← BioTransfer GEO Dataset Finder
GEO series

Epigenetic Subtypes of High-Grade T1 Bladder Cancer Reveal Intra-Tumor Heterogeneity and Distinct Interactions with Tumor Microenvironment

GSE281743 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 41 samples Submitted 2026/06/10 Platform GPL24676Platform GPL34284
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 41 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Topic
Bladder cancerThis series is part of our curated briefing on it.
Samples
41 samples: 26 distinct titles, 2 of them repeatedGroups 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
Epigenetic subtypes of high-grade T1 bladder cancer reveal intra-tumor heterogeneity and distinct interactions with tumor microenvironmentTheranostics · 2026
Summary (written by the submitters)
High-Grade T1 (HGT1) Non-Muscle Invasive Bladder Cancer (NMIBC) is a clinically heterogeneous disease, characterized by unpredictable treatment responses and limited tools for recurrence prediction. Although molecular classification efforts have been made, patient stratification still primarily relies on clinicopathological features, which offer limited clinical precision.
Read the full summary ↓
In this study, we integrated chromatin profiling in bulk with single-nuclei(sn) RNA-seq, immunohistochemistry and spatial transcriptomics to define epigenetic subtypes of HGT1, characterize their heterogeneity, and investigate tumor–microenvironment interactions. Our findings reveal distinct chromatin profiles differentiating urothelial (URO) and micropapillary (MP) histological variants of high-risk HGT1. We identified three epigenetic states: two within the URO group, luminal-like inflammatory (LLI) and basal-like (BL), and a separate signature unique to MP tumors. Single-cell and spatial resolution approaches validated intratumoral heterogeneity and provided insight into subtype-specific microenvironmental contexts. Notably, ∼40% of URO tumors exhibited spatially distinct coexisting LLI and BL components. BL regions showed enrichment for angiogenesis and hypoxia pathways and were preferentially located near vascular stroma, while LLI regions showed to be located at the core of the tumor. MP tumors featured a markedly different microenvironment, characterized by diverse populations of cancer-associated fibroblasts (CAFs) and M2-polarized macrophages intermingled with tumor cells, suggesting a more immunosuppressive niche which could account for their worse clinical outcome. URO tumors, by comparison, showed a more immune-excluded phenotype. These findings provide a detailed molecular and spatial map of bladder cancer heterogeneity, highlighting how distinct epigenetic subtypes and histological variants are associated with tumor architecture and microenvironmental interactions. Our data underscore the need for subtype-specific therapeutic strategies to more effectively address the complexity already existing at HGT1 bladder cancer.
Published in
Epigenetic subtypes of high-grade T1 bladder cancer reveal intra-tumor heterogeneity and distinct interactions with tumor microenvironment
Bellmunt J, Xie Y, Juanpere N et al. · Theranostics 2026 · PMID 42244999 · doi:10.7150/thno.129729
This dataset
Open GSE281743 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE281743_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 peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1185647 and SRA study SRP544911. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42244999) ↗
Study design
26 distinct titles, 2 of them repeated
ucc ×11 MP ×6

How we read this: Read from 41 sample titles: 26 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

+ 1 more — browse all 41 samples with per-sample file links →

More Bladder cancer datasets

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

Share this dataset

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.