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Global gene expression response to PPARG inhibition in three urothelial cancer cell lines

GSE298581 Homo sapiens (human) RNA-seq (gene expression) 48 samples Submitted 2026/05/28 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 48 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
48 samples: 12 conditions, mostly with about 4 replicates eachGroups 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.
Summary (written by the submitters)
We report our mechanistic investigation into the conformationally-driven activation bias of PPARG in muscle-invasive urothelial cancer (MIUC) and our efforts to pharmacologically reverse this activation bias through covalent PPARG inverse agonism. Studies into tumor-associated mutations in both PPARG and RXRA were integrated with structure-based drug design as well as insights from biochemical mechanistic studies to discover FX-909, a first-in-class clinical PPARG inverse agonist that robustly enforces a conformationally repressive state of PPARG, even in highly activated biological contexts.
Read the full summary ↓
FX-909 is a potent, highly selective, and powerful suppressor of PPARG transcriptional activity through enhancement of PPARG-NCOR (Nuclear Co-Repressor) binding affinity. Treatment with FX-909 resulted in selective growth inhibition in PPARG-activated MIUC cell lines. Further, FX-909 achieved durable regressions in xenograft models of MIUC. FX-909 is the first chemical tool that is capable of recapitulating PPARG genetic knockout in vivo and is currently in clinical development for the treatment of intractable MIUC.
This dataset
Open GSE298581 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 GSE298581_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 PRJNA1269971 and SRA study SRP588978. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly with about 4 replicates each
5637 cells, DMSO, 24 hrs, ×4 5637 cells, 0.5 uM T0070907, 24 hrs, ×4 5637 cells, 0.5 uM FTX-6746, 24 hrs, ×4 5637 cells, 0.5 uM FX-909, 24 hrs, ×4 UMUC9 cells, DMSO, 24 hrs, ×4 UMUC9 cells, 0.5 uM T0070907, 24 hrs, ×4 UMUC9 cells, 0.5 uM FTX-6746, 24 hrs, ×4 UMUC9 cells, 0.5 uM FX-909, 24 hrs, ×4 +4 more

Supports a between-group comparison across 48 samples.

How we read this: 12 replicated groups read from 48 sample titles; they account for 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

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.