整合生物代谢网络和新一代测序数据识别卵巢癌相关基因及风险通路的研究

项目名称： 整合生物代谢网络和新一代测序数据识别卵巢癌相关基因及风险通路的研究

项目编号： No.31301040

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 生物科学

项目作者： 张良才

作者单位： 哈尔滨医科大学

项目金额： 23万元

中文摘要： 识别卵巢癌(OvCa)基因和风险通路，可以揭示疾病基因致病机制和疾病发生过程中的代谢紊乱机制，帮助发现药物靶点和有效确立诊断治疗方案。我们前期分析了20多种癌症样本的拷贝数数据，发现并证实了卵巢癌属于拷贝数变异驱动的高危癌症。我们还注意到不同的基因组数据预测基因集合重复性差，这与不同基因组数据的样本不一致及整体样本数量太小有关。对高通量的癌症样本数据进行分析并获取多平台相互印证的数据信息，对于癌症致病基因和风险通路的准确确定至关重要。另外，当前识别OvCa风险通路的研究一般只能发现单一风险通路，不能获得多个生物风险通路之间的协同调控机制。我们将利用"癌症基因组蓝图"计划提供的新一代测序数据，从拷贝数数据出发，采用生物信息和计算系统生物学分析技术，整合DNA甲基化和表达数据，识别OvCa致病基因，并鉴别与之密切相关的风险生物代谢通路。本项目实施将为卵巢癌综合代谢紊乱机制的探索提供一种新途径。

中文关键词： 整合分析；致病基因；人类代谢网络；风险通路；新一代测序技术

英文摘要： Identifying ovarian cancer (OvCa) genes and risk pathways could help uncover disease pathogenesis of susceptible genes and metabolic dysfunctions during disease process, which could further aid in drug target identification and effective diagnosis/treatment decision support. Previously, we observed on the copy number variation data in case and control samples of about 20 cancer types, and found ovarian cancer is a high risk cancer that driven by copy number variations. And we also noticed that due to less overlaps of tested samples and small sample sizes, there is a large discrepancy of predicted cancer genes from different genomic data. It is extremely essential of getting cross-validated information between multiple high-throughput genomic data on multiple platforms when we work on accurate identification of cancer susceptible genes and risk pathways. In addition, traditional risk pathway identification methods often elaborate in search of single risk pathway related to OvCa and cannot illustrate the "cross-talk" regulatory mechanisms between multiple biological risk pathways, thus leading to the failure in better understanding of the global metabolic dysfunctions of biological processes in cancer patients. In this project, we will focus on ovarian cancer and analyze the next generation sequencing omics data p

英文关键词： integrative analysis；Pathogenic gene；Human metabolic network；risk pathway；next generation sequencing