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基于机器学习的血液生物标志物和治疗剂的识别,用于个性化缺血性中风管理

Jing Liu¹, Congxia Bai², Haitao Yang¹

¹State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.

Journal of cardiovascular translational research

|July 22, 2025

概括

研究人员确定了早期缺血性中风 (IS) 诊断的关键血液生物标志物. 一个四基因模型准确地预测IS,为更快的检测和治疗提供了希望.

科学领域:

生物化学生物化学
免疫学免疫学免疫学
遗传学遗传学是一个

背景情况:

缺血性中风 (IS) 是最常见的中风类型,但缺乏可靠的血液生物标志物用于早期检测.
目前IS的诊断方法可以是侵入性的或延迟的,突出显示了需要可访问的基于血液的标记物.

研究的目的:

使用外周血液生物标志物开发早期缺血性中风诊断的预测模型.
识别与IS相关的特定信使RNA (mRNA) 签名.

主要方法:

在IS患者的外周血液样本和多个队列的健康对照中进行了RNA测序 (RNA-seq).
功能性丰富分析和单个样本基因组丰富分析 (ssGSEA) 用于识别失调的基因和细胞群.
使用已识别的关键基因构建了一个诊断预测模型,并在独立数据集中验证.

主要成果:

与对照组相比,在IS患者中发现69个mRNA显著失调.
IS与NK细胞中介的细胞毒性降低有关,特别是Cd56NK细胞,Cd56NK细胞和NKT细胞水平降低.
一个四个基因签名 (BCL2A1,FAM200B,IGJ,TXN) 在识别IS患者时显示出高的诊断准确性 (AUC:0.94-0.96).

结论:

一个新的四基因外周血液生物标记面板为早期缺血性中风检测提供了高准确度.
这些发现表明IS的发病与改变的NK细胞种群之间存在联系.
基于化合物查,细胞素D成为IS治疗的潜在治疗候选者.

关键词:

生物标志物生物标志物缺血性中风是因为缺血性中风.机器学习机器学习在 NK 细胞中,NK 细胞是 NK 细胞.有关RNA测序的RNA测序

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