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Novel Gene expression-based Risk Stratification tool predicts recurrence in Non-muscle invasive Bladder cancer

GSE295809 Homo sapiens (human) RNA-seq (gene expression) 25 samples Submitted 2025/05/22 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 25 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
25 samples
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
Novel Gene expression-based Risk Stratification tool predicts recurrence in Non-muscle invasive Bladder cancerBMC cancer · 2025
Summary (written by the submitters)
Background Bladder cancer represents a heterogeneous disease with distinct clinical challenges. Non-muscle invasive bladder cancer (NMIBC) typically presents as indolent and slow-growing, yet a critical clinical challenge remains: identifying which patients will progress to muscle-invasive disease requiring radical interventions.
Read the full summary ↓
Early detection of progression propensity is essential, as once muscle invasion occurs, the risk of distant metastasis increases substantially, and treatment shifts from conservative TURBT (Transurethral Resection of Bladder Tumor) to aggressive surgical interventions with significant morbidity. Current risk stratification methods fail to adequately predict this transition in approximately 30% of cases, highlighting the urgent need for more accurate prognostic tools. Objective This retrospective study aimed to develop and validate a transcriptomics-based mRNA score for predicting early NMIBC recurrence, comparing its performance against traditional risk stratification methods. Methods We analyzed mRNA expression profiles from primary retrospective NMIBC tumor specimens (n=25) collected between [2018-2022]. Traditional risk stratification tools, including EORTC scoring, were applied alongside our novel mRNA-based risk score to evaluate predictive accuracy for recurrence. Results The transcriptomics-based mRNA score demonstrated a median prediction accuracy of 90% across 10,000 resampling iterations for predicting early NMIBC recurrence, significantly outperforming traditional EORTC risk scores. Our comprehensive gene set identified 435 differentially expressed genes associated with recurrence. Kaplan-Meier analysis showed significantly different recurrence-free survival between high and low mRNA risk score groups (Bonferroni corrected p-value<0.0001). Conclusions This retrospective analysis confirms that mRNA expression-based risk stratification provides superior predictive accuracy compared to conventional clinicopathologic risk tools. Implementation of this gene signature could potentially reduce over-investigation and improve surveillance cost-effectiveness after TURBT in patients with primary high-risk NMIBC. These findings may transform the clinical management paradigm by enabling more personalized follow-up protocols based on molecular risk assessment.
Published in
Novel Gene expression-based Risk Stratification tool predicts recurrence in Non-muscle invasive Bladder cancer
N S, Ps H, P R et al. · BMC cancer 2025 · PMID 40405063 · doi:10.1186/s12885-025-14273-y
This dataset
Open GSE295809 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 GSE295809_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 PRJNA1256420 and SRA study SRP581905. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40405063) ↗
Study design
A cohort of 25 individual samples

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

How we read this: Sample titles run NMIBC1 … NMIBC25 and carry no group information.

Samples in this study
More Bladder cancer datasets

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