在个别瘤中识别癌症驱动基因
Rhys Gillman1,2, Matt A Field1,2,3,4, Ulf Schmitz1,2
1Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia.
Computational and structural biotechnology journal
|October 23, 2023
概括
精准医学使用基因相互作用网络 (GIN) 识别癌症驱动因素. 本综述探讨了在个体瘤中优先考虑驱动基因的方法,重点关注基于GIN的方法.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 癌症是一种复杂的遗传疾病,推动了对精准医学的需求.
- 大规模测序已经确定了常见的癌症突变,改善了治疗.
- 个别的瘤突变和罕见的驱动因素需要个性化的驱动基因优先级.
研究的目的:
- 审查目前在单个瘤中识别驱动基因的方法.
- 专注于基因相互作用网络 (GIN) 基于驱动器优先级的方法.
主要方法:
- 在单个瘤中基于GIN的驱动基因鉴定现有文献的审查.
- 将方法分类为使用参考GIN和使用新GIN的方法.
主要成果:
- 基于GIN的方法量化突变对基因表达的影响.
- 参考方法和 de novo GIN 方法都有局限性,包括数据源和数学近似.
- 需要进一步的研究来验证预测的体外和体内适用性.
结论:
- 目前的单个瘤驱动者识别方法,特别是基于GIN的方法,有局限性.
- 个性化方法对于识别罕见的驱动突变至关重要.
- 在临床环境中对基于GIN的预测进行验证是必不可少的.
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