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High-frequency recurrence index for predicting multiple recurrences in non-muscle-invasive bladder cancer (Discovery cohort)

GSE294703 Homo sapiens (human) RNA-seq (gene expression) 45 samples Submitted 2026/02/07 Platform GPL16791
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: 23 × NMIBC_N vs 22 × NMIBC_PGroups 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.
Summary (written by the submitters)
Non-muscle invasive bladder cancer (NMIBC) has a recurrence rate of more than 50% after transurethral resection (TUR) and Calmette-Guérin (BCG) treatment. This study aimed to develop a high-frequency recurrence index (HfRI) to predict multiple recurrences in NMIBC patients and guide personalized treatment strategies. In this study, we analyzed transcriptome data (Discovery cohort) of 45 high-risk NMIBC patients who received intravesical BCG treatment, including patients with >=2 recurrences, to identify genes significantly associated with high-frequency recurrence and construct a signature.
Read the full summary ↓
In addition, we validated the predictive performance using transcriptome data of 94 patients (Discovery cohort).
This dataset
Open GSE294703 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 GSE294703_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 PRJNA1251343 and SRA study SRP578952. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
23 × NMIBC_N vs 22 × NMIBC_P

Supports a between-group comparison across 45 samples.

How we read this: 2 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

+ 5 more — browse all 45 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.