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中文摘要
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描述(申请人提供):浅表性膀胱癌的复发现象使膀胱癌成为世界上最常见的癌症之一,因此对医疗系统来说是一个巨大的负担。浅表肿瘤患者通过常规膀胱镜检查持续监测,以便及早发现新的肿瘤进展。这是由于缺乏临床上有用的标记物,可以在不进行侵入性手术的情况下检测膀胱癌的存在。因此,使用可靠的诊断标记物的尿液分析分析的发展将对患者和医疗保健系统都有巨大的好处。本提案旨在根据蛋白质组表达谱确定膀胱癌的分子特征(S)。尿样的蛋白质成分将使用一种新的二维、液相分离和下游质量映射技术的组合进行分析。由定量和结构信息组成的数据将与临床和病理参数相关联。因此,拟议的调查将确定表明膀胱癌存在的分子图谱。 公共卫生影响:该项目通过分析自然排尿,寻求能够改进膀胱癌非侵入性诊断的见解。这将避免不必要的膀胱镜检查,从而减少患者的不适,降低医疗成本。此外,建立一种准确的尿液化验方法可以促进人群常规筛查的发展。
英文摘要
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
海外基金