通过机器学习评估潜在结核病复发风险的预测模型
Chunze Wang1, Gao Li2, Heqiu Ruan1
1The Medical Laboratory Center, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, 19 Xiuhua Road, Xiuying District, Haikou, Hainan, 570311, China.
BMC infectious diseases
|November 3, 2025
概括
预测潜伏性结核病感染 (LTBI) 复发是消除疾病的关键. 一个使用三个基因 (ALG2,FARS2,PGP) 的新模型准确地评估了用于个性化结核病管理的LTBI重新激活风险.
科学领域:
- 基因组学就是基因组学.
- 免疫学 免疫学 免疫学
- 传染性疾病 传染性疾病
背景情况:
- 潜伏性结核病感染 (LTBI) 的重新激活阻碍了结核病根除工作.
- 预测LTBI复发对于有效的疾病管理至关重要,但目前尚未充分研究.
研究的目的:
- 为了确定与LTBI复发相关的基因.
- 开发一个对LTBI重启风险的预测模型.
主要方法:
- 在GSE54992数据上使用差异基因表达分析和WGCNA.
- 使用SVM-RFE,LASSO和随机森林算法构建了一个预测模型,选择ALG2,FARS2和PGP.
- 使用AUC评估模型性能,并通过GSEA探索与免疫路径的基因关联.
主要成果:
- 在LTBI和活跃结核病感染 (ATBI) 患者之间确定了279个共同差异表达的基因.
- 三基因模型 (ALG2,FARS2,PGP) 在验证中实现了AUC值0.781和0.725.
- ALG2与免疫反应相关,FARS2和PGP与代谢相关;这三种都与BCG负载相关.
结论:
- 使用ALG2,FARS2和PGP的综合模型为评估LTBI重新激活风险提供了一个强大的工具.
- 这种模式支持针对结核病管理的个性化战略.
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