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Increased translation driven by a non-canonical EZH2 cistrome creates a synthetic vulnerability in enzalutamide-resistant prostate cancer

GSE266956 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 42 samples Submitted 2024/10/22 Platform GPL20301Platform GPL24676Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 42 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
Mus musculus (mouse)
Topic
Prostate cancerThis series is part of our curated briefing on it.
Samples
42 samples: 6 conditions, mostly with about 6 replicates eachGroups 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
Increased translation driven by non-canonical EZH2 creates a synthetic vulnerability in enzalutamide-resistant prostate cancerNature communications · 2024
SuperSeries — this record groups several sub-series.
Summary (written by the submitters)
This SuperSeries is composed of the SubSeries listed below.
Published in
Increased translation driven by non-canonical EZH2 creates a synthetic vulnerability in enzalutamide-resistant prostate cancer
Chatterjee SS, Linares JF, Cid-Diaz T et al. · Nature communications 2024 · PMID 39567499 · doi:10.1038/s41467-024-53874-2
This dataset
Open GSE266956 in finder → Find more RNA-seq 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 GSE266956_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 PRJNA1108768 and SRA study SRP506218. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39567499) ↗
Study design
6 conditions, mostly with about 6 replicates each
RNSC ×6 TCD ×6 JFL ×6 MRC ×4 LNCaP-sgC_ENZA_IgG ×2 LNCaP-sgPRKCI_ENZA_IgG ×2 LNCaP_sgC_DMSO_IgG ×1 LNCaP-sgPRKCI_DMSO_IgG ×1 +14 more

Supports a between-group comparison across 26 samples.

How we read this: 6 replicated groups read from 42 sample titles; they account for 26 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

+ 2 more — browse all 42 samples with per-sample file links →

More Prostate cancer datasets

Search all RNA-seq 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.