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Updated: Jan 9, 2026

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多组学和机器学习揭示了喘炎症内型的独特免疫代谢特征和诊断生物标志物

Shuang Liu^1,2, Zhiwei Lin^1,3, Jiayong Zhou⁴

¹Department of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China.

|December 1, 2025

概括

这项研究揭示了喘亚型中明显的蛋白质代谢物相互作用,确定了用于精确诊断和治疗策略的关键生物标志物,以确定疹性和中性喘的精确诊断和治疗策略.

科学领域:

免疫学免疫学免疫学
代谢学代谢学代谢学
系统生物学系统生物学

背景情况:

喘是一种异质的炎症性疾病,分子机制不明.
生性喘 (EA) 和中性喘 (NEA) 有不同的病理途径和治疗反应.
蛋白质代谢物相互作用至关重要,但在喘发病过程中还没有得到充分研究.

研究的目的:

整合多组学数据,以全面了解喘病因.
为了识别EA和NEA亚型的独特分子特征.
为喘及其亚型开发准确的诊断模型.

主要方法:

从喘患者和对照组的吐中进行多组学分析 (蛋白学,代谢学).
包括WGCNA和CIBERSORT在内的生物信息分析用于免疫细胞透.
机器学习模型 (LASSO,SVM-RFE,随机森林) 用于生物标志物发现和模型构建.

主要成果:

综合的蛋白质代谢物相互作用网络揭示了喘中失调的途径.
EA与Th2免疫力和histidine/purine代谢有关;NEA与中性粒细胞驱动的氧化应激和缺氧有关.
一个7蛋白模型诊断出喘 (AUC=0.944); 2个生物标志物分层EA/NEA (AUC=0.903).

结论:

多种组合的整合界定了喘,EA和NEA的独特免疫代谢特征.
建立了高精度的非侵入性诊断模型.
这些发现为喘亚型和精确疗法提供了新的目标,特别是针对NEA的缺氧向干预.

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