Suppr超能文献

癌症易感性的遗传学:从小鼠到人类。

The genetics of cancer susceptibility: from mouse to man.

作者信息

Ewart-Toland Amanda, Balmain Allan

机构信息

University of California Comprehensive Cancer Center, University of California, San Francisco, California 94115, USA.

出版信息

Toxicol Pathol. 2004 Mar-Apr;32 Suppl 1:26-30. doi: 10.1080/01926230490424716.

Abstract

Cancer affects approximately 1 in 3 individuals. An individual's susceptibility to cancer is partly determined by environmental exposures and by the combination of inherited cancer susceptibility and resistance genes. Initial mapping of these low penetrance cancer susceptibility genes has been done in the mouse because human low penetrance genes are extremely difficult to find using traditional methods due to heterogeneity and interacting factors. Also, the choice of candidate genes for human association studies can miss the unknown or unexpected. Mouse models also have limitations; it can be difficult to identify causal polymorphisms in the mouse because linkage disequilibrium often extends across several genes. To exploit the strengths of both systems, we outline a cross-species strategy to identify human variants associated with increased cancer risk. This approach uses linkage analysis and haplotyping, allelic imbalance in tumors, and gene expression studies in the mouse, combined with association studies and tumor imbalance studies in humans to identify causal cancer susceptibility variants. Allelic variants in both mouse and human can then be used to better understand the mechanisms behind cancer risk and as targets for intervention.

摘要

约三分之一的人会患癌症。个体对癌症的易感性部分由环境暴露以及遗传的癌症易感性和抗性基因的组合决定。由于异质性和相互作用因素,使用传统方法极难找到人类低外显率癌症易感基因,因此已在小鼠中对这些基因进行了初步定位。此外,人类关联研究中候选基因的选择可能会遗漏未知或意外的基因。小鼠模型也有局限性;由于连锁不平衡通常会延伸到多个基因,因此很难在小鼠中鉴定出因果多态性。为了利用这两种系统的优势,我们概述了一种跨物种策略,以鉴定与癌症风险增加相关的人类变异。该方法使用连锁分析和单倍型分析、肿瘤中的等位基因不平衡以及小鼠中的基因表达研究,并结合人类的关联研究和肿瘤不平衡研究,以鉴定因果癌症易感变异。然后,小鼠和人类中的等位基因变异可用于更好地理解癌症风险背后的机制,并作为干预靶点。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验