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、最致命的脑肿瘤。它对放疗和化疗有很高的抵抗力。了解其分子机制对于开发更有效的治疗方法至关重要。近年来的研究表明，microRNAs （miRNAs）在GBM的发病机制中起着至关重要的作用。迄今为止，通过各种miRNA表达研究，在GBM中发现了100多种miRNA表达异常，这为我们了解GBM中miRNA的表达模式提供了惊人的见解。然而，迄今为止报道的结果并不一致，这给破译GBM中潜在的miRNA调控机制带来了巨大的挑战。因此，对先前的miRNA数据进行系统的检查是立即需要和可执行的。在本项目中，我们将开发创新策略，在miRNA调控网络的背景下识别与GBM显著相关的功能重要miRNA。该项目将首先利用混合效应模型整合多个研究结果，对mirna进行优先排序，然后构建由加权分子（即GBM mirna、GBM基因和人转录因子（tf））组成的GBM特异性调控网络，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mirna。我们提出三个具体目标。(1)利用混合效应模型建立一个新的统计整合框架，对来自多个研究的miRNA表达数据进行meta分析。与传统的混合分析相比，我们将每个miRNA的所有可能的效应大小整合到一个混合分析中

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.