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

卵巢癌是妇女恶性肿瘤相关死亡的主要原因。它的诊断因晚发性症状而变得复杂，常常导致在晚期才被发现，总体存活率很低。目前的筛查方式是不充分的，未能降低卵巢癌死亡率。MicroRNAs （miRNAs）是参与基因表达调控的非编码rna。它们可以在血清中测量，以前被标记为卵巢癌的有希望的诊断潜力。此外，它们与肿瘤生物学直接相关。为了精确诊断，我们建议将新一代测序技术与新的统计测量相结合。由于其复杂的调控，利用机器学习分析miRNA网络可能有助于提高患者风险筛选和分类的准确性。对miRNA调控的进一步了解可能进一步用于实验靶肿瘤生长。拟定研究计划的目的如下：1)在患者队列中研究miRNA网络的人口统计学混杂因素，以提高其对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，将在纳入miRNA网络模型的患者队列中研究卵巢癌的特征和亚型。3)在治疗过程中评估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意义。4)由于miRNA参与卵巢癌生物学，因此在实验动物模型中，通过调节全身miRNA反应，将先前目标生成的数据用于靶向癌症生长。

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.