基于生物信息学和临床样本的机器学习模型和结核病中的表观遗传染色素调节者的分子
Huawei He1, Liuying Wei1, Lanwei Nong2
1Department of Tuberculosis, The Fourth People's Hospital of Nanning, Nanning, China.
Scientific reports
|November 25, 2025
概括
染色体调节器 (CRs) 是了解结核病 (TB) 感染的关键. 这项研究确定了五个基因特征和XGBoost模型,用于准确的结核病诊断和亚型.
科学领域:
- 表观遗传学和免疫学
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 染色体调节器 (CRs) 在结核病 (TB) 发病和诊断中的作用基本上是未知的.
- 通过CRs的表观遗传修饰会影响结核病感染期间的免疫反应,这有助于疾病异质性.
研究的目的:
- 研究CRs在诊断结核病和表征其亚型方面的有效性.
- 确定用于结核病检测和分类的新生物标志物.
主要方法:
- 对基因表达数据集 (GSE83456,GSE152532) 的分析,以确定差异表达的CRs (DE-CRs).
- 基于DE-CR表达的共识聚类来分层结核病患者.
- 机器学习模型 (RF,SVM,GLM,XGB) 被训练并验证了集群差异化.
- 一个五基因的签名是从最佳的XGBoost模型中得出的.
主要成果:
- 鉴定了15个DE-CR,揭示了结核病患者具有不同的免疫微环境的两个不同的分子.
- 在XGBoost模型实现高精度 (AUC=0.965) 在区分这些集群.
- 一个五个基因签名 (DHRS9,HIST1H2BK,C16orf74,SLC30A1,GBP1) 准确地预测了结核病亚型和活跃结核病 (ATB).
- 肺结核 (PTB) 和结核性脑膜炎 (TBM) 患者的IFIT3表达升高证实了其作为泛结核生物标志物的潜力.
结论:
- CRs与免疫透和结核病异质性显著相关.
- 使用五个基因签名的强大的XGBoost模型能够准确地进行结核病亚型和疾病评估.
- CRs,特别是IFIT3,代表了结核病诊断的有希望的新生物标志物.
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