titles: 15 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
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