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: 8994367
• 负责人: Yiwen Chen
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8994367
• 关键词: Algorithms; Androgen Receptor; Base Pairing; Binding; Bioinformatics; Biometry; Biophysics; Categories; Cell Line; Clinical; Communities; Computational Biology; Computing Methodologies; DNA Sequence Alteration; Dana-Farber Cancer Institute; Data; Data Analyses; Data Set; Databases; Development; Environment; Exons; Genomic approach; Genomics; 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; RNA; RNA immunoprecipitation sequencing; RNA-Protein Interaction; Repression; Research; Research Personnel; Resources; Ribonucleoproteins; Role; Sampling; Structure-Activity Relationship; Technology; Training; Training Programs; Tumor Suppression; Untranslated RNA; 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

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.

项目总结 最近的研究表明，人类基因组编码数以千计的lncRNA，蛋白质编码很少。 容量。研究表明，lncRNAs在癌症中起着重要作用，可能是一种新的治疗方法。 癌症的靶点。然而，绝大多数lncRNAs在癌症中的功能仍不清楚。IncRNA 功能通常取决于它与蛋白质复合体的物理相互作用。他们还可以影响 通过竞争microRNA结合而成为相同microRNA靶标的其他mRNA的丰度， 即作为竞争的内源RNA(Cerna)。基因组技术的进展，特别是那些 基于下一代测序(NGS)，提供无与伦比的机会来表征功能 癌症中的lncRNA网络。然而，不同类型的基因组数据集的分析和整合 生成可测试的假说是具有挑战性的，系统的方法来表征lncRNA功能在 癌症是不存在的。本应用描述了计算方法的发展和综合 系统剖析癌症中lncRNA功能网络的基因组策略及其组合 的计算和实验方法来解开LncRNA的几个重要的功能网络 前列腺癌。具体地说，它将(1)开发一种计算方法，以重新利用公共可用的 基于阵列的数据以询问肿瘤样本中的LncRNA表达并利用整合基因组 预测在前列腺癌的肿瘤发生/肿瘤抑制中可能重要的lncRNAs的策略 肿瘤的lncRNA表达谱、临床信息和体细胞基因组改变谱分析 样本，(2)确定与EZH2或EZH2的直接转录靶标相关的lncRNAs 抑制对前列腺癌的发生或抑制很重要，以及(3)确定 AR和PTEN参与了前列腺癌的发生或抑制。除了它的科学建议之外， 本申请提出了一项全面的培训计划，以培养一名独立的调查员 计算基因组学、非编码RNA和癌症领域，谁开发了尖端技术 计算方法，并使用计算和实验相结合的方法来理解结构功能 非编码RNA与非编码RNA的功能及RNA-蛋白质相互作用的关系 癌症。虽然应聘者在生物物理学、统计学、机器等方面接受了广泛的培训 学习和计算基因组学，这个职业发展奖将允许他发展他的 实验技能，特别是那些基于下一代测序的技能 人类细胞系的技术和分子生物学实验。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Dana Farber出色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 生物学家还将在科学研究和职业发展方面为他提供建议。