Investigating MicroRNAs and Their Regulatory Networks in Glioblastoma

描述（由申请人提供）：多形性胶质母细胞瘤（GBM）是人类最常见和最致命的脑肿瘤。它对放疗和化疗有很强的抵抗力。了解其分子机制对于开发更有效的治疗方法至关重要。近年来的研究发现microRNAs（miRNAs）在GBM的发病机制中起着重要作用。迄今为止，在各种miRNA表达研究中，已经在GBM中发现了超过100种失调的miRNA，这为我们提供了对GBM中miRNA表达模式的惊人见解。然而，迄今为止报道的结果不一致，在破译GBM中潜在的miRNA调控机制方面提出了巨大的挑战。因此，对先前的miRNA数据的系统检查是立即需要和可执行的。在这个项目中，我们将开发创新的策略，以确定功能上重要的miRNAs显着相关的GBM的背景下，miRNA的调控网络。该项目将首先通过使用混合效应模型整合多项研究的结果来优先考虑miRNAs，然后构建由加权分子组成的GBM特异性调控网络，即GBM miRNAs，GBM基因和人类转录因子（TF），最后对调控网络进行密集模块搜索（DMS）以检测GBM调控网络中功能关键的miRNAs。我们提出三个具体目标。(1)开发一种新的统计整合框架，用于使用混合效应模型对来自多项研究的miRNA表达数据进行荟萃分析。与传统的合并分析相比，我们将每个miRNA的所有可能的效应大小整合到一个混合的模型中。 效应模型并计算P值作为其对GBM的总体效应量。(2)开发一种新的计算管道来构建由GBM miRNA、GBM基因和TF组成的GBM特异性miRNA介导的调控网络。(3)建立一种新的密集模块搜索（DMS）算法，用于识别GBM中功能重要的miRNAs。在该DMS算法中，模块被定义为一组FFL，每个FFL包括miRNA、基因和TF以及它们的调控关系。该项目是一项开创性的工作，旨在建立一个综合全面的建模框架，以及用于检测复杂疾病中功能重要的miRNA的实用计算方法，并在GBM中进行演示。该项目的成功完成将大大提高我们对GBM监管系统的理解，这将可能导致有效预防，诊断和治疗策略的发展。

DESCRIPTION (provided by applicant): Glioblastoma multiforme (GBM) is the most common and lethal brain tumor in humans. It is highly resistant to radiation and chemotherapy. Understanding its molecular mechanisms is critical in order to develop more effective treatments. Recent studies revealed that microRNAs (miRNAs) play critical roles in the pathogenesis of GBM. To date, more than 100 dysregulated miRNAs have been found in GBM from various miRNA expression studies, which provide us with astonishing insights into the patterns of miRNA expression in GBM. However, the results reported so far have been inconsistent, presenting a great challenge in deciphering the underlying miRNA regulatory mechanisms in GBM. Therefore, a systematic examination of previous miRNA data is immediately needed and executable. In this project, we will develop innovative strategies to identify functionally important miRNAs significantly associated with GBM in the context of miRNA regulatory networks. The project will start by prioritizing miRNAs through integrating results from multiple studies using a mixed effects model, then build GBM-specific regulatory networks comprised of weighted molecules, i.e. GBM miRNAs, GBM genes and human transcription factors (TFs), and finally perform dense module search (DMS) of the regulatory networks to detect functionally critical miRNAs in GBM regulatory networks. We propose three specific aims. (1) To develop a novel, statistical integrative framework for the meta-analysis of miRNA expression data from multiple studies using a mixed effects model. Compared to traditional pooled analysis, we will integrate all possible effect sizes of each miRNA into a mixed effects model and calculate a P-value as its overall effect size to GBM. (2) To develop a novel computational pipeline to construct GBM-specific miRNA- mediated regulatory networks consisting of GBM miRNAs, GBM genes, and TFs. (3) To develop a novel dense module search (DMS) algorithm for identifying functionally important miRNAs in GBM. In this DMS algorithm, a module is defined as a set of FFLs, each of which includes miRNA, gene(s), and TF, and their regulatory relationships. This project constitutes a pioneering effort to establish an integrative and comprehensive modeling framework, as well as practical computational methods for detecting functionally important miRNAs in complex diseases and demonstrates it in GBM. Successful completion of this project will greatly enhance our understanding of the regulatory systems in GBM, which will likely lead to the development of effective prevention, diagnosis, and treatment strategies.