陆军军医大学学报2026，Vol.48Issue(16)：2263-2275,13.DOI:10.16016/j.2097-0927.202605032
慢性阻塞性肺疾病铁死亡关键标志物的生物信息学筛选、诊断模型构建及在体实验验证
Bioinformatics-based identification of ferroptosis biomarkers and a diagnostic model with in vivo validation in chronic obstructive pulmonary disease
摘要
Abstract
Objective Chronic obstructive pulmonary disease(COPD)is difficult to recognize early,and its oxidative stress,iron metabolism disorder,and lipid peroxidation are closely associated with ferroptosis.Screening stable ferroptosis-related biomarkers may help elucidate the molecular mechanisms of COPD and improve auxiliary diagnosis.This study aimed to screen key ferroptosis biomarkers in COPD based on bioinformatics,machine learning,and single-cell transcriptomic analyses,construct a combined diagnostic model,and validate their expression in cigarette smoke-exposed mice.Methods Two lung tissue bulk transcriptomic datasets,GSE178513(3 normal,6 COPD)and GSE106986(5 normal,14 COPD),and a single-cell transcriptomic dataset GSE279570(7 normal,11 COPD)were obtained from the Gene Expression Omnibus(GEO).Combined with 484 ferroptosis-related genes from the FerrDb database,differential analysis,functional enrichment analysis,and weighted gene co-expression network analysis were performed to screen candidate genes.Seurat,Monocle2,and CellChat were used to analyze cell clustering,pseudotime trajectories,and cell communication among 64 620 high-quality cells.The least absolute shrinkage and selection operator(LASSO)regression was used to select feature genes,and the combined model was evaluated using receiver operating characteristic curves,calibration curves,and decision curve analysis.Male C57BL/6 mice aged 6 to 8 weeks were randomly divided into control and COPD groups(n=8 per group)using a random number table.The COPD group was exposed to cigarette smoke for 12 consecutive weeks.Lung function testing,hematoxylin-eosin staining,RT-qPCR,Western blotting,and immunohistochemistry were used to assess lung function,lung histopathology,and key gene expression.Results Five ferroptosis-related key genes were identified:SPATA2,CEMIP,FABP4,DPP4,and IL6.Their expression in COPD lung tissues was significantly higher than in normal controls(P all<0.05).Single-cell analysis revealed that IL6 and FABP4 increased along the pseudotime trajectory from monocytes to type 2 conventional dendritic cells,and enhanced communication between macrophages and Club cells was observed in the COPD group(P<0.01).The area under the curve of the 5-gene combined model was 0.921.Compared with the control group,mice in the COPD group showed decreased forced expiratory volume in 0.3 s(FEV0.3)(P=0.031 4),increased forced vital capacity(FVC)(P=0.028 7),a reduced FEV0.3/FVC ratio(P=0.004 8),and emphysema-like pathological changes.The mRNA and protein expression of all five genes was increased(P<0.05).Conclusion SPATA2,CEMIP,FABP4,DPP4,and IL6 are candidate biomarkers associated with ferroptosis,inflammation,and abnormal lipid metabolism in COPD.The 5-gene combined model showed good discriminatory performance in the training data,and the abnormally high expression was validated in vivo in mice,providing clues for mechanistic studies and the screening of auxiliary diagnostic biomarkers for COPD.关键词
慢性阻塞性肺疾病/铁死亡/单细胞转录组/诊断模型/机器学习Key words
chronic obstructive pulmonary disease/ferroptosis/single-cell transcriptomics/diagnostic model/machine learning分类
医药卫生引用本文复制引用
钟勇,朱佑君,王强,左玉洁..慢性阻塞性肺疾病铁死亡关键标志物的生物信息学筛选、诊断模型构建及在体实验验证[J].陆军军医大学学报,2026,48(16):2263-2275,13.基金项目
重庆市自然科学基金重点项目(CSTB2023NSCQ-ZDJ0018) Supported by the Key Project of Natural Science Foundation of Chongqing(CSTB2023NSCQ-ZDJ0018). （CSTB2023NSCQ-ZDJ0018）
