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

剖析大非编码 RNA 功能网络的综合方法

• 批准号: 8710112
• 负责人: Yiwen Chen
• 金额: $ 11.25万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2015-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8710112
• 关键词: Algorithms; Androgen Receptor; Base Pairing; Binding; Bioinformatics; Biometry; Biophysics; Categories; Cell Line; Clinical; Code; Communities; Computational Biology; Computing Methodologies; Dana-Farber Cancer Institute; Data; Data Analyses; Data Set; Databases; Development; Environment; Exons; Functional RNA; Genomics; Health; Human Cell Line; Human Genome; Immunoprecipitation; K-Series Research Career Programs; Machine Learning; Malignant Neoplasms; Malignant neoplasm of prostate; Mediating; Medicine; Mentors; Messenger RNA; Methods; MicroRNAs; Modeling; Molecular Biology Techniques; Molecular Profiling; PTEN gene; Play; Prostatic Neoplasms; Proteins; RNA; RNA-Protein Interaction; Repression; Research; Research Personnel; Resources; Ribonucleoproteins; Role; Sampling; Structure-Activity Relationship; Technology; Training; Training Programs; Tumor Suppression; base; career development; chromatin immunoprecipitation; experience; human EZH2 protein; mRNA Expression; medical schools; new therapeutic target; next generation sequencing; novel; professor; prostate carcinogenesis; protein complex; research and development; research study; skills; statistics; therapeutic target; transcriptome sequencing; tumor; tumorigenesis

DESCRIPTION (provided by applicant): Recent studies revealed that the human genome encodes thousands of lncRNAs with little protein-coding 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, non-coding RNA and cancer, who develops cutting-edge computational methods, and uses a combination of computational and experimental approaches to understand structure- function relationship of non-coding RNA and the function of non-coding 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.

描述（由申请人提供）：最近的研究表明，人类基因组编码数千种lncRNA，但蛋白质编码能力很小。LncRNA在癌症中发挥重要作用，是一类潜在的癌症治疗靶点。然而，绝大多数lncRNA在癌症中的功能仍然未知。LncRNA的功能通常取决于其与蛋白质复合物的物理相互作用。它们还可以通过竞争microRNA结合来影响相同microRNA靶向的其他mRNA的丰度，即，作为竞争性内源RNA（ceRNA）。基因组技术的进步，特别是基于下一代测序（NGS）的技术，为表征癌症中lncRNA的功能网络提供了无与伦比的机会。然而，分析和整合不同类型的基因组数据集以生成可检验的假设是具有挑战性的，并且缺乏表征lncRNA在癌症中功能的系统方法。本申请描述了用于系统地解剖癌症中lncRNA的功能网络的计算方法和综合基因组策略的发展，以及计算和实验方法的组合来解开前列腺癌中lncRNA的几个重要功能网络。具体而言，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，并利用整合基因组策略通过分析肿瘤样品的lncRNA表达谱、临床信息和体细胞基因组改变谱来预测可能对前列腺癌中的肿瘤发生/肿瘤抑制重要的lncRNA，（2）鉴定与EZH 2相关的lncRNA或对前列腺肿瘤发生或肿瘤抑制重要的EZH 2抑制的直接转录靶标，和（3）鉴定介导前列腺肿瘤发生或肿瘤抑制的AR和PTEN的ceRNA。除了其科学建议外，该申请还提出了一个全面的培训计划，用于培养计算基因组学，非编码RNA和癌症领域的独立研究人员，他们开发尖端的计算方法，并使用计算和实验方法的组合来理解非编码RNA的结构-功能关系以及非编码RNA和RNA-蛋白质相互作用在癌症中的功能。虽然该申请的候选人已经接受了生物物理学，统计学，机器学习和计算基因组学方面的广泛培训，但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哈佛医学院社区的丹娜-法伯癌症研究所的优秀培训环境中指导候选人。一个由经验丰富的计算和癌症生物学家组成的委员会也将在科学研究和职业发展方面为他提供建议。