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整合癌症基因组和表观基因组景观的多个维度。

Integrating the multiple dimensions of genomic and epigenomic landscapes of cancer.

机构信息

Genetics Unit - Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada.

出版信息

Cancer Metastasis Rev. 2010 Mar;29(1):73-93. doi: 10.1007/s10555-010-9199-2.

DOI:10.1007/s10555-010-9199-2
PMID:20108112
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3415277/
Abstract

Advances in high-throughput, genome-wide profiling technologies have allowed for an unprecedented view of the cancer genome landscape. Specifically, high-density microarrays and sequencing-based strategies have been widely utilized to identify genetic (such as gene dosage, allelic status, and mutations in gene sequence) and epigenetic (such as DNA methylation, histone modification, and microRNA) aberrations in cancer. Although the application of these profiling technologies in unidimensional analyses has been instrumental in cancer gene discovery, genes affected by low-frequency events are often overlooked. The integrative approach of analyzing parallel dimensions has enabled the identification of (a) genes that are often disrupted by multiple mechanisms but at low frequencies by any one mechanism and (b) pathways that are often disrupted at multiple components but at low frequencies at individual components. These benefits of using an integrative approach illustrate the concept that the whole is greater than the sum of its parts. As efforts have now turned toward parallel and integrative multidimensional approaches for studying the cancer genome landscape in hopes of obtaining a more insightful understanding of the key genes and pathways driving cancer cells, this review describes key findings disseminating from such high-throughput, integrative analyses, including contributions to our understanding of causative genetic events in cancer cell biology.

摘要

高通量、全基因组分析技术的进步使人们能够以前所未有的视角观察癌症基因组景观。具体来说,高密度微阵列和基于测序的策略已被广泛用于识别癌症中的遗传(如基因剂量、等位基因状态和基因序列中的突变)和表观遗传(如 DNA 甲基化、组蛋白修饰和 microRNA)异常。尽管这些分析技术在一维分析中的应用对癌症基因的发现具有重要意义,但受低频事件影响的基因往往被忽视。分析平行维度的综合方法使人们能够识别(a)经常受到多种机制但任何一种机制的频率都较低的影响的基因,以及(b)经常受到多个组成部分但在单个组成部分的频率较低的影响的途径。这种使用综合方法的优势说明了整体大于部分之和的概念。由于目前的研究重点是平行和综合的多维方法,以研究癌症基因组景观,希望更深入地了解驱动癌细胞的关键基因和途径,因此,本文描述了从这些高通量、综合分析中得出的关键发现,包括对癌症细胞生物学中因果遗传事件的理解所做出的贡献。

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