An integrated approach to dissect the functional network of large non-coding RNA

项目摘要 最近的研究表明，人类基因组编码数千种lncRNA， 容量LncRNA已被证明在癌症中发挥重要作用，并可能成为一类新的治疗药物 癌症的目标。然而，绝大多数lncRNA在癌症中的功能仍然未知。lncRNA 功能通常取决于其与蛋白质复合物的物理相互作用。他们也可以影响 通过竞争microRNA结合而被相同microRNA靶向的其他mRNA的丰度， 也就是说，作为竞争性内源RNA（ceRNA）。基因组技术的进步，尤其是 基于下一代测序（NGS），提供了无与伦比的机会来表征功能性 lncRNA在癌症中的网络。然而，分析和整合不同类型的基因组数据集， 产生可检验的假设是具有挑战性的，系统的方法来表征lncRNA的功能， 癌症缺乏。本申请描述了计算方法的发展和综合 用于系统地剖析癌症中lncRNA的功能网络的基因组策略，以及 的计算和实验方法来解开几个重要的功能网络的lncRNA在 前列腺癌具体来说，它将（1）开发一种计算方法，用于重新利用可利用的 基于阵列的数据来询问肿瘤样品中的lncRNA表达，并利用整合的基因组 预测lncRNA的策略，可能是重要的肿瘤发生/肿瘤抑制前列腺癌，通过 分析肿瘤的lncRNA表达谱、临床信息和体细胞基因组改变谱 样品，（2）鉴定与EZH 2相关的lncRNA或EZH 2的直接转录靶标 抑制，这是重要的前列腺肿瘤发生或肿瘤抑制，和（3）确定的ceRNA AR和PTEN介导前列腺肿瘤发生或肿瘤抑制。除了科学建议外， 本申请提出了一个全面的培训计划，为准备一个独立的调查员， 计算基因组学、非编码RNA和癌症领域的专家， 计算方法，并使用计算和实验方法相结合，以了解结构-功能 非编码RNA与非编码RNA功能及RNA-蛋白质相互作用的关系 癌虽然该申请的候选人在生物物理学，统计学，机器方面接受了广泛的培训， 学习和计算基因组学，这个职业发展奖将使他能够发展他的 实验技能，特别是那些基于下一代测序的 技术和人类细胞系的分子生物学实验。生物统计学和计算生物学教授刘博士和医学教授布朗博士将在达纳法伯优秀的培训环境中指导候选人 癌症研究所，哈佛医学院社区的一部分。一个由经验丰富的计算机和癌症专家组成的委员会 生物学家亦会为他提供科研及职业发展方面的意见。

PROJECT SUMMARY Recent studies revealed that the human genome encodes thousands of lncRNAs with little proteincoding capacity. LncRNAs were shown to play important roles in cancer and are potentially a new class of therapeutic targets for cancer. However, the function of the vast majority of lncRNAs in cancer remains unknown. LncRNA function often depends on its physical interactions with protein complexes. They can also influence the abundance of other mRNAs that are targeted by the same microRNAs by competing for microRNA binding, i.e., serving as competing endogenous RNA (ceRNA). Advances in genomic technologies, especially those based on next generation sequencing (NGS), provide unparalleled opportunities to characterize the functional networks of lncRNA in cancer. However, analysis and integration of different types of genomic datasets to generate testable hypotheses is challenging, and systematic approaches to characterize lncRNA function in cancer are lacking. This application describes the development of computational methods and integrative genomic strategies for systematically dissecting the functional network of lncRNA in cancer, and a combination of computational and experimental approaches to unravel several important functional networks of lncRNA in prostate cancer. Specifically, it will (1) develop a computational method for repurposing the publically available array-based data to interrogate lncRNA expression in tumor samples and utilize an integrative genomic strategy to predict lncRNAs that may be important for tumorigenesis/tumor suppression in prostate cancer via analysis of lncRNA expression profiles, clinical information and somatic genomic alteration profiles of tumor samples, (2) identify the lncRNAs that are associated with EZH2 or direct transcriptional targets of EZH2 repression that are important for prostate tumorigenesis or tumor suppression, and (3) identify the ceRNAs of AR and PTEN that mediate prostate tumorigenesis or tumor suppression. In addition to its scientific proposal, this application proposes a comprehensive training program for preparing an independent investigator in the fields of computational genomics, noncoding RNA and cancer, who develops cutting-edge computational methods, and uses a combination of computational and experimental approaches to understand structure-function relationship of noncoding RNA and the function of noncoding RNA and RNA-protein interaction in cancer. While the candidate of this application has received extensive training in biophysics, statistics, machine learning and computational genomics, this career development award will allow him to develop his experimental skills, especially those next-generation sequencing-based techniques and molecular biology experiments in human cell lines. Dr. Liu, Professor of Biostatistics and Computational Biology and Dr. Brown, Professor of Medicine will mentor the candidate in the excellent training environment of Dana Farber Cancer Institute, a part of Harvard Medical School community. A committee of experienced computational and cancer biologists will also advise him on both scientific research and career development.