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Decoding 3D chromatin architecture reveals distinct enhancer classes underlying hierarchical gene regulation in prostate cancer [ChIP-Seq]

GSE314776 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 24 samples Submitted 2026/06/15 Platform GPL34284Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 24 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
Prostate cancerThis series is part of our curated briefing on it.
Samples
24 samples: 12 conditions, mostly in duplicateGroups 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
Decoding 3D chromatin architecture reveals distinct enhancer classes underlying hierarchical gene regulation in prostate cancerGenome biology · 2026
Summary (written by the submitters)
Background: The transcription process is controlled by non-coding regulatory elements, more than 70% of which are putative enhancers. These enhancers comprise over 600,000 regions and are marked by histone modifications. However, the mechanisms by which altered enhancers in cancer cooperate within the three-dimensional chromatin architecture to drive oncogenic programs remain poorly understood.
Read the full summary ↓
Results: By integrating 201 H3K27ac ChIP seq datasets from prostate, we identify 3,216 high confidence prostate cancer-specific putative enhancers. Ultra high resolution chromatin interaction profiling by Region Capture Micro-C at a representative chr6q24.1 locus reveals that these enhancers form cancer specific, highly nested interactions with promoters that coalesce into a multi connected hub absent in normal prostate cells. CRISPR/Cas9 perturbations of these enhancers, examined one by one, distinguish enhancer classes within the hub. Deletion of a central enhancer collapses hub-wide enhancer activities and architecture, leading to the downregulation of target genes, impaired proliferation, and reduced clonogenic growth. In contrast, deletion of a redundant enhancer results in minimal transcriptional changes, as neighboring enhancers rescue cancer signaling through compensatory architectural rewiring that strengthens alternative enhancer-promoter interactions. We also observe that FOXA1, a pioneer transcription factor activated in prostate cancer, directly binds to these enhancers and regulates distinct enhancer classes, leading to varying degrees of chromatin accessibility and gene expression changes. Conclusions: These findings suggest that enhancers function in a coordinated manner, forming multi-connected cancer-specific chromatin interaction hubs, with distinct enhancer classes contributing differently to gene regulation. This study advances our ability to modulate gene expression in a cell type-specific manner, opening new avenues for precision therapies.
Published in
Decoding 3D chromatin architecture reveals distinct enhancer classes underlying hierarchical gene regulation in prostate cancer
Cao H, Wu Z, Ji B et al. · Genome biology 2026 · PMID 42487124 · doi:10.1186/s13059-026-04178-9
This dataset
Open GSE314776 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Prostate 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 GSE314776_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 PRJNA1392579 and SRA study SRP657432. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42487124) ↗
Study design
12 conditions, mostly in duplicate
22Rv1_H3K27ac_NTC ×2 22Rv1_H3K27ac_cPCE_del ×2 22Rv1_H3K27ac_rPCE_del ×2 22Rv1_H3K27ac_resPCE_del ×2 22Rv1_CTCF_NTC ×2 22Rv1_CTCF_cPCE_del ×2 22Rv1_FOXA1_NTC ×2 22Rv1_FOXA1_cPCE_del ×2 +4 more

Supports a between-group comparison across 24 samples.

How we read this: 12 replicated groups read from 24 sample titles; they account for 24 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
More Prostate cancer datasets

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