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Yuping Zhang¹, M Henry Linder², Ali Shojaie³

¹Department of Statistics, Institute for Systems Genomics, Center for Quantitative Medicine, Institute for Collaboration on Health, Intervention, and Policy, The Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT 06269, USA.

Statistics in biosciences

|June 30, 2023

概括

整合多omics数据,包括副本数变异和基因表达,增强了癌症途径干扰的发现. 在11种癌症类型中分析BRAF途径,揭示了瘤特异性异常和共同点.

科学领域:

在瘤学瘤学.
基因组学就是基因组学.
系统生物学系统生物学

背景情况:

像癌症这样的复杂疾病是由累积途径干扰引起的,而不仅仅是单个基因突变.
单一平台分析可能错过了对疾病机制的关键见解.
多平台"omics"数据为生物系统提供了更全面的视图.

研究的目的:

用综合的多学科数据评估基于路径分析的好处.
用基因组学,表观基因组学和转录基因组学研究11种癌症类型的BRAF途径干扰.
评估不同分子调节元素和网络拓对发现瘤特异性异常的影响.

主要方法:

利用了来自癌症基因组图谱 (TCGA) 的多平台"奥米克"数据 (基因组学,表观基因组学,转录基因组学) 对11种癌症类型.
专注于BRAF瘤遗传途径,分析复制数变异 (CNV),甲基化和基因表达.
进行模拟研究以评估网络拓和多omics集成的影响.

主要成果:

整合CNVs和/或甲基化与mRNA表达改善了瘤异常的检测.
将CNV与mRNA表达结合起来比使用甲基化数据更有效.
网络拓分析增强了发现瘤异常的发现.
在各种癌症类型的BRAF通路干扰中确定了相似之处和差异.

结论:

多omics数据集成,特别是与CNV和基因表达,显著提高对癌症途径干扰的理解.
使用集成的"omics"数据进行基于途径的分析,为瘤特异性异常和跨癌症共同点提供了宝贵的见解.
网络拓在剖析癌症中复杂途径失调方面发挥着至关重要的作用.

关键词:

数据集成数据集成数据集成多平台基因组学网络拓学网络拓学路径分析路径分析

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