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Transcriptome analysis of the effect of RAD52 knockout on olaparib response in BRCA2-deficient, olaparib-resistant murine ovarian cancer cells (ID8-OR)

GSE302064 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/02/25 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 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
Mus musculus (mouse)
Samples
12 samples: 3 × Scramble, vehicle, vs 3 × Rad52 KO1, vehicle, vs 3 × Scramble, olaparib, vs 3 × Rad52 KO1, olaparib,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
Targeting RAD52 overcomes PARP inhibitor resistance in preclinical Brca2-deficient ovarian cancer modelbioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
This study investigates the role of RAD52 in PARP inhibitor-resistant BRCA2-deficient ovarian cancer, with a particular focus on identifying compensatory DNA repair pathways. To explore RAD52-regulated transcriptional programs, we used CRISPR/Cas9 to generate RAD52 knockout clones in the PARP inhibitor-resistant, BRCA2-deficient mouse ovarian cancer cell line ID8-OR.
Read the full summary ↓
Scrambled guide RNA-transduced cells served as controls. Both RAD52 knockout and control cells were treated with either olaparib or vehicle. RNA was extracted from all samples (n=3 per group; four groups total) for transcriptome analysis. This dataset provides a valuable resource for investigating RAD52-dependent gene expression and alternative DNA repair mechanisms in the setting of PARP inhibitor resistance.
Published in
Targeting RAD52 overcomes PARP inhibitor resistance in preclinical Brca2-deficient ovarian cancer model
Ota Y, Gupta V, Fashemi BE et al. · bioRxiv : the preprint server for biology 2025 · PMID 41040355 · doi:10.1101/2025.09.24.678351
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 GSE302064_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 PRJNA1288437 and SRA study SRP599299. Searching any of these in the dataset finder brings you back here.

Study design
3 × Scramble, vehicle, vs 3 × Rad52 KO1, vehicle, vs 3 × Scramble, olaparib, vs 3 × Rad52 KO1, olaparib,

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
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