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Targeting NRF2 and FSP1 to Overcome Ferroptosis Resistance in TSC2-Deficient and Cancer Cells

GSE300960 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2025/07/14 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples
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 NRF2 and FSP1 to Overcome Ferroptosis Resistance in TSC2-Deficient and Cancer CellsCancers · 2025
Summary (written by the submitters)
Background/Objectives: Ferroptosis, an iron-dependent form of cell death, shows promise as a target for therapy-resistant cancers that exhibit increased iron metabolism. Ferroptosis is primarily characterised by lipid peroxidation within cell membranes. However, many cancers evade ferroptosis by upregulation of specialised ferroptosis defence mechanisms.
Read the full summary ↓
This study investigates ferroptosis susceptibility in Tuberous Sclerosis Complex (TSC) cell models, and ovarian and breast cancer cell lines to identify ferroptosis resistance mechanisms and therapeutic targets. Methods: We assessed ferroptosis susceptibility using the ferroptosis inducers, RSL3 and erastin. We explored ferroptosis resistance genes using inhibitors of NRF2 (ML385) and FSP1 (iFSP1). RNA-sequencing was conducted to identify dysregulated ferroptosis resistance genes and to better characterise NRF2 target genes. RNA sequencing was also performed to identify gene expression after treatment with pro-ferroptotic iron-oxide nanoparticles. Results: TSC2-deficient cells exhibited resistance to RSL3 and erastin-induced ferroptosis, which correlated with increased ferroptosis defence gene expression, including NRF2 and downstream targets. NRF2 inhibition re-sensitised TSC2-deficient cells to ferroptosis, confirming its protective role. However, FSP1 inhibition did not re-sensitise TSC2-deficient angiomyolipoma kidney tumor cells to RSL3. In contrast, FSP1 knockdown significantly enhanced ferroptosis sensitivity in ovarian (PEO1, PEO4, OVCAR3) and breast (MDA-MB-436) cancer cell lines. Notably, in MDA-MB-436 cells, FSP1 knockdown was more effective than NRF2 inhibition in reversing ferroptosis resistance. Conclusions: Our findings highlight NRF2 and FSP1 as key regulators of ferroptosis resistance in TSC2-deficient and cancer cells. However, the differential efficacy of targeting these pathways suggests that patient stratification may be necessary for optimal therapeutic strategies. Targeting NRF2 and FSP1 could enhance ferroptosis susceptibility, offering a potential therapeutic approach for ferroptosis resistance cancers.
Published in
Targeting NRF2 and FSP1 to Overcome Ferroptosis Resistance in TSC2-Deficient and Cancer Cells
Tahsin T, McPhail DK, Champion JD et al. · Cancers 2025 · PMID 40867343 · doi:10.3390/cancers17162714
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 GSE300960_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 PRJNA1282741 and SRA study SRP594933. Searching any of these in the dataset finder brings you back here.

Samples in this study

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