Bladder and Urothelial Cancer Treatments

The paper identifies SH2D2A as a favourable prognosis marker in bladder cancer, enriched in Treg cells, and suggests its role in immunotherapy targets.

The paper investigates SRC's role in bladder cancer metastasis across molecular subtypes, finding subtype-specific effects on migration but no overall impact on metastasis.

The paper introduces a 3D bladder cancer model in a human urothelial environment to better test cancer treatments and improve translational research.

The paper develops and evaluates a classifier for urothelial carcinoma subtypes that is robust across different platforms and preprocessing methods.

This study identifies sex-specific biomarkers in bladder cancer by analyzing genomic, expression, and clinical data from TCGA and GTEx, revealing distinct pathways and hub genes associated with male and female patients.

The paper identifies how XPD mutations alter somatic mutation patterns in bladder cancer and develops a machine-learning model to predict pathogenic XPD mutations with 100% accuracy in an independent cohort.

The paper develops a machine learning model using transcriptomic data to predict chemotherapy response in muscle-invasive bladder cancer with high accuracy.

This study reviews biomarkers for bladder cancer detection, identifying gene and protein candidates that may reduce reliance on cystoscopy.

This study identifies molecular changes in bladder mucosa that initiate cancer, revealing inflammation's role and mutation patterns linked to early carcinogenesis.

This study develops a machine learning model to predict 10-year overall survival in bladder cancer patients using clinical and molecular features, without relying on tumor grade classification.

This study identifies distinct genomic and molecular features of sarcomatoid bladder cancer, linking EMT dysregulation to aggressive behavior and immune infiltration.