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Endothelial exosomes remodel chromatin accessibility and superenhancer in cancer cells to promote liver metastatic colonization

GSE329420 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 26 samples Submitted 2026/05/01 Platform GPL15456Platform GPL20795
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 26 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
Uveal melanomaThis series is part of our curated briefing on it.
Samples
26 samples: 26 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.
Summary (written by the submitters)
Liver-organotropic metastasis occurs in cancers such as uveal melanoma (UM) and pancreatic ductal adenocarcinoma (PDAC) and is a leading cause of mortality at the terminal stage. Colonization is a rate-limiting and converging step in the metastatic cascade. The drivers underlying liver colonization have largely remained elusive.
Read the full summary ↓
In this report, we hypothesized that pioneer transcription factors (TFs) increase chromatin accessibility and SE remodeling, which subsequently promote colonization-associated cellular features (e.g., proliferation, survival, cancer stem cells (CSCs), motility) by driving the transcription of the respective SE-associated genes. We tested this hypothesis and discovered that the pioneer TF KLF4 promoted chromatin accessibility and SE remodeling by ATAC-seq and ChIP-seq. Chromatin accessibility and SE remodeling significantly increased the transcription of the SE-associated genes MCL-1 and RASGRP3, which consequently promoted proliferation, motility, and metastasis. Furthermore, the upregulation of KLF4 expression in UM cells was promoted by RN7SL2-containing exosomes from surrounding endothelial cells. Analysis of clinical datasets revealed that the overexpression of RIG-I, STAT1, KLF4 and RASGRP3 was individually correlated with shorter metastasis-free survival or overall survival in patients with UM and PDAC. In conclusion, the identification of the exoRN7SL2-STAT1-KLF4-SEs-RASGRP3 signaling axis may help elucidate metastatic colonization through interactions between tumor cells and the endothelial niche and may yield novel and viable therapeutic targets.
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 GSE329420_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1458847 and SRA study SRP695757. Searching any of these in the dataset finder brings you back here.

Study design
26 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 26 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
More Uveal melanoma 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.