GEO series

RNA-seq in C4.2B prostate cancer cell line treated with AKTi (ipatasertib) and EZH2i (tazemetostat).

GSE297894 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/05/18 Platform GPL18573

At a glance

RNA-seq (gene expression) · human · 12 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)
Samples: 12 samples: 12 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)

Men with advanced prostate cancer are typically treated with androgen deprivation therapy, but most ultimately develop resistance and incurable disease (e.g. castration-resistant prostate cancer (CRPC)). The majority of CRPCs overexpress the epigenetic enzyme EZH2 and harbor alterations in the PI3K pathway, providing two targetable pathways outside of AR.

Read the full summary ↓

Here we show that EZH2 inhibitors synergize with PI3K, AKT, or mTORC1 inhibitors to kill CRPC in vitro and promote tumor regression in vivo. Strikingly, these agents trigger a catastrophic energy crisis by cooperatively suppressing glycolysis, the TCA cycle, and oxidative phosphorylation prior to cell death. EZH2 and PI3K pathway inhibitors achieve this by respectively inhibiting two key regulators of metabolism, MYC and HIF-1A, while concomitantly derepressing a pro-apoptotic stress sensor. Together, these studies reveal a promising therapeutic strategy for CRPC and demonstrate how metabolic plasticity can be fatally impaired by co-targeting upstream oncogenic nodes that converge on this important process.

This dataset

Open GSE297894 in finder → Find more human 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 GSE297894_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 PRJNA1266691 and SRA study SRP587203. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

12 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 12 sample titles: 12 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

GSM9001550 C4.2B DMSO REP A
GSM9001551 C4.2B DMSO REP B
GSM9001552 C4.2B DMSO REP C
GSM9001553 C4.2B IPATA REP A
GSM9001554 C4.2B IPATA REP B
GSM9001555 C4.2B IPATA REP C
GSM9001556 C4.2B TAZ REP A
GSM9001557 C4.2B TAZ REP B
GSM9001558 C4.2B TAZ REP C
GSM9001559 C4.2B COMBO REP A
GSM9001560 C4.2B COMBO REP B
GSM9001561 C4.2B COMBO REP C

Datasets on a similar topic

GSE252382 ATACseq of TNBC cell lines treated with EZH2i (tazemetostat) and/or AKTi (ipatasertib) 84 samples shares ezh2i, tazemetostat, ipatasertib, treated, line, akti
GSE279872 Targeting FOXA1 and FOXA2 Disrupts the Lineage-Specific Oncogenic Output in Prostate Cancer [RNA-seq] 30 samples shares prostate, rna-seq, line
GSE265762 Individual RNA-seq for different prostate cancer cells and Lucap145.2 PDX treated by JQ1, AZD5153,ENZ or by FOXA1 and BRD4 gene knockdown 20 samples shares prostate, rna-seq, treated
GSE296115 The Histone Methyltransferase KMT2D is a Critical Mediator of Lineage Plasticity and Therapeutic Response in Castration Resistant Prostate Cancer [LNCaP Bulk RNA-Seq] 16 samples shares prostate, rna-seq, line
GSE157605 RNA sequencing data of PC3, an androgen negative human prostate cell line, treated with estrogenic compounds 12 samples shares prostate, treated, line
GSE230601 ZNF397 Deficiency Triggers TET2-driven Lineage Plasticity and AR-Targeted Therapy Resistance in Prostate Cancer [RNA-seq] 12 samples shares prostate, rna-seq, line
GSE222196 Treatment of prostate cancer cell lines with Androgen Receptor and Receptor Tyrosine Kinase inhibitors 246 samples shares prostate, line
GSE297742 Bulk RNA-seq of primary tumor biopsy samples from prostate adenocarcinoma patients 234 samples shares prostate, rna-seq

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.