使用共识机器学习方法来识别与肺结核治疗反应相关的宿主生物标志物
Ruiqi Zhang1, Fusheng Yao2, Wenqi Liu1
1National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen 518112, China.
Briefings in bioinformatics
|December 2, 2025
概括
开发新的结核病 (TB) 治疗监测生物标志物至关重要. 这项研究确定了Antimony (Sb),Copper (Cu) 和Strontium (Sr) 作为结核病管理的有希望的微量元素生物标志物.
科学领域:
- 生物化学 生物化学
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 监测结核病 (TB) 治疗反应缺乏可靠的生物标志物,可能导致治疗不足或过度.
- 标准的六个月结核病治疗方案需要更好的工具来评估治疗疗效.
研究的目的:
- 开发一种新的结核病治疗反应评估模型,使用综合生化资料.
- 确定监测结核病治疗有效性的重要生物标志物.
主要方法:
- 集成的氨基酸和微量元素配置文件与四个机器学习功能选择方法.
- 研究了与已识别的生物标志物相关的动态基因表达模式.
- 分析公共RNA测序数据集以验证生物标志物性能.
主要成果:
- 确定了34种潜在的生物标志物,其中反 (Sb),铜 (Cu) 和 (Sr) 始终突出.
- 与Sb,Cu和Sr相关的基因在区分治疗和未治疗的结核病方面表现出高准确性.
- 动态基因表达模式涉及编程细胞死亡,信号转导和免疫反应途径.
结论:
- Sb,Cu和Sr的组合显示出作为监测结核病治疗反应的新型实验室标准的潜力.
- 这些生物标志物可以作为指导临床决策和改善结核病患者治疗结果的实际工具.
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