中国医科大学学报2025，Vol.54Issue(4)：333-339,7.DOI:10.12007/j.issn.0258-4646.2025.04.009
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
摘要
Abstract
Objective To identify novel biomarkers for predicting the risk of latent tuberculosis infection(LTBI)activation using bio-informatics 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 per-formed 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 GSE 112104 dataset,296 genes were upregulated and 1 569 genes were downregulated in active tuberculosis compared to those in LTBI.Among the LTBI progressors,506 genes were upregulated and 1 132 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=0.000 04),con-taining 1 340 genes.Intersections with 728 ferroptosis-related genes resulted in eight FRG-DEGs.The machine-learning algorithms iden-tified four FRG-hubs:PLA2G6,GLS2,JUN,and AMN,whose expression decreased with LTBI activation.Reverse transcription PCR con-firmed 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Key words
tuberculosis/latent tuberculosis infection/ferroptosis/risk model/machine learning分类
医药卫生引用本文复制引用
姜吉亮,王文涛,李乐然,尹绍卿,付玉荣,伊正君..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[J].中国医科大学学报,2025,54(4):333-339,7.基金项目
山东省自然科学基金(ZR2022MH024) （ZR2022MH024）
山东第二医科大学研究生科研创新基金(2023YJSCX007) （2023YJSCX007）
