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Tumor intrinsic METTL5 induces immune resistance in ovarian cancer by suppressing ferroptosis

GSE267542 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2025/10/30 Platform GPL24676Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 18 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)
Topic
Ovarian cancerThis series is part of our curated briefing on it.
Samples
18 samples: 18 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
Tumor Intrinsic METTL5 Modulates ATF4 Translation to Prevent T Cell-Induced Ferroptosis in Ovarian CancerAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
Summary (written by the submitters)
Despite approval of immunotherapy for the treatment of ovarian cancer patients, the majority of patients fail to respond to immunotherapy or relapse following initial response. These failures may be attributed to immunosuppressive mechanisms co-opted by tumor cells. However, it is challenging to use conventional methods to systematically evaluate the potential of tumor intrinsic factors to act as immune regulators in patients with cancer.
Read the full summary ↓
To identify immunosuppressive mechanisms in non-responders to cancer immunotherapy in ovarian cancer , we firstly performed an unbiased genome-wide CRISPR immune screens to explore tumor intrinsic factors which regulates ovarian cancer cell lines response to T cell mediated killing. Then we integrated the in vitro CRISPR screen results with multi-omics clinical data to generate a sub-library including top candidates for in vivo CRISPR screen to further evaluate the role of potential tumor intrinsic factors in regulating ovarian cancer cell response to immunotherapy with using the immune checkpoint blocker. Collectively, the data provide a rich resource of novel targets for rational immuno-oncology combinations.
Published in
Tumor Intrinsic METTL5 Modulates ATF4 Translation to Prevent T Cell-Induced Ferroptosis in Ovarian Cancer
Hou J, Ju CW, Egan NA et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2025 · PMID 41042068 · doi:10.1002/advs.202507718
This dataset
Open GSE267542 in finder → Find more RNA-seq datasets → Ovarian 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 GSE267542_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 PRJNA1111804 and SRA study SRP507870. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41042068) ↗
Study design
18 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 18 sample titles: 18 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 Ovarian cancer datasets

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