铁死亡相关基因作为新型标志物预测结核潜伏感染活化风险及风险模型构建  

Ferroptosis-related genes as novel biomarkers for predicting the risk of latent tuberculosis infection activation and establishment of a risk model

作　　者：姜吉亮 王文涛 李乐然 尹绍卿 付玉荣 伊正君 JIANG Jiliang;WANG Wentao;LI Leran;YIN Shaoqing;FU Yurong;YI Zhengjun(Department of Molecular Diagnostics,School of Medical Laboratory Science,Shandong Second Medical University,Weifang 261053,China;Department of Pathogenic Biology,School of Basic Medical Sciences,Shandong Second Medical University,Weifang 261053,China)

机构地区：[1]山东第二医科大学医学检验学院分子诊断学教研室,山东潍坊261053 [2]山东第二医科大学基础医学院病原生物学教研室,山东潍坊261053

出　　处：《中国医科大学学报》2025年第4期333-339,共7页Journal of China Medical University

基　　金：山东省自然科学基金(ZR2022MH024);山东第二医科大学研究生科研创新基金(2023YJSCX007)。

摘　　要：目的利用生物信息学和多种机器学习算法,筛选出预测结核潜伏感染(LTBI)活化风险的新型标志物并建立风险模型。方法从基因表达综合数据库获取GSE112104和GSE193777数据集,通过差异基因分析和加权基因共表达网络分析筛选与LTBI活化相关的铁死亡相关差异基因(FRG-DEGs)。进一步通过LASSO、SVM-RFE和RF 3种机器学习算法筛选铁死亡相关关键基因(FRG-hubs),并通过验证集和逆转录PCR验证其可靠性。最后,利用R语言建立风险模型。结果在GSE112104数据集中,与LTBI相比,活动性结核病中296个基因显著上调,1569个基因显著下调,LTBI进展者中506个基因显著上调,1132个基因显著下调。WGCNA共得到5个基因共表达模块,其中蓝色模块与LTBI活化相关性最强(cor=0.62,P=0.00004),包含1340个基因。将三者连同728个铁死亡相关基因(FRG)取交集,共得到8个表达趋势一致的FRG-DEGs。3种机器学习算法共筛选出4个FRG-hubs,包括PLA2G6、GLS2、JUN和AMN。逆转录PCR结果显示,随着休眠结核分枝杆菌的活化,FRG-hubs的表达逐渐降低。最后,基于FRG-hubs构建了LTBI活化风险模型,曲线下面积在0.98~1.00之间。结论本研究成功筛选出用于预测LTBI活化风险的新型标志物,并构建了具有良好预测效能的LTBI活化风险模型。Objective To identify novel biomarkers for predicting the risk of latent tuberculosis infection(LTBI)activation using bioinformatics and machine-learning algorithms and to establish a risk model.Methods The GSE112104 and GSE193777 datasets were obtained from the Gene Expression Omnibus.Differential gene expression and weighted gene co-expression network analyses were performed to identify ferroptosis-related differentially expressed genes(FRG-DEGs)associated with LTBI activation.Three machine-learning algorithms,least absolute shrinkage and selection operator,support vector machine-recursive feature elimination,and random forest,were used to identify ferroptosis-related hub genes(FRG-hubs).The reliability of these genes was validated using independent validation datasets and reverse transcription polymerase chain reaction(PCR).A risk model was established using R software.Results In the GSE112104 dataset,296 genes were upregulated and 1569 genes were downregulated in active tuberculosis compared to those in LTBI.Among the LTBI progressors,506 genes were upregulated and 1132 genes were downregulated.Weighted correlation network analysis identified five gene modules,with the blue module showing the strongest correlation with LTBI activation(cor=0.62,P=0.00004),containing 1340 genes.Intersections with 728 ferroptosis-related genes resulted in eight FRG-DEGs.The machine-learning algorithms identified four FRG-hubs:PLA2G6,GLS2,JUN,and AMN,whose expression decreased with LTBI activation.Reverse transcription PCR confirmed this trend.A risk model based on these genes yielded an area under the curve of 0.98 to 1.00.Conclusion This study successfully identified novel biomarkers for predicting the risk of LTBI activation and developed an accurate predictive risk model.

关 键 词：结核病 结核潜伏感染 铁死亡 风险模型 机器学习 

分 类 号：R521[医药卫生—内科学]