在机器学习驱动下发现结核病中NETs相关的诊断生物标志物和分子亚型
Shoupeng Ding1, Yimei Yang2, Chunxiao Huang1
1Department of Laboratory Medicine, Gutian County Hospital, Ningde, China.
Frontiers in cellular and infection microbiology
|October 17, 2025
概括
中性细胞外细胞陷 (NETs) 在结核病 (TB) 发病过程中发挥着关键作用. 这项研究确定了六个核心NETs基因,揭示了不同的结核病患者亚型和诊断和治疗的潜在生物标志物.
科学领域:
- 免疫学 免疫学 免疫学
- 遗传学 遗传学 是一个
- 计算生物学 计算生物学
背景情况:
- 中性细胞细胞外陷 (NETs) 在结核病 (TB) 发病和进展中至关重要.
- 结核病中NETs的遗传基础需要进一步阐明.
- 识别关键的NETs基因可以导致结核病的新型诊断和治疗策略.
研究的目的:
- 为了识别与结核相关的核心NETs基因.
- 基于NETs基因表达的结核病患者的分子亚型的特征.
- 发现结核病诊断和治疗的潜在生物标志物.
主要方法:
- 对基因表达数据集的综合分析 (GSE83456,GSE54992).
- 差异基因表达分析,权重基因共同表达网络分析 (WGCNA) 和机器学习算法.
- 共识聚类用于患者分层和RT-qPCR用于验证.
主要成果:
- 通过WGCNA识别了630个差异表达基因 (DEG) 和1,252个基因的关键模块.
- 通过使用一组113个机器学习算法,确定了6个核心NETs基因.
- 结核病患者被分为两种分类,具有不同的免疫透特征,与核心NETs基因相关.
结论:
- 核心NETs基因在结核病原和进展中起着关键作用.
- 这些基因被认为是早期结核病诊断的新生物标志物.
- 基于这些发现,可以制定针对结核病的向治疗策略.
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