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通过奥林克蛋白质学和机器学习发现无症状结核病的生物标志物

Qingfeng Sun^1,2, Shanshan Li¹, Weicong Ren¹

¹Department of Bacteriology and Immunology, Beijing Key Laboratory on Drug-Resistant Tuberculosis Research, Capital Medical University, Beijing Chest Hospital, Beijing 101149, China.

Journal of proteome research

|October 7, 2025

概括

确定无症状结核病 (TB) 的血生物标志物至关重要. EN-RAGE和MCP-3在早期发现结核病方面表现有前途,将其与健康个体和潜在结核病感染 (LTBI) 区分开来.

科学领域:

生物化学生物化学
免疫学免疫学免疫学
蛋白质组学是指蛋白质组学.

背景情况:

诊断无症状结核病 (TB) 是具有挑战性的,因为它的早期,往往未被发现,阶段.
早期发现结核病对于有效治疗和公共卫生控制策略至关重要.
目前的诊断方法可能无法充分识别无症状结核病患者.

研究的目的:

识别和验证用于早期诊断无症状结核病的血生物标志物.
为了区分无症状结核病与健康对照和潜在结核病感染 (LTBI).

主要方法:

奥林克蛋白质组学试验用于分析血样本中的92种与炎症相关的蛋白质.
机器学习算法 (SVM,随机森林,神经网络,XGBoost) 用于生物标志物查.
用于独立队列验证的酶相关免疫吸收试验 (ELISA).

主要成果:

在无症状结核病病例中,EN-RAGE和MCP-3的调控显著上升.
在EN-RAGE和MCP-3的组合中,在歧视无症状结核病方面,AUC达到0.90.
艾丽莎验证证实无症状结核病中EN-RAGE和MCP-3的升高 (AUC = 0.837).

结论:

EN-RAGE和MCP-3是诊断无症状结核病的有希望的血生物标志物.
这种生物标志物组合显示出早期结核病识别的巨大潜力.
临床翻译可以改善结核病患者的治疗结果和控制工作.

关键词:

奥林克的蛋白质组学没有症状的无症状.生物标志物生物标志物机器学习是机器学习.结核病是一种肺结核病.

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