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利用共现测度关联表达和基因组数据。

Associating expression and genomic data using co-occurrence measures.

机构信息

Department of Information Technology, Ghent University - Imec, Technologiepark-Zwijnaarde 126, 9052, Ghent, Belgium.

Department of Plant Biotechnology and Bioinformatics, Ghent University - Imec, Technologiepark-Zwijnaarde 126, 9052, Ghent, Belgium.

出版信息

Biol Direct. 2019 May 9;14(1):10. doi: 10.1186/s13062-019-0240-2.

DOI:10.1186/s13062-019-0240-2
PMID:31072345
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6507230/
Abstract

Recent technological evolutions have led to an exponential increase in data in all the omics fields. It is expected that integration of these different data sources, will drastically enhance our knowledge of the biological mechanisms behind genomic diseases such as cancer. However, the integration of different omics data still remains a challenge. In this work we propose an intuitive workflow for the integrative analysis of expression, mutation and copy number data taken from the METABRIC study on breast cancer. First, we present evidence that the expression profile of many important breast cancer genes consists of two modes or 'regimes', which contain important clinical information. Then, we show how the co-occurrence of these expression regimes can be used as an association measure between genes and validate our findings on the TCGA-BRCA study. Finally, we demonstrate how these co-occurrence measures can also be applied to link expression regimes to genomic aberrations, providing a more complete, integrative view on breast cancer. As a case study, an integrative analysis of the identified MLPH-FOXA1 association is performed, illustrating that the obtained expression associations are intimately linked to the underlying genomic changes. REVIEWERS: This article was reviewed by Dirk Walther, Francisco Garcia and Isabel Nepomuceno.

摘要

最近的技术发展导致所有组学领域的数据呈指数级增长。预计整合这些不同的数据源,将极大地提高我们对基因组疾病(如癌症)背后的生物学机制的认识。然而,不同组学数据的整合仍然是一个挑战。在这项工作中,我们提出了一种直观的工作流程,用于对来自乳腺癌 METABRIC 研究的表达、突变和拷贝数数据进行综合分析。首先,我们证明了许多重要的乳腺癌基因的表达谱由两种模式或“状态”组成,这些状态包含重要的临床信息。然后,我们展示了如何将这些表达状态的共现用作基因之间的关联度量,并在 TCGA-BRCA 研究中验证了我们的发现。最后,我们证明了这些共现度量也可以应用于将表达状态与基因组异常联系起来,为乳腺癌提供更完整、综合的视图。作为一个案例研究,对鉴定的 MLPH-FOXA1 关联进行了综合分析,说明获得的表达关联与潜在的基因组变化密切相关。审校人:Dirk Walther、Francisco Garcia 和 Isabel Nepomuceno。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/e1f83addfebe/13062_2019_240_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/7c30b06e797e/13062_2019_240_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/ebf16315880f/13062_2019_240_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/76ca706fec0e/13062_2019_240_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/9f82bf67a4c8/13062_2019_240_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/3aee14660e27/13062_2019_240_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/e1f83addfebe/13062_2019_240_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/7c30b06e797e/13062_2019_240_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/ebf16315880f/13062_2019_240_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/76ca706fec0e/13062_2019_240_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/9f82bf67a4c8/13062_2019_240_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/3aee14660e27/13062_2019_240_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e7/6507230/e1f83addfebe/13062_2019_240_Fig6_HTML.jpg

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