GEO series

Sex-related differences in gene expression in early-stage bladder cancer revealed by whole-transcriptome sequencing

GSE293398 Homo sapiens (human) RNA-seq (gene expression) 186 samples Submitted 2025/09/03 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 186 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: 186 samples: 186 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: Sex-related differences in gene expression in early-stage bladder cancer revealed by whole-transcriptome sequencingBMC cancer · 2026

Summary (written by the submitters)

Bladder cancer (BC) is significantly more prevalent in men than in women, yet female patients often experience higher recurrence rates and poorer prognosis. Our study aimed to investigated sex-specific gene expression differences in early-stage BC using whole-transcriptome sequencing. A total of 51 patients diagnosed with low-grade Ta stage non-muscle-invasive bladder cancer were recruited.

Read the full summary ↓

Paired tissue samples from tumor lesions and adjacent healthy bladder mucosa were analyzed to identify differentially expressed genes (DEGs). Among the top 100 most significant DEGs, overwhelmingly more upregulated were in male than female tissues (90% vs. 19%). The most significantly altered expression in female BC tissues included MT-ND6, ARL4C, ASGR1, MYBL1, and SCAMP5, whereas in males, ONECUT2, SPEG, CTSE, GJB2, and SYNM. Notably, 753 DEGs were unique to female patients, while 3989 were specific to males, with 1633 shared between both sexes. Functional annotation revealed that female-unique DEGs were significantly enriched in immune-related pathways, including regulation of leukocyte activation and cell-cell adhesion, and lymphocyte differentiation. Whereas male-unique DEGs were predominantly associated with pathways related to cell cycle regulation, mitochondrial function, and androgen receptor signaling. Immune-related gene expression indicated that female-specific DEGs were involved in leukocyte activation and antigen receptor signaling, whereas male-specific DEGs were linked to B-cell activation and neutrophil-mediated immune responses. A two-factor interaction model identified S100A14 as the only protein-coding gene whose expression exhibited a significant sex-dependent pattern, with four additional genes (GJB2, DSC2, TM4SF and ALOX15B) showing a probable interaction effect. These findings highlight the necessity for sex-specific therapeutic strategies in BC management.

Published in

Zeber-Lubecka N, Bilski K, Dąbrowska M et al. · BMC cancer 2026 · PMID 41663990 · doi:10.1186/s12885-026-15666-3

This dataset

Open GSE293398 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 GSE293398_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 PRJNA1244362 and SRA study SRP575464. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41663990) ↗

Study design

186 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 186 sample titles: 186 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

+ 146 more — browse all 186 samples with per-sample file links →

More Bladder cancer datasets

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GSE193755 Human bladder cancer cell line high throughput sequencing - genes regulated by hMSH2 12 samples same disease briefing · shares bladder
GSE211924 UCHL5 promotes Tumorigenesis of Bladder Cancer by Activating AKT/mTOR signaling pathway and targeting c-myc, SLC25A19, and ICAM5. 9 samples same disease briefing · shares bladder
GSE172359 Combination of urine exosomal mRNAs and lncRNAs as novel diagnostic biomarkers for bladder cancer 6 samples same disease briefing · shares bladder
GSE182310 Transcriptome sequence of Human bladder cancer cell line 6 samples same disease briefing · shares bladder
GSE193753 Human bladder cancer cell line high throughput sequencing - genes regulated by hMSH2 - CDDP therapy 6 samples same disease briefing · shares bladder
GSE193754 Human bladder cancer cell line high throughput sequencing - genes regulated by hMSH2 - untreated 6 samples same disease briefing · shares bladder

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.