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EZH2 alters aggressive variant prostate cancer subtype evolution, but is not required for neuroendocrine prostate cancer [RNAseq]

GSE297143 Mus musculus (mouse) RNA-seq (gene expression) 54 samples Submitted 2025/10/28 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 54 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
54 samples: 19 × DKOE vs 11 × T vs 4 × DKOGroups 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
EZH2 Suppression Diversifies Prostate Cancer Lineage Variant Evolution and Lacks Efficacy in Inhibiting Disease ProgressionCancer research · 2026
Summary (written by the submitters)
Advanced prostate cancer (PrCa) remains a leading cause of cancer-related death among men because nearly all patients progress on standard therapy that targets androgen receptor (AR) signaling. An important mechanism driving therapeutic resistance is lineage plasticity, the ability of PrCa cells to reprogram into AR independent lineage variants like neuroendocrine (NE) PrCa.
Read the full summary ↓
The histone methyltransferase EZH2 has been implicated in PrCa lineage plasticity and EZH2 inhibitors are being evaluated clinically for the treatment of PrCa. Here we test how Ezh2 impacts the evolution of PrCa lineage plasticity using genetically engineered mice and human patient data. Ezh2 deficiency in mice diversifies the evolution of PrCa lineage variants, including a newly discovered variant with high expression of neurofilament genes. Analogous PrCa lineage variants are detectable in human specimens where they similarly correlate with EZH2 expression. Lineage variant diversification is associated with activation of transcription factor programs and AR cistrome reprogramming that is normally repressed by Ezh2. These findings advance the understanding of PrCa lineage plasticity and have implications for targeting EZH2 as a PrCa therapy.
Published in
EZH2 Suppression Diversifies Prostate Cancer Lineage Variant Evolution and Lacks Efficacy in Inhibiting Disease Progression
Jacobi JJ, Wadosky KM, Jaiswal N et al. · Cancer research 2026 · PMID 41223330 · doi:10.1158/0008-5472.CAN-25-2747
This dataset
Open GSE297143 in finder → Find more mouse RNA-seq datasets →
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 GSE297143_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1262013. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41223330) ↗
Study design
19 × DKOE vs 11 × T vs 4 × DKO

Supports a between-group comparison across 34 samples.

How we read this: 3 replicated groups read from 54 sample titles; they account for 34 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.