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A continuous ferroptosis-to-apoptosis landscape identifies ferroptosis biomarkers and repressors for cancer therapy

GSE235201 Homo sapiens (human) RNA-seq (gene expression) 64 samples Submitted 2024/03/07 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 64 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
Breast cancerThis series is part of our curated briefing on it.
Samples
64 samples: 64 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
Programming a Ferroptosis-to-Apoptosis Transition Landscape Revealed Ferroptosis Biomarkers and Repressors for Cancer TherapyAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
Summary (written by the submitters)
Ferroptosis, a cell death pathway driven by lipid peroxidation, is implicated in several human diseases including cancer. Although ferroptosis is entwined with apoptosis, it currently has no characteristic biomarkers or gene signature. In this project we established a continuous phenotypic gradient between ferroptosis and apoptosis and coupled it to transcriptomic and metabolomic landscapes.
Read the full summary ↓
Omics and mechanistic studies revealed that ferroptosis was associated with enhanced MITF activity, lysosomal function, glutaminolysis, TCA cycle, and activation of ATF4, while its transition into apoptosis was attributed to enhanced ER-stress, ATF4/CHOP switch and PE/PC metabolic shift. The gradual ferroptosis-to-apoptosis transcriptomic landscape was used to generate a unique, unbiased transcriptomic predictor, the Gradient Gene Set (GGS), which classified ferroptosis and apoptosis with high accuracy. Further GGS optimization and datasets analysis of various ferroptotic and apoptotic responses revealed highly specific ferroptosis biomarkers, which were robustly validated in vitro and in vivo. A subset of the GGS was correlated with poor prognosis in breast cancer patients and in PDX models and was found to consist of different ferroptosis repressors, including PDAP1, whose knockdown suppressed breast tumor growth in a mouse model. Collectively, this study highlights molecular switches between ferroptosis and apoptosis, uncovering a highly prognostic predictive ferroptosis gene signature, pointing to a few ferroptosis repressors for targeted breast cancer therapy.
Published in
Programming a Ferroptosis-to-Apoptosis Transition Landscape Revealed Ferroptosis Biomarkers and Repressors for Cancer Therapy
Vinik Y, Maimon A, Dubey V et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2024 · PMID 38441406 · doi:10.1002/advs.202307263
This dataset
Open GSE235201 in finder → Find more human RNA-seq datasets → Breast 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 GSE235201_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 PRJNA985458 and SRA study SRP444782. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38441406) ↗
Study design
64 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 64 sample titles: 64 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

+ 24 more — browse all 64 samples with per-sample file links →

More Breast cancer datasets

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.