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The bladder cancer m6A landscape is defined by global methylation dilution and focal 3′-UTR hypermethylation

GSE281750 Homo sapiens (human) DNA methylation 54 samples Submitted 2025/07/18 Platform GPL24676
At a glance

DNA methylation · human · 54 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Topic
Bladder cancerThis series is part of our curated briefing on it.
Samples
54 samples: 14 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
The bladder cancer m(6)A landscape is defined by global methylation dilution and focal 3'-UTR hypermethylationEMBO reports · 2026
Summary (written by the submitters)
N6-Methyladenosine (m6A) is the most abundant internal modification of eukaryotic mRNAs and regulates target transcripts throughout the mRNA life cycle. Although changes in m6A have been reported in human cancers, technical limitations have hindered a comprehensive understanding of the cancer-associated m6A landscape. Here, we used GLORI-sequencing to establish the first transcriptome-wide, single-nucleotide resolution maps of m6A in cancer.
Read the full summary ↓
Differentially methylated transcripts were enriched in oncogenic pathways relevant to urothelial carcinoma of the bladder (UCB). We discovered two key m6A signatures in UCB: a global loss of methylation and a local hypermethylation at 3′-UTRs. Integration of RNA-sequencing data revealed that the global loss resulted from dilution of methylation marks due to increased expression of unmethylated transcripts and decreased expression of highly methylated transcripts. In contrast, local 3′-UTR hypermethylation was associated with the overexpression of VIRMA, a component of the m6A writer complex, which was linked to UCB progression. Our study is the first to describe the m6A epitranscriptomic landscape of cancer at single-base resolution and provides first insights into the processes that generate its characteristic signatures.
Published in
The bladder cancer m(6)A landscape is defined by global methylation dilution and focal 3'-UTR hypermethylation
Koch J, Xu J, Bormann F et al. · EMBO reports 2026 · PMID 41872550 · doi:10.1038/s44319-026-00739-y
This dataset
Open GSE281750 in finder → Find more human RNA-seq datasets → Bladder 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 GSE281750_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 PRJNA1185642 and SRA study SRP544939. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41872550) ↗
Study design
14 conditions, mostly in triplicate
Paratumoral ×9 Tumoral ×9 T24_DMSO ×3 T24_STM2457 ×3 UMUC3_DMSO ×3 UMUC3_STM2457 ×3 T24_Ctrl ×3 T24_KO ×3 +6 more

Supports a case/control comparison: 18 samples read as cases, 18 as controls.

How we read this: 14 replicated groups read from 54 sample titles; they account for 54 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

+ 14 more — browse all 54 samples with per-sample file links →

More Bladder 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.