Assisted Network-based Analysis of Cancer Gene Expression Studies

项目摘要 对于大量的癌症类型，基因表达谱(GE)的研究已经被广泛地进行。 分析由此产生的数据有助于更好地理解癌症生物学，这是药物的有效标记物 发展，和临床有用的预测模型。有了癌症GE数据，基于网络的分析需要 从系统的角度更有效地解释了基因之间的相互联系，导致了重要的 超越基于个体基因和基于途径的分析的发现。随着分析在更高的 在功能层面上，这样的发现通常更稳定，更具重复性。 尽管付出了巨大的努力，但通用电气的数据分析结果往往仍然不尽如人意，因为 低信噪比和高维数据所造成的“信息”。在最近的癌症研究中，一项 突出的趋势是进行多维研究，收集关于GE和其他类型组学的数据 对相同的受试者进行测量。GE水平受CNV、microRNAs、DNA甲基化和 其他，因此监管机构包含关于通用电气的信息。在基于个体基因的分析中，我们团队和其他人 已经表明，有效地从监管机构提取信息可以帮助分析通用电气的数据。 在已有研究的基础上，我们将开发一种新的基于网络的辅助基因 表情分析)框架和一套创新的方法。这项研究将是第一批到更多 通过向监管机构借用信息，有效地进行基于网络的GE数据分析。它由以下内容组成 三个紧密结合的目标。(目标1)开发新的辅助方法来识别基因网络模块和 集线器。在现有研究的基础上，我们将构建一个更全面的网络，该网络由 通用电气和它们的监管者。将开发新的正则化方法来构建网络 拉普拉斯和识别模块和集线器。(目标2)开发一种辅助构建GE模型的方法 癌症结果和表型。在现有研究的基础上，我们将开发一种新的 在GE建模中直接引入调节器并在估计中显式借用信息的方法 和标记选择。(目标3)分析多种癌症类型的数据。数据将从我们自己的研究中收集 和公共资源。凭借我们独特的专业知识，我们将首先分析皮肤癌、肺癌和淋巴癌的数据。 节点。关于其他癌症类型的数据也将被分析。分析结果将进行广泛的统计 和生物信息学评估。我们将与其他选择进行广泛的比较。 我们将提供一个新颖的分析框架和一套有竞争力的方法。这样的方法，尽管 为GE数据开发的，也将适用于其他类型数据的分析。同样重视的是 数据分析，本研究将促进多种癌症类型的研究和临床实践。

Project Summary For a large number of cancer types, gene expression (GE) profiling studies have been extensively conducted. Analyzing data so generated has led to a better understanding of cancer biology, effective markers for drug development, and clinically useful prediction models. With cancer GE data, network-based analysis, which takes a system perspective and more effectively accounts for the interconnections among genes, has led to important findings beyond individual-gene-based and pathway-based analyses. With analysis conducted at a higher functional level, such findings are usually more stable and more reproducible. Despite tremendous effort, GE data analysis results are still often unsatisfactory, because of “a lack of information” caused by the low signal-to-noise ratio and high data dimensionality. In recent cancer research, a prominent trend is to conduct multidimensional studies, which collect data on GEs as well as other types of omics measurements on the same subjects. GE levels are regulated by CNVs, microRNAs, DNA methylation, and others, and thus regulators contain information on GEs. In individual-gene-based analysis, our group and others have shown that effectively extracting information from regulators can assist the analysis of GE data. Advancing from the existing studies, we will develop a novel ANGEA (Assisted Network-based Gene Expression Analysis) framework and a set of innovative methods. This study will be among the first to more effectively conduct network-based GE data analysis by “borrowing information” from regulators. It consists of three tightly integrated aims. (Aim 1) Develop novel assisted methods for identifying gene network modules and hubs. Advancing from the existing studies, we will construct a more comprehensive network which is composed of both GEs and their regulators. Novel regularization methods will be developed for constructing the network Laplacian and identifying modules and hubs. (Aim 2) Develop an assisted method for building GE models for cancer outcomes and phenotypes. Significantly advancing from the existing studies, we will develop a novel method which directly incorporates regulators in GE modeling and explicitly borrows information in estimation and marker selection. (Aim 3) Analyze data on multiple cancer types. Data will be collected from our own studies and public resources. With our unique expertise, we will first analyze data on the cancers of skin, lung, and lymph node. Data on other cancer types will also be analyzed. The analysis results will undergo extensive statistical and bioinformatics evaluations. We will conduct extensive comparisons with the alternatives. We will deliver a novel analysis framework and a set of competitive methods. Such methods, although developed for GE data, will also be applicable to the analysis of other types of data. With an equal emphasis on data analysis, this study will foster the research and clinical practice of multiple cancer types.