首页|期刊导航|陆军军医大学学报|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

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OA

Bioinformatics-based identification of ferroptosis biomarkers and a diagnostic model with in vivo validation in chronic obstructive pulmonary disease

中文摘要英文摘要

目的 慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)早期识别困难,其氧化应激、铁代谢紊乱和脂质过氧化与铁死亡密切相关.筛选稳定的铁死亡相关标志物有助于理解COPD的分子机制并改进辅助诊断.本研究基于生物信息学、机器学习和单细胞转录组分析筛选COPD铁死亡关键标志物,构建联合诊断模型,并在香烟烟雾暴露小鼠中验证其表达.方法 从基因表达综合数据库(Gene Expression Omnibus,GEO)获取2个肺组织bulk转录组数据集GSE178513(正常3例、COPD 6例)和GSE106986(正常5例、COPD 14例),以及单细胞转录组数据集GSE279570(正常7例、COPD 11例),结合FerrDb收录的484个铁死亡相关基因,采用差异分析、功能富集分析和加权基因共表达网络分析筛选候选基因.采用Seurat、Monocle2和CellChat分析64 620个高质量细胞的细胞分群、拟时序和细胞通讯;采用LASSO回归筛选特征基因,通过受试者工作特征曲线、校准曲线和决策曲线评价联合模型.将6~8周龄雄性C57BL/6小鼠按随机数字表法分为对照组和COPD组(每组8只),COPD组连续12周接受香烟烟雾暴露;采用肺功能检测、苏木精-伊红染色、RT-qPCR、Western blotting和免疫组织化学等方法检测肺功能、肺组织病理及关键基因表达.结果 筛得SPATA2、CEMIP、FABP4、DPP4和IL6 共5个铁死亡相关关键基因;其在COPD肺组织中的表达均高于正常对照(P均<0.05).单细胞分析显示,IL6和FABP4沿单核细胞向2型常规树突状细胞的拟时序进程升高,COPD组巨噬细胞与Club细胞间通讯增强(P<0.01).5基因联合模型的曲线下面积为0.921.与对照组比较,COPD组小鼠0.3 s用力呼气容积降低(P=0.031 4)、用力肺活量升高(P=0.028 7)、二者比值降低(P=0.004 8),并出现肺气肿样病理改变;5个基因的mRNA和蛋白表达均升高(P<0.05).结论 SPATA2、CEMIP、FABP4、DPP4和IL6是与COPD铁死亡、炎症及脂质代谢异常相关的候选标志物;5基因联合模型在建模数据中具有较好的区分效能,且其异常高表达已在小鼠模型中得到证实,可为后续COPD机制研究和辅助诊断标志物筛选提供参考.

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.

钟勇;朱佑君;王强;左玉洁

陆军军医大学(第三军医大学)第二附属医院,心内科,重庆陆军军医大学(第三军医大学)第二附属医院,心内科,重庆陆军军医大学(第三军医大学)第二附属医院,药剂科,重庆重庆市公共卫生医疗救治中心结核二科,重庆

医药卫生

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

chronic obstructive pulmonary diseaseferroptosissingle-cell transcriptomicsdiagnostic modelmachine learning

《陆军军医大学学报》 2026 (16)

2263-2275,13

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).

10.16016/j.2097-0927.202605032

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