Assisted Network-based Analysis of Cancer Gene Expression Studies

癌症基因表达研究的辅助网络分析

基本信息

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
项目类别：
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&apos 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

项目摘要

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.

项目概要对于大量癌症类型，基因表达 (GE) 分析研究已得到广泛开展。分析如此生成的数据可以更好地了解癌症生物学、药物的有效标记物开发和临床有用的预测模型，利用癌症 GE 数据进行基于网络的分析。系统视角并更有效地解释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结果。在功能水平上，此类发现通常更稳定且更具可重复性。尽管付出了巨大的努力，GE的数据分析结果仍然常常不能令人满意，因为“缺乏在最近的癌症研究中，低信噪比和高数据维度引起的“信息”。突出的趋势是进行多维研究，收集 GE 以及其他类型组学的数据对同一受试者的 GE 水平受 CNV、microRNA、DNA 甲基化和其他人，因此监管机构包含有关基因组的信息在基于个体基因的分析中，我们的团队和其他人。已经表明，有效地从监管机构提取信息可以协助 GE 数据的分析。在现有研究的基础上，我们将开发一种新型 ANGEA（基于辅助网络的基因这项研究将是第一个更多的研究。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。（目标 1）开发新的辅助方法来识别基因网络模块和我们将在现有研究的基础上，构建一个更加全面的网络。 GE 及其监管机构将开发新的正则化方法来构建网络。拉普拉斯算子并识别模块和中心（目标 2）开发一种用于构建 GE 模型的辅助方法。在现有研究的基础上，我们将开发一种新的癌症结果和表型。直接将监管者纳入 GE 建模并在估计中明确借用信息的方法（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。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。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。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竞争方法。为 GE 数据开发的，也将同样适用于其他类型数据的分析。数据分析，这项研究将促进多种癌症类型的研究和临床实践。