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Related Experiment Videos

Bladder cancer outcome and subtype classification by gene expression.

Ekaterini Blaveri1, Jeff P Simko, James E Korkola

  • 1Comprehensive Cancer Center, University of California at San Francisco, San Francisco, California 94143-0808, USA.

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research
|June 3, 2005
PubMed
Summary
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This study used gene expression analysis to classify bladder tumors, identifying distinct subgroups based on invasiveness and subtype. These findings may lead to new biomarkers for bladder cancer progression and targeted therapies.

Area of Science:

  • Oncology
  • Genomics
  • Molecular Biology

Background:

  • Genetic alterations are implicated in bladder tumor progression, influencing both tumor phenotype and clinical outcomes.
  • Accurate classification of bladder tumors into homogeneous subgroups is crucial for understanding disease progression and guiding treatment.

Purpose of the Study:

  • To utilize expression microarray analysis for elucidating bladder cancer progression.
  • To classify bladder tumors into clinically relevant subgroups based on gene expression patterns.

Main Methods:

  • Global gene expression profiling of 80 bladder tumors, 9 cell lines, and 3 normal samples using cDNA microarrays (10,368 gene elements).
  • Application of robust statistical methods for identifying differentially expressed genes, accounting for multiple testing.

Related Experiment Videos

  • Unsupervised hierarchical and supervised clustering for tumor classification based on gene expression.

    • Main Results:

    • Hierarchical clustering successfully separated superficial (pT(a), pT(1)) from muscle-invasive (pT(2)-pT(4)) bladder tumors.
    • Supervised classification achieved 90.5% accuracy in distinguishing superficial from muscle-invasive tumors using a subset of genes.
    • Tumor classification into transitional versus squamous subtypes (89% accuracy) and good versus bad prognosis (78% accuracy) was also achieved.
    • In silico validation confirmed the performance of stage classifiers on an independent dataset.
    • Immunohistochemistry validated differential expression of cathepsin E, cyclin A2, and parathyroid hormone-related protein.

    Conclusions:

    • Gene expression profiling provides a powerful tool for classifying bladder tumors into clinically relevant subgroups.
    • Identified differentially expressed genes may serve as potential biomarkers for bladder cancer development, progression, and therapeutic targeting.