河北医科大学学报 ›› 2025, Vol. 46 ›› Issue (7): 808-817.doi: 10.3969/j.issn.1007-3205.2025.07.011

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结核病中关键代谢基因的挖掘与分析

  

  1. 河北医科大学公共卫生学院流行病与卫生统计学系，河北 石家庄 050017

  • 出版日期:2025-07-25 发布日期:2025-07-24
  • 作者简介:魏唯（1989-），女，河北石家庄人，河北医科大学公共卫生学院医学硕士研究生，从事流行病与卫生统计学研究。

  • 基金资助:
    中央引导地方科技发展资金项目(自由探索类基础研究)（226Z7705G）

The excavation and analysis of key metabolic genes in tuberculosis

  1. Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang 050017, China

  • Online:2025-07-25 Published:2025-07-24

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摘要： 目的 识别结核病患者中关键的代谢基因，并探讨这些基因的诊断价值。
方法 使用GEO数据库下载数据集GSE83456和GSE42834。通过差异分析、通路富集分析、机器学习算法等方法，对结核病中关键代谢基因进行了挖掘与分析。
结果 在结核病患者和对照人群中发现了1 170个差异表达的基因，其主要富集在与免疫相关的通路。与代谢相关基因集取交集后，使用机器学习算法筛选出了5个关键的代谢基因（PRDX6、MGLL、RENBP、WASF3、IDO1），通过神经网络模型展现了较高的预测准确性。
结论 通过综合的生物信息学分析，鉴定了5个关键代谢基因，为结核病的早期诊断和治疗提供了新的思路。


关键词: 结核, 代谢基因, 诊断

Abstract: Objective To identify key metabolic genes in tuberculosis (TB) patients and to explore their diagnostic value. 
Methods Datasets GSE83456 and GSE42834 were downloaded from the GEO database. Differential expression analysis, pathway enrichment analysis, and machine learning algorithms were employed to identify and analyze key metabolic genes in TB. 
Results A total of 1 170 differentially expressed genes (DEGs) were identified between TB patients and healthy controls, primarily enriched in immune-related pathways. After intersecting with metabolic gene sets, five key metabolic genes (PRDX6, MGLL, RENBP, WASF3, and IDO1) were screened using machine learning algorithms. A neural network model demonstrated high predictive accuracy for these genes. 
Conclusion Through comprehensive bioinformatics analysis, five key metabolic genes are identified, providing new insights for the early diagnosis and treatment of TB. 


Key words: tuberculosis, metabolic genes, diagnosis

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