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DESCRIPTION (provided by applicant): The recurrence phenomenon of superficial bladder tumors makes bladder cancer one of the most prevalent cancers world-wide and is therefore a great burden to healthcare systems. Patients with superficial tumors are under continued surveillance by routine cystoscopy examinations of the bladder for early detection of new tumor developments. This is due to the lack of clinically useful markers which can detect the presence of bladder cancer without invasive procedures. Consequently, the development of urinalysis assays using reliable diagnostic markers would be of tremendous benefit to both patients and healthcare systems. This proposal aims to define molecular signature(s) of bladder cancer based on proteomic expression profiles. The protein complement of urine samples will be profiled using a novel combination of two-dimensional, liquid-phase separation and downstream mass mapping techniques. Data comprised of both quantitative and structural information will be correlated with clinical and pathological parameters. Thus, the proposed investigation will identify molecular profiles which are indicative of the presence of bladder cancer. Public health impact: The project seeks insights that will enable improvements in the non-invasive diagnosis of bladder cancer, through analysis of naturally voided urine. This will avoid unnecessary cystoscopy and therefore reduce patient discomfort and reduce health costs. Furthermore, the establishment of an accurate urine-based assay could lead to the development of routine screening of the population.
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Prognostic analysis and progression modeling of basal-like breast cancer using multi-region sequencing
Disease Progression Modeling of Bladder Cancer
  • 批准号:
    10518025
  • 项目类别:
  • 资助金额:
    $50.75万
  • 财政年份:
    2022
  • 负责人:
    Steve Goodison
  • 依托单位:
Disease Progression Modeling of Bladder Cancer
  • 批准号:
    10674950
  • 项目类别:
  • 资助金额:
    $48.57万
  • 财政年份:
    2022
  • 负责人:
    Steve Goodison
  • 依托单位:
Advanced Computational Approaches to Delineating Dynamic Cancer Progression Processes by Using Massive Static Sample Data
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