Translational Significance of miRNAs to Optimize Diagnostics and Determination of Prognosis in Ovarian Cancer

miRNA 对优化卵巢癌诊断和确定预后的转化意义

• 批准号: 450518177
• 负责人: Dr. Laura Wollborn
• 金额: --
• 依托单位: Brigham and Womens Hospital
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/450518177?language=en
• 关键词: Translational; Significance; miRNAs; Optimize; Diagnostics

Ovarian cancer belongs to the leading causes of malignancy-related deaths in women. Its diagnosis is complicated by late-onset symptoms, often leading to detection in advanced stages and low overall survival rates. Current screening modalities are insufficient and failed to reduce ovarian cancer mortality. MicroRNAs (miRNAs) are non-coding RNAs which are involved in gene expression regulation. They can be measured in serum and were previously flagged with promising diagnostic potential in ovarian cancer. Furthermore, they are directly linked to tumor biology. For refined diagnostics, we propose to combine techniques of next-generation sequencing with novel statistical measures. Due to its complex regulation, analysis of miRNA networks by using machine learning may be useful to improve accuracy for screening and classification of patient risk. Growing understanding of miRNA regulation may furthermore be used to experimentally target tumor growth.The objectives of the proposed research plan are as follows: 1) Demographic confounders to miRNA networks are studied in a cohort of patients to improve its diagnostic specifications for screening and diagnosis of ovarian cancer. 2) To improve the diagnostic potential of miRNA networks, characteristics and subtypes of ovarian cancer will be studied in a cohort of patients to be included into a miRNA network model. 3) The potential of miRNA network kinetics is evaluated during therapy to determine its significance to predict therapeutic response and recurrence of disease. 4) As miRNAs are involved in ovarian cancer biology, the generated data from previous objectives will be used to target cancer growth by modulating systemic miRNA response in an experimental animal model.

卵巢癌是女性恶性肿瘤相关死亡的主要原因。其诊断因迟发症状而变得复杂，通常导致发现时已是晚期，总体生存率较低。目前的筛查方式不足，未能降低卵巢癌死亡率。 MicroRNA (miRNA) 是参与基因表达调控的非编码RNA。它们可以在血清中进行测量，并且之前被标记为在卵巢癌中具有良好的诊断潜力。此外，它们与肿瘤生物学直接相关。对于精细诊断，我们建议将下一代测序技术与新颖的统计方法相结合。由于其复杂的调控，使用机器学习对 miRNA 网络进行分析可能有助于提高患者风险筛查和分类的准确性。对 miRNA 调控的不断加深的了解可以进一步用于实验性靶向肿瘤生长。拟议研究计划的目标如下：1）在一组患者中研究 miRNA 网络的人口统计学混杂因素，以改进卵巢癌筛查和诊断的诊断规范。 2) 为了提高 miRNA 网络的诊断潜力，将在一组患者中研究卵巢癌的特征和亚型，并将其纳入 miRNA 网络模型。 3) 在治疗期间评估miRNA网络动力学的潜力，以确定其对预测治疗反应和疾病复发的重要性。 4) 由于 miRNA 参与卵巢癌生物学，因此先前目标生成的数据将用于通过调节实验动物模型中的全身 miRNA 反应来靶向癌症生长。