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通过生物信息学分析和机器学习算法识别和验证活跃结核病中NET相关的生物标志物

Shengfang Xia¹, Qi An¹, Rui Lin¹

¹Department of Science and Education Division, Public Health Clinical Center of Chengdu, Chengdu, Sichuan, China.

Frontiers in immunology

|July 3, 2025

概括

这项研究确定了与中性细胞外细胞陷 (NETs) 相关的基因 (CD274,IRF1,HPSE) 作为早期活性结核病 (ATB) 检测的潜在生物标志物,有助于区分ATB与潜在结核病感染 (LTBI). 这些发现为结核病诊断和治疗提供了新的途径.

科学领域:

免疫学免疫学免疫学
基因组学就是基因组学.
医学诊断医学诊断医学诊断

背景情况:

结核病 (TB) 的诊断延迟阻碍了全球控制工作.
中性细胞外细胞陷 (NETs) 在结核病免疫病理学中发挥作用.
需要生物标志物来区分活跃结核病 (ATB) 和潜伏结核病感染 (LTBI).

研究的目的:

探索与NETs相关的生物标志物,以区分ATB和LTBI.
确定关键的基因和参与NETs介导的结核病理学的调节网络.
预测结核病治疗的潜在药物改用候选人.

主要方法:

使用微分表达式分析分析转录组数据集的分析.
免疫细胞分析和机器学习算法 (SVM-RFE,LASSO,随机森林).
监管相互作用和药物标预测的网络分析.

主要成果:

三个枢纽基因 (CD274,IRF1,HPSE) 显示了ATB的高诊断准确性 (AUC为0.865-0.98).
IRF1和HPSE与中性粒细胞透相关,表明参与NETosis.
确定了核心调节剂 (FOXC1,GATA2,hsa-miR-106a-5p) 和46种潜在的候选药物.

结论:

CD274,IRF1和HPSE是ATB诊断的NETs衍生的有希望的生物标志物.
这些基因对中性粒细胞介导免疫具有潜在的治疗意义.
需要进一步的临床前验证来重新定位药物; 建议进行机械学研究.

关键词:

活动性结核病 (ATB)诊断诊断诊断诊断诊断诊断潜在的结核病感染 (LTBI)机器学习是机器学习.中性粒细胞外细胞陷 (NETs)

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