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TFE3 fusion proteins drive oxidative metabolism, ferroptosis resistance and general RNA synthesis in translocation renal cell carcinoma. [RNA-seq]

GSE268092 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2025/02/01 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 36 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
36 samples: 12 conditions, mostly in triplicateGroups 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
TFE3 fusions drive oxidative metabolism and ferroptosis resistance in translocation renal cell carcinomaEMBO molecular medicine · 2025
Summary (written by the submitters)
The oncogenic mechanisms by which TFE3 fusion proteins drive translocation renal cell carcinoma (tRCC) are poorly characterised. Here, we integrated loss and gain of function experiments with multi-omics analyses in tRCC cell lines and patient tumors. High nuclear accumulation of NONO-TFE3 or PRCC-TFE3 fusion proteins promotes their broad binding across the genome, engaging a core set of M/E-box-containing regulatory elements to activate specific gene expression programs as well as promiscuous binding to active promoters to stimulate mRNA synthesis.
Read the full summary ↓
Within the core program, TFE3 fusions directly regulate genes involved in ferroptosis resistance and oxidative phosphorylation metabolism (OxPhos) increasing functional OxPhos levels. Consequently, human tRCC tumors display high OxPhos scores that persist during their epithelial to mesenchymal transition (EMT). EMT of tRCC tumours was further associated with presence of mesenchymal tRCC cancer cells and myofibroblast cancer-associated fibroblasts (myCAFs) that are both hallmarks of poor prognostic outcomes. We provide unique insights into how broad genomic binding of TFE3 fusion proteins promotes tRCC tumourigenesis by regulating OxPhos and ferroptosis resistance and more generally stimulating RNA synthesis.
Published in
TFE3 fusions drive oxidative metabolism and ferroptosis resistance in translocation renal cell carcinoma
Helleux A, Davidson G, Lallement A et al. · EMBO molecular medicine 2025 · PMID 40148585 · doi:10.1038/s44321-025-00221-7
This dataset
Open GSE268092 in finder → Find more human RNA-seq datasets →
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 GSE268092_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 PRJNA1114647 and SRA study SRP509168. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40148585) ↗
Study design
12 conditions, mostly in triplicate
UOK120_siCtrl ×3 UOK120_siTFE3 ×3 UOK109_siCtrl ×3 UOK109_siTFE3 ×3 TF1_siCtrl ×3 TF1_siTFE3 ×3 UOK146_siCtrl ×3 UOK146_siTFE3 ×3 +4 more

Supports a between-group comparison across 36 samples.

How we read this: 12 replicated groups read from 36 sample titles; they account for 36 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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