Assisted Network-based Analysis of Cancer Gene Expression Studies

癌症基因表达研究的辅助网络分析

• 批准号: 9306472
• 负责人: Shuangge Ma
• 金额: $ 8.38万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9306472
• 关键词: Bioinformatics; Biological; Cancer Biology; Cancer Model; Clinical; Computational algorithm; Copy Number Polymorphism; DNA; DNA Methylation; Data; Data Analyses; Databases; Development; Dimensions; Ensure; Epigenetic Process; Evaluation; Fostering; Gene Expression; Gene Expression Profiling; Genes; Individual; Laplacian; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Methodology; Methods; Methylation; MicroRNAs; Modeling; Molecular Profiling; Network-based; Noise; Non-Hodgkin' s Lymphoma; Outcome; Pathway interactions; Performance; Phenotype; Reproducibility; Research; Resources; Signal Transduction; Skin Cancer; System; Testing; anticancer research; base; cancer biomarkers; cancer gene expression; cancer type; clinical practice; cost; data collection methodology; drug development; experience; gene function; innovation; lymph nodes; melanoma; novel; programs; secondary analysis; simulation; targeted treatment; therapeutic development; therapeutic target; trend

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.

项目摘要 对于大量的癌症类型，基因表达（GE）分析研究已经广泛进行。 分析如此生成的数据有助于更好地了解癌症生物学，药物治疗的有效标志物， 开发和临床上有用的预测模型。利用癌症GE数据，进行基于网络的分析，需要 一个系统的观点，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路径的分析。分析结果显示， 在功能水平上，这样的发现通常更稳定，更可重复。 尽管付出了巨大的努力，通用电气的数据分析结果仍然往往不令人满意，因为“缺乏 低信噪比和高数据维数导致的“信息”。在最近的癌症研究中， 一个突出的趋势是进行多维研究，收集关于GE以及其他类型组学的数据 在相同的主题上进行测量。GE水平受CNV、microRNA、DNA甲基化和 因此，监管机构包含有关通用电气公司的信息。在基于个体基因的分析中， 已经表明，有效地从监管机构提取信息可以帮助分析GE数据。 在现有研究的基础上，我们将开发一种新的ANGEA（Assisted Network Based Gene 表达式分析）框架和一套创新的方法。这项研究将是第一个到更多的 通过从监管机构“借用信息”，有效地进行基于网络的GE数据分析。它包括 三个紧密结合的目标。(Aim 1）开发用于识别基因网络模块的新的辅助方法， 枢纽在现有研究的基础上，我们将构建一个更全面的网络， 和他们的监管者。将开发新的正则化方法来构建网络 拉普拉斯算子和识别模块和集线器。(Aim 2）开发一种辅助方法，用于构建GE模型， 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 一种直接将调节器纳入GE建模并在估计中显式借用信息的方法 和标记选择。(Aim 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 node.还将分析其他癌症类型的数据。分析结果将进行广泛的统计 和生物信息学评价。我们将与替代方案进行广泛的比较。 我们将提供一个新颖的分析框架和一套有竞争力的方法。这些方法，虽然 为通用电气数据开发的数据分析软件也将适用于其他类型数据的分析。同时强调 通过数据分析，本研究将促进多种癌症类型的研究和临床实践。