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Molecular Heterogeneity and Immune Infiltration Drive Clinical Outcomes in Upper Tract Urothelial Carcinoma

GSE318164 Homo sapiens (human) RNA-seq (gene expression) 113 samples Submitted 2026/02/01 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 113 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
113 samples: 100 × MSK100 UTUC Sample vs 10 × Supp13 UTUC Sample vs 3 × Supp13 Lung-Met SampleGroups 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
Molecular Heterogeneity and Immune Infiltration Drive Clinical Outcomes in Upper Tract Urothelial CarcinomaEuropean urology · 2025
Summary (written by the submitters)
Molecular classification of upper tract urothelial carcinoma (UTUC) can provide insight into divergent clinical outcomes and provide a biological rationale for clinical decision-making. As such, we performed multi-omic analysis of UTUC tumors to identify molecular features associated with disease recurrence and response to immune checkpoint blockade (ICB).
Read the full summary ↓
Five molecular clusters with distinct clinical outcomes were identified. Favorable subtypes (C1 and C2) were characterized by a luminal-like signature and an immunologically depleted tumor microenvironment (TME). Subtype C3 was characterized by FGFR3 alterations and a higher tumor mutational burden, and included all tumors with microsatellite instability. Despite higher rates of recurrence and inferior survival, subtypes C4 and C5 harbored an immunologically rich TME favoring response to ICB. Limitations include extrapolation of molecular features of tumors from the primary site to determine response to systemic immunotherapy and the limited resolution of bulk sequencing to distinguish gene expression in the tumor, stroma, and immune compartments. RNA sequencing identified previously underappreciated UTUC molecular heterogeneity and suggests that UTUC patients at the highest risk of metastatic recurrence following surgery include those most likely to benefit from perioperative ICB.
Published in
Molecular Heterogeneity and Immune Infiltration Drive Clinical Outcomes in Upper Tract Urothelial Carcinoma
Kim K, Alam SM, Kuo F et al. · European urology 2025 · PMID 39550333 · doi:10.1016/j.eururo.2024.10.024
This dataset
Open GSE318164 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 GSE318164_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 PRJNA1416829. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39550333) ↗
Study design
100 × MSK100 UTUC Sample vs 10 × Supp13 UTUC Sample vs 3 × Supp13 Lung-Met Sample

Supports a between-group comparison across 113 samples.

How we read this: 3 replicated groups read from 113 sample titles; they account for 113 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

+ 73 more — browse all 113 samples with per-sample file links →

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.