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Single-cell RNA-sequencing of BCG naïve and recurrent non-muscle invasive bladder cancer reveals a CD6/ALCAM-mediated immune-suppressive pathway

GSE269877 Homo sapiens (human) Single-cell RNA-seq 38 samples Submitted 2025/08/12 Platform GPL20795
At a glance

Single-cell RNA-seq · human · 38 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Topic
Bladder cancerThis series is part of our curated briefing on it.
Samples
38 samples: 38 samples, each with a distinct title — no replicated groupsGroups 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
Single-cell RNA-sequencing of BCG naïve and recurrent non-muscle invasive bladder cancer reveals a CD6/ALCAM-mediated immune-suppressive pathwayNPJ precision oncology · 2025
Summary (written by the submitters)
Bacillus Calmette-Guérin (BCG) is the mainstay of treatment for intermediate- and high-risk non-muscle invasive bladder cancer (NMIBC), yet recurrence rates remain high. To improve the efficacy of BCG, a better understanding of the immune landscape underlying BCG resistance is critical. Here, we performed single-cell RNA-sequencing (scRNA-seq) and whole-exome sequencing on tumors from NMIBC patients before and after BCG treatment.
Read the full summary ↓
Our analysis revealed a marked increase in CD6/ALCAM interactions between T cells and urothelial cells in BCG recurrent tumors. CD6-high T cells were enriched in recurrent tumors and exhibited downregulation of activation-related genes, indicative of functional impairment. These observations were supported by analysis of an independent BCG-treated NMIBC cohort, in which CD6/ALCAM signaling was correlated with shorter recurrence-free survival (p = 0.00059). Our findings reveal a previously unrecognized association between CD6/ALCAM signaling and BCG resistance in NMIBC patients and highlight this pathway as a potential therapeutic target to enhance response to BCG.
Published in
Single-cell RNA-sequencing of BCG naïve and recurrent non-muscle invasive bladder cancer reveals a CD6/ALCAM-mediated immune-suppressive pathway
Juric I, Fink EE, Qiu H et al. · NPJ precision oncology 2025 · PMID 41006678 · doi:10.1038/s41698-025-01093-3
This dataset
Open GSE269877 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 GSE269877_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 PRJNA1039857. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41006678) ↗
Study design
38 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 38 sample titles: 38 distinct titles with little repetition. 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 Bladder cancer datasets

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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.