# Integrative Analysis of Biomarkers for Cancer Stem Cells in Bladder Cancer and Their Therapeutic Potential

## TL;DR

This study identifies an 8-gene signature linked to cancer stem cells in bladder cancer, which predicts survival and could guide new therapies.

## Contribution

A novel 8-gene prognostic signature for bladder cancer stem cells is developed and validated across multiple datasets.

## Key findings

- The 8-gene signature (ALDH1A1, CBX7, CSPG4, DCN, FASN, INHBB, MYC, NCAM1) predicts overall survival in bladder cancer patients.
- A nomogram integrating age, tumor stage, and risk scores showed high predictive accuracy for patient outcomes.
- Drug sensitivity analysis revealed varying responses across patient groups, suggesting potential for personalized treatment strategies.

## Abstract

Background: Cancer stem cells (CSCs) are key drivers of tumorigenesis and metastasis. However, the precise roles of CSC-associated genes in these processes remain unclear. Methods: This study integrates cancer stem cell biomarkers and clinical data from The Cancer Genome Atlas (TCGA) specific to bladder cancer (BLCA). By combining differentially expressed genes (DEGs) from TCGA-BLCA samples with CSC-related biomarkers, we conducted comprehensive functional analyses and developed an 8-gene prognostic signature through Cox regression, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox regression. This model was validated with GEO datasets (GSE13507 and GSE32894), and the single-cell RNA seq dataset GSE222315 was subsequently analyzed to characterize the signature genes and elucidate their interactions. And a nomogram was created to stratify TCGA-BLCA patients into risk categories. The ‘oncoPredict’ algorithm based on the GDSC2 dataset assessed drug sensitivity in BLCA. Result: From the TCGA cohort, 665 CSC-related genes were identified, with 120 showing significant differential expression. The 8-gene signature (ALDH1A1, CBX7, CSPG4, DCN, FASN, INHBB, MYC, NCAM1) demonstrated strong predictive power for overall survival in both TCGA and GEO cohorts, as confirmed by Kaplan–Meier and ROC analyses. The nomogram, integrating age, tumor stage and risk scores, demonstrated high predictive accuracy. Additionally, the oncoPredict algorithm indicated varying drug sensitivities across patient groups. Based on retrospective data, we identified a novel CSC-related prognostic signature for BLCA. This finding suggests that targeting these genes could offer promising therapeutic strategies.