了解癌症中 dNTP 代谢与基因组稳定性之间的相互作用。

IF 4 3区 医学 Q2 CELL BIOLOGY
Disease Models & Mechanisms Pub Date : 2024-08-01 Epub Date: 2024-08-29 DOI:10.1242/dmm.050775
Miriam Yagüe-Capilla, Sean G Rudd
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引用次数: 0

摘要

细胞内 DNA 前体池的大小和组成是维持基因组稳定性不可或缺的因素,这种关系是我们了解癌症的基础。癌变的主要方面,包括癌细胞突变率升高和诱导某些类型的 DNA 损伤,都与脱氧核苷三磷酸(dNTP)池的紊乱有关。此外,我们治疗癌症的方法在很大程度上利用了 DNA 和 dNTP 池之间的新陈代谢相互作用,其中一个由来已久的例子就是使用基于抗代谢物的癌症疗法。在本综述中,我们梳理了目前关于癌细胞中 dNTP 池扰动的原因和后果及其对基因组稳定性的影响的知识。我们概述了该领域的几个悬而未决的问题,如 dNTP 分解在基因组稳定性中的作用以及 dNTP 池扩大的后果。重要的是,我们详细介绍了如何利用我们对这些过程的机理认识,为癌症患者提供更明智的治疗方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding the interplay between dNTP metabolism and genome stability in cancer.

The size and composition of the intracellular DNA precursor pool is integral to the maintenance of genome stability, and this relationship is fundamental to our understanding of cancer. Key aspects of carcinogenesis, including elevated mutation rates and induction of certain types of DNA damage in cancer cells, can be linked to disturbances in deoxynucleoside triphosphate (dNTP) pools. Furthermore, our approaches to treat cancer heavily exploit the metabolic interplay between the DNA and the dNTP pool, with a long-standing example being the use of antimetabolite-based cancer therapies, and this strategy continues to show promise with the development of new targeted therapies. In this Review, we compile the current knowledge on both the causes and consequences of dNTP pool perturbations in cancer cells, together with their impact on genome stability. We outline several outstanding questions remaining in the field, such as the role of dNTP catabolism in genome stability and the consequences of dNTP pool expansion. Importantly, we detail how our mechanistic understanding of these processes can be utilised with the aim of providing better informed treatment options to patients with cancer.

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来源期刊
Disease Models & Mechanisms
Disease Models & Mechanisms 医学-病理学
CiteScore
6.60
自引率
7.00%
发文量
203
审稿时长
6-12 weeks
期刊介绍: Disease Models & Mechanisms (DMM) is an online Open Access journal focusing on the use of model systems to better understand, diagnose and treat human disease.
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