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Histologic variants in bladder cancer share aggressive molecular features including a CA125+ cell state and targetable TM4SF1 expression

GSE293189 Homo sapiens (human) Single-cell RNA-seq 67 samples Submitted 2025/04/24 Platform GPL34281
At a glance

Single-cell RNA-seq · human · 67 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Bladder cancerThis series is part of our curated briefing on it.
Samples
67 samples: 27 conditions, mostly in duplicateGroups 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
Bladder cancer variants share aggressive features including a CA125+ cell state and targetable TM4SF1 expressionNature communications · 2025
Summary (written by the submitters)
Histologic variant (HV) subtypes of bladder cancer are clinically aggressive tumors that are more resistant to standard therapy compared to conventional urothelial carcinoma (UC). Little is known about the transcriptional programs that account for the morphological and biological differences in HV tumors. To investigate the tumor biology of HV bladder cancers, we generated a single cell RNA sequencing (scRNA-seq) atlas of nine HV tumors and three UC tumors.
Read the full summary ↓
Our analyses revealed a tumor cell state specific to HVs that is characterized by expression of MUC16 (CA125), KRT24, and WISP2. This CA125+ cell state bears transcriptional hallmarks of epithelial-mesenchymal transition, is enriched in metastases, is predicted to be highly chemotherapy resistant, and is linked with poor survival, suggesting that this cell state plays an important role in the aggressive biology of HV tumors. Our analyses also provide evidence of transcriptional “mimicry” between HVs and histologically similar non-urothelial cell types. Lastly, we identified higher expression of TM4SF1, a cell surface protein associated with cancer metastasis, in HV tumor cells compared to UC tumor cells. Chimeric antigen receptor (CAR) T cells engineered against TM4SF1 protein demonstrated in vitro and in vivo activity against bladder cancer cell lines in a TM4SF1 expression-dependent manner, highlighting its potential as a therapeutic target in bladder cancer.
Published in
Bladder cancer variants share aggressive features including a CA125+ cell state and targetable TM4SF1 expression
Yang H, Song H, Yip E et al. · Nature communications 2025 · PMID 40527915 · doi:10.1038/s41467-025-59888-8
This dataset
Open GSE293189 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 GSE293189_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1243332 and SRA study SRP574011. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40527915) ↗
Study design
27 conditions, mostly in duplicate
Patient 21032 (CIS) Paired Normal ×3 Patient FG (CIS) Tumo ×3 Patient FG (Nested) Tumor1 Replica ×3 Patient 20847 (Lymphoepithelioma) Paired Norm… ×2 Patient 20847 (Lymphoepithelioma) Tumor1 Repl… ×2 Patient 20847 (Lymphoepithelioma) Tumor2 Repl… ×2 Patient 20847 (Lymphoepithelioma) Tumor3 Repl… ×2 Patient 21217 (Squamous + Sarcomatoid) Tumor1… ×2 +29 more

Supports a between-group comparison across 57 samples.

How we read this: 27 replicated groups read from 67 sample titles; they account for 57 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

+ 27 more — browse all 67 samples with per-sample file links →

More Bladder cancer datasets

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.