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慢性病的无偏的以为中心的分子分类,作为迈向精准医学的一步

Anna Reznichenko¹, Viji Nair², Sean Eddy²

¹Translational Science & Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.

Kidney international

|January 29, 2024

概括

慢性病 (CKD) 的新分子类别使用机器学习对组织数据进行了识别. 这些类别,包括CKD-Gold,表现出明显的进展率,为脏疾病的精准医学铺平了道路.

科学领域:

腎臟病學 (nephrology) 是一種醫學.
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:

目前慢性病 (CKD) 的分期依赖于间接措施,限制了个性化治疗.
现有的方法无法捕捉病中的多样化分子驱动因素.

研究的目的:

开发一种基于脏组织直接分子洞察力的新型CKD分类系统.
为了精准医学,将CKD患者分为不同的分子亚型.

主要方法:

分析了369名慢性病患者和46名健康脏捐赠者的转录组数据.
自组织地图 (SOM) 的应用无监督的人工神经网络用于患者分层.
在独立的CKD和糖尿病病数据集中复制已识别的分子类别.

主要成果:

确定了四个新的分子CKD类别:CKD-蓝色,CKD-金色,CKD-橄和CKD-.
每个类别代表着不同的生物通路激活,跨越现有的CKD阶段和组织病理学.
CKD-Gold表现出明显更快的疾病进展,衰竭的高危险比率.
鉴定出明显的尿蛋白组图案和25蛋白签名,以区分CKD-Gold.

结论:

组织奥米克学使得一种新的,基于生物标志物的慢性病患者分层成为可能.
这种方法为CKD的非侵入性分类和精准医学提供了一条途径.
已识别的分子类别可以通过 kidneyclass.org.org 在外部数据上进行测试.

关键词:

基因表达的基因表达方式脏活组织检查机器学习是机器学习.患者分层是患者的分层.精准医学是一门精准医学.组织转录学组织转录学

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