← BioTransfer GEO Dataset Finder
GEO series

Histone methyltransferase PRDM9 promotes survival of drug-tolerant persister cells in glioblastoma [RNA-seq]

GSE262251 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/09/30 Platform GPL30173
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: 3 × Parent_n= vs 3 × MRK-740_n= vs 3 × CMPD1_n= vs 2 × CMPD1+MRK-740_n=Groups 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
Histone methyltransferase PRDM9 promotes survival of drug-tolerant persister cells in glioblastomaNature communications · 2025
Summary (written by the submitters)
Chemotherapy often kills a large fraction of cancer cells but leaves behind a small population of drug-tolerant persister cells. These persister cells survive drug treatments through reversible, non-genetic mechanisms and cause tumour recurrence upon cessation of therapy. Here, we report a drug tolerance mechanism regulated by the germ-cell-specific H3K4 methyltransferase PRDM9.
Read the full summary ↓
Through histone proteomic, transcriptomic, lipidomic, and ChIP-sequencing studies combined with CRISPR knockout and phenotypic drug screen, we identified that chemotherapy-induced PRDM9 upregulation promotes metabolic rewiring in glioblastoma stem cells, leading to chemotherapy tolerance. Mechanistically, PRDM9-dependent H3K4me3 at cholesterol biosynthesis genes enhances cholesterol biosynthesis, which persister cells rely on to maintain homeostasis under chemotherapy-induced oxidative stress and lipid peroxidation. PRDM9 inhibition, combined with chemotherapy, resulted in strong anti-cancer efficacy in preclinical glioblastoma models, significantly enhancing the magnitude and duration of the antitumor response by eliminating persisters. These findings demonstrate a previously unknown role of PRDM9 in promoting metabolic reprogramming that enables the survival of drug-tolerant persister cells.
Published in
Histone methyltransferase PRDM9 promotes survival of drug-tolerant persister cells in glioblastoma
Joun GL, Kempe EG, Chen B et al. · Nature communications 2025 · PMID 41397959 · doi:10.1038/s41467-025-65888-5
This dataset
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.

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 GSE262251_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1090907 and SRA study SRP497321. Searching any of these in the dataset finder brings you back here.

Study design
3 × Parent_n= vs 3 × MRK-740_n= vs 3 × CMPD1_n= vs 2 × CMPD1+MRK-740_n=

Supports a between-group comparison across 11 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 11 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
Datasets on a similar topic

Search all human 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.