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Effect of fructose on human colon DNA methylation: implication for racial disparities in colorectal cancer risk

GSE261717 Homo sapiens (human) DNA methylation 10 samples Submitted 2025/03/15 Platform GPL24676
At a glance

DNA methylation · human · 10 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Topic
Colorectal cancerThis series is part of our curated briefing on it.
Samples
10 samples: 10 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
Association between dietary fructose and human colon DNA methylation: implication for racial disparities in colorectal cancer risk using a cross-sectional studyThe American journal of clinical nutrition · 2025
Summary (written by the submitters)
Background: An increasing body of evidence has linked fructose intake to colorectal cancer (CRC). African Americans (AAs) consume greater quantities of fructose and are more likely to develop right-side colon cancer than European Americans. Objective: We examined the hypothesis that fructose consumption leads to genomic differences associated with CRC tumor biology.
Read the full summary ↓
Methods: DNA methylation data from this study was obtained using the Illumina Infinium MethylationEPIC kit (GSE151732). Right and left colon differentially methylated regions (DMRs) were identified using DMRcate through analysis of fructose consumption in normal AA colon biopsies (n=79) undergoing screening colonoscopy. Secondary analysis of CRC tumors was carried out using data derived from TCGA-COAD, GSE101764 and GSE193535. Right colon organoids derived from AA normal colon tissues were exposed to 4.4mM of fructose for 72 hours. Fructose-associated differentially expressed genes (DEGs) were identified using DESeq2. This package was also used to identify DEGs in CRC tumors from TCGA-COAD. Results: We identified 4,263 right colon fructose-associated DMRs (FDR<0.05). In contrast, only 24 DMRs survived multiple testing corrections (FDR<0.05) in matched, left colon. Almost 50% of right colon fructose-associated DMRs overlapped regions implicated in CRC in at least one of three datasets. A highly significant enrichment was also observed between genes corresponding to right colon fructose-associated DMRs and DEGs associated with fructose exposure in AA right colon organoids (P=3.28E-30). Further, overlapping and significant enrichments for a number of fatty acid metabolism, glycolysis and cell proliferation pathways were also found. By further examining the overlap of genes within these pathways that were also differentially expressed in TCGA-COAD, our analysis reveals potential role for PFKP and ANKRD23 in fructose-mediated CRC risk. Conclusions: Our data support that dietary fructose exerts a greater CRC risk-related effect in right than left colon among AAs, alluding to its potential role in contributing to racial disparities in CRC.
Published in
Association between dietary fructose and human colon DNA methylation: implication for racial disparities in colorectal cancer risk using a cross-sectional study
Devall MA, Eaton S, Hu G et al. · The American journal of clinical nutrition 2025 · PMID 39788295 · doi:10.1016/j.ajcnut.2025.01.005
This dataset
Open GSE261717 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 GSE261717_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: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1088155 and SRA study SRP495317. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39788295) ↗
Study design
10 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 10 sample titles: 10 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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