GEO series

CTCFL(BORIS) contributes to the transcriptional program of ovarian cancer by relaxing CTCF-mediated three-dimensional genome organization

GSE341599 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 32 samples Submitted 2026/07/27 Platform GPL24676

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 32 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: Ovarian cancerThis series is part of our curated briefing on it.
Samples: 32 samples: 32 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)

Disruption of three-dimensional genome architecture is a major driver of cancer initiation and progression, and genetic or epigenetic alterations of CTCF- and cohesin-bound chromatin anchors are among the principal mechanisms underlying this process. These same chromatin loop anchors can also be occupied by the germ cell–specific CTCF paralog CTCFL (BORIS), which is aberrantly reactivated in multiple malignancies.

Read the full summary ↓

Here, we show that in ovarian cancer cells, BORIS reactivation establishes a distinct transcriptional program that collapses upon BORIS knockout and is accompanied by widespread changes in CTCF and cohesin occupancy, histone modifications, and chromatin accessibility. These BORIS-dependent transcriptional alterations occur in long-range genomic clusters, resulting in the coordinated activation or repression of neighboring genes, including hormonally regulated gene families. BORIS loss also increases topologically associating domain (TAD) insulation, strengthens A/B compartment segregation and chromatin loop interactions, and results in a more compact and constrained chromatin architecture. Together, our findings suggest that aberrant BORIS activation promotes transcriptional reprogramming by weakening CTCF-mediated chromatin insulation and relaxing three-dimensional genome organization in ovarian cancer.

This dataset

Open GSE341599 in finder → Find more human datasets → Ovarian 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 GSE341599_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 PRJNA1502992 and SRA study SRP721774. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

32 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 32 sample titles: 32 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

GSM9913799 ChIP-seq on OVCAR8 WT cells with BORIS antibodies
GSM9913800 ChIP-seq on OVCAR8 WT cells with CTCF antibodies
GSM9913801 ChIP-seq on OVCAR8 WT cells with H2AZ antibodies
GSM9913802 ChIP-seq on OVCAR8 WT cells with H3K27ac antibodies
GSM9913803 ChIP-seq on OVCAR8 WT cells with H3K4me3 antibodies
GSM9913804 ChIP-seq on OVCAR8 WT cells with RAD21 antibodies
GSM9913805 ChIP-seq on OVCAR8 WT cells with SMC3 antibodies
GSM9913806 ChIP-seq on OVCAR8 ko1 cells with BORIS antibodies
GSM9913807 ChIP-seq on OVCAR8 ko1 cells with CTCF antibodies
GSM9913808 ChIP-seq on OVCAR8 ko1 cells with H2AZ antibodies
GSM9913809 ChIP-seq on OVCAR8 ko1 cells with H3K27ac antibodies
GSM9913810 ChIP-seq on OVCAR8 ko1 cells with H3K4me3 antibodies
GSM9913811 ChIP-seq on OVCAR8 ko1 cells with RAD21 antibodies
GSM9913812 ChIP-seq on OVCAR8 ko1 cells with SMC3 antibodies
GSM9913813 ChIP-seq on OVCAR8 ko2 cells with BORIS antibodies
GSM9913814 ChIP-seq on OVCAR8 ko2 cells with CTCF antibodies
GSM9913815 ChIP-seq on OVCAR8 ko2 cells with H2AZ antibodies
GSM9913816 ChIP-seq on OVCAR8 ko2 cells with H3K27ac antibodies
GSM9913817 ChIP-seq on OVCAR8 ko2 cells with H3K4me3 antibodies
GSM9913818 ChIP-seq on OVCAR8 ko2 cells with RAD21 antibodies
GSM9913819 ChIP-seq on OVCAR8 ko2 cells with SMC3 antibodies
GSM9913820 Input for ChIP-seq on OVCAR8 ko2 cells
GSM9913821 Input for ChIP-seq on OVCAR8 ko1 cells
GSM9913822 Input for ChIP-seq on OVCAR8WT cells
GSM9913823 ATAC-seq on OVCAR8 WT cells
GSM9913824 ATAC-seq on OVCAR8 ko1 cells
GSM9913825 ATAC-seq on OVCAR8ko2 cells
GSM9913826 RNA-seq on OVCAR8 WT cells
GSM9913827 RNA-seq on OVCAR8 ko1 cells
GSM9913828 RNA-seq on OVCAR8ko2 cells
GSM9913829 HiC on OVCAR8 WT cells
GSM9913830 HiC on OVCAR8 ko2 cells

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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.