← BioTransfer GEO Dataset Finder
GEO series

The gene differential expression analysis of TFE3 rearranged renal cell carcinoma UOK120 cell line after knocking down PRCC-TFE3 fusion gene

GSE269083 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/02/25 Platform GPL29480
At a glance

RNA-seq (gene expression) · human · 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
Homo sapiens (human)
Topic
Kidney cancerThis series is part of our curated briefing on it.
Samples
12 samples: 3 × UOK109 cells, shNC vs 3 × UOK109 cells, shTFE3 vs 3 × UOK120 cells, shNC vs 3 × UOK120 cells, shTFE3Groups 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 fusion proteins drive TFE3 rearranged renal cell carcinoma progression via PGC-1α-mediated fatty acid oxidationFrontiers in immunology · 2026
Summary (written by the submitters)
TFE3 rearranged renal cell carcinoma (TFE3-rRCC), previously known as Xp11.2 translocation renal cell carcinoma (Xp11.2 tRCC), is a relatively rare type of renal cell carcinoma, accounting for about 1-4% of adult cases. To date, 23 subtypes of TFE3-rRCC have been identified, among which the NONO-TFE3 (UOK109 cell line) and PRCC-TFE3 (UOK120 cell line) fusion are two of the more common subtypes.
Read the full summary ↓
In the UOK109 and UOK120 cell line, we knocked down the TFE3 fusion gene and attempted to study the important role this fusion gene plays in tumor initiation, progression, and metastasis through various mechanisms by analyzing differential gene expression
Published in
TFE3 fusion proteins drive TFE3 rearranged renal cell carcinoma progression via PGC-1α-mediated fatty acid oxidation
Feng F, Xu Y, Deng Z et al. · Frontiers in immunology 2026 · PMID 41716419 · doi:10.3389/fimmu.2026.1700983
This dataset
Open GSE269083 in finder → Find more human RNA-seq datasets → Kidney 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 GSE269083_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 PRJNA1120074 and SRA study SRP511764. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41716419) ↗
Study design
3 × UOK109 cells, shNC vs 3 × UOK109 cells, shTFE3 vs 3 × UOK120 cells, shNC vs 3 × UOK120 cells, shTFE3

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
More Kidney cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.