GEO series

Simplified Molecular Classification of Urothelial Carcinoma via Multi-Omic Profiling: Implications for Precision Therapy

GSE308936 Homo sapiens (human) RNA-seq (gene expression) 45 samples Submitted 2026/03/11 Platform GPL26167

At a glance

RNA-seq (gene expression) · human · 45 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: Bladder cancerThis series is part of our curated briefing on it.
Samples: 45 samples: 15 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: Multi-omic profiling defines three distinct molecular subtypes of urothelial carcinoma with implications for precision therapyClinical and translational medicine · 2026

Summary (written by the submitters)

Urothelial carcinoma (UC) is a biologically heterogeneous disease and current molecular classification frameworks have limited integration into clinical decision-making. To foster precision oncology in UC, we developed a molecular classification framework of UC applicable to transcriptomic and proteomic data of non-muscle invasive bladder cancers (NMIBC), muscle-invasive bladder cancer (MIBC), and urothelial cancer cell lines.

Read the full summary ↓

Using a whole-transcriptome self-organized map and regularized semi-supervised clustering ofn 4439 bulk NMIBC and MIBC transcriptomes and proteomes, and 33 UC cell lines, we identified three molecular UC clusters. The clusters had distinct signatures of mRNA, proteins, biological processes, metabolism, and essential driver genes. They also differed in prognosis and machine learning-predicted treatment vulnerabilities. High-risk stroma-rich cluster #1 cancers were predicted to respond to selected cytotoxic drugs, ferroptosis inducers, and PARP inhibitors. For the aggressive, fast proliferating and immune-infiltrated cluster #2 tumors with basal/squamous differentiation, cytotoxic agents and EGFR/ERBB- and MEK/ERK-targeting therapy was proposed. Cluster #3 cancers of predominantly luminal papillary phenotype, with scarce stroma and immune infiltration were enriched with NMIBC and low-risk malignancies. For cluster #3 patients, selected epigenetic drugs or EGFR/FGFR inhibitors may pose an attractive treatment option. This molecular taxonomy holds promise as a practical framework for patient stratification and therapeutic guidance in UC. Our molecular classification scheme may facilitate personalized transcriptome- and proteome-based risk assessment and therapy guidance, and the development of new cancer medication.

Published in

Multi-omic profiling defines three distinct molecular subtypes of urothelial carcinoma with implications for precision therapy

van Creij NCH, Tymoszuk P, Handle F et al. · Clinical and translational medicine 2026 · PMID 41804750 · doi:10.1002/ctm2.70638

This dataset

Open GSE308936 in finder → Find more human RNA-seq datasets → Bladder 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 GSE308936_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 PRJNA1333445 and SRA study SRP625596. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41804750) ↗

Study design

15 conditions, mostly in triplicate

UMUC3 Control ×3 UMUC3 0.6 µM Neratinib ×3 UMUC3 4 µM Afatinib ×3 UMUC3 2 µM Trametinib ×3 UMUC3 7 µM Erdafitinib ×3 5637 Control ×3 5637 0.6 µM Neratinib ×3 5637 4 µM Afatinib ×3 +7 more

Supports a between-group comparison across 45 samples.

How we read this: 15 replicated groups read from 45 sample titles; they account for 45 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

GSM9256430 UMUC3 Control rep1 UMUC3 Control
GSM9256431 UMUC3 0.6 µM Neratinib rep1 UMUC3 0.6 µM Neratinib
GSM9256432 UMUC3 4 µM Afatinib rep1 UMUC3 4 µM Afatinib
GSM9256433 UMUC3 2 µM Trametinib rep1 UMUC3 2 µM Trametinib
GSM9256434 UMUC3 7 µM Erdafitinib rep1 UMUC3 7 µM Erdafitinib
GSM9256435 5637 Control rep1 5637 Control
GSM9256436 5637 0.6 µM Neratinib rep1 5637 0.6 µM Neratinib
GSM9256437 5637 4 µM Afatinib rep1 5637 4 µM Afatinib
GSM9256438 5637 2 µM Trametinib rep1 5637 2 µM Trametinib
GSM9256439 5637 7 µM Erdafitinib rep1 5637 7 µM Erdafitinib
GSM9256440 RT112 Control rep1 RT112 Control
GSM9256441 RT112 0.6 µM Neratinib rep1 RT112 0.6 µM Neratinib
GSM9256442 RT112 4 µM Afatinib rep1 RT112 4 µM Afatinib
GSM9256443 RT112 2 µM Trametinib rep1 RT112 2 µM Trametinib
GSM9256444 RT112 7 µM Erdafitinib rep1 RT112 7 µM Erdafitinib
GSM9256445 UMUC3 Control rep2 UMUC3 Control
GSM9256446 UMUC3 0.6 µM Neratinib rep2 UMUC3 0.6 µM Neratinib
GSM9256447 UMUC3 4 µM Afatinib rep2 UMUC3 4 µM Afatinib
GSM9256448 UMUC3 2 µM Trametinib rep2 UMUC3 2 µM Trametinib
GSM9256449 UMUC3 7 µM Erdafitinib rep2 UMUC3 7 µM Erdafitinib
GSM9256450 5637 Control rep2 5637 Control
GSM9256451 5637 0.6 µM Neratinib rep2 5637 0.6 µM Neratinib
GSM9256452 5637 4 µM Afatinib rep2 5637 4 µM Afatinib
GSM9256453 5637 2 µM Trametinib rep2 5637 2 µM Trametinib
GSM9256454 5637 7 µM Erdafitinib rep2 5637 7 µM Erdafitinib
GSM9256455 RT112 Control rep2 RT112 Control
GSM9256456 RT112 0.6 µM Neratinib rep2 RT112 0.6 µM Neratinib
GSM9256457 RT112 4 µM Afatinib rep2 RT112 4 µM Afatinib
GSM9256458 RT112 2 µM Trametinib rep2 RT112 2 µM Trametinib
GSM9256459 RT112 7 µM Erdafitinib rep2 RT112 7 µM Erdafitinib
GSM9256460 UMUC3 Control rep3 UMUC3 Control
GSM9256461 UMUC3 0.6 µM Neratinib rep3 UMUC3 0.6 µM Neratinib
GSM9256462 UMUC3 4 µM Afatinib rep3 UMUC3 4 µM Afatinib
GSM9256463 UMUC3 2 µM Trametinib rep3 UMUC3 2 µM Trametinib
GSM9256464 UMUC3 7 µM Erdafitinib rep3 UMUC3 7 µM Erdafitinib
GSM9256465 5637 Control rep3 5637 Control
GSM9256466 5637 0.6 µM Neratinib rep3 5637 0.6 µM Neratinib
GSM9256467 5637 4 µM Afatinib rep3 5637 4 µM Afatinib
GSM9256468 5637 2 µM Trametinib rep3 5637 2 µM Trametinib
GSM9256469 5637 7 µM Erdafitinib rep3 5637 7 µM Erdafitinib

+ 5 more — browse all 45 samples with per-sample file links →

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GSE176493 Comprehensive target engagement by EZH2 inhibitor CPI-0209 allows for preferential targeting of ARID1A mutant urothelial carcinoma 252 samples same disease briefing · shares urothelial
GSE181389 Transcriptome profiling of HOXA13 and HOXB13 expression knockdown in bladder cancer cell lines. 59 samples same disease briefing
GSE193755 Human bladder cancer cell line high throughput sequencing - genes regulated by hMSH2 12 samples same disease briefing
GSE214886 Downregulation of CBX7 induced by EZH2 upregulates FGFR3 expression to reduce sensitivity to cisplatin in bladder cancer 12 samples same disease briefing
GSE211924 UCHL5 promotes Tumorigenesis of Bladder Cancer by Activating AKT/mTOR signaling pathway and targeting c-myc, SLC25A19, and ICAM5. 9 samples same disease briefing
GSE172359 Combination of urine exosomal mRNAs and lncRNAs as novel diagnostic biomarkers for bladder cancer 6 samples same disease briefing

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.