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EZH2 PROTACs are unable to abolish EZH2 activator function but inhibit certain prostate cancer by rapid senescence induction

GSE298094 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2026/01/09 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 24 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
Homo sapiens (human)
Topic
Prostate cancerThis series is part of our curated briefing on it.
Samples
24 samples: 24 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.
Paper
EZH2 PROTACs outperform catalytic inhibitors in prostate cancer by targeting a methylation-independent function of PRC2Oncogene · 2026
Summary (written by the submitters)
Enhancer of Zeste Homolog 2 (EZH2) is the enzymatic subunit of the Polycomb Repressive Complex 2 (PRC2) that catalyzes H3K27 methylation for epigenetic silencing. EZH2 up-regulation or mutations contribute to the progression of various cancers, including prostate cancer (PCa). Enzymatic EZH2 inhibitors (EZH2i) exert limited efficacy in PCa, partially due to PRC2-independent roles of EZH2 in activating androgen receptor (AR) expression.
Read the full summary ↓
Several proteolysis-targeting chimera (PROTAC) degraders of EZH2 have been developed, which, surprisingly, continued to have limited efficacy in PCa cells. Here, we designed and developed a series of EZH2 PROTACs using EZH2i EPZ-6438 as a ligand and identified PROTAC-6272 as a potent lead compound. We demonstrated that 6272 effectively degraded EZH2 and other PRC2 subunits across diverse PCa cell lines. Interestingly, EZH2 PROTACs were consistently unable to decrease AR and p300 due to their inability to engage and target EZH2 outside of the PRC2 complex. Regardless, EZH2 PROTAC-6272 exhibited greater anti-proliferative activities than EPZ-6438 in some PCa cell lines, wherein it rapidly induced cellular senescence and p21 expression independently of H3K27 methylation. In summary, we report that EZH2i-based PROTACs could benefit certain PCa but are unable to target the EZH2 activator function, shedding significant light on further drug development.
Published in
EZH2 PROTACs outperform catalytic inhibitors in prostate cancer by targeting a methylation-independent function of PRC2
Xie W, Chu Q, Brea L et al. · Oncogene 2026 · PMID 41501210 · doi:10.1038/s41388-025-03662-z
This dataset
Open GSE298094 in finder → Find more human 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 GSE298094_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 PRJNA1268338 and SRA study SRP588478. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41501210) ↗
Study design
24 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 24 sample titles: 24 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 Prostate cancer datasets

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