基于生物信息学和机器学习鉴定慢性阻塞性肺病合并肺动脉高压标志物及实验验证  

Identification and experimental validation of biomarkers for chronic obstructive pulmonary disease complicated with pulmonary arterial hypertension based on bioinformatics and machine learning

作　　者：杨燕 陶春容 朱佑君 张聪[2] 李德峰 YANG Yan;TAO Chunrong;ZHU Youjun;ZHANG Cong;LI Defeng(Department of Cardiology,Second Affiliated Hospital,Army Medical University(Third Military Medical University),Chongqing;Animal Research Center,College of Basic Medical Sciences,Army Medical University(Third Military Medical University),Chongqing,China;Clinical Medical Research Center,Second Affiliated Hospital,Army Medical University(Third Military Medical University),Chongqing)

机构地区：[1]陆军军医大学(第三军医大学)第二附属医院心内科,重庆 [2]陆军军医大学(第三军医大学)基础医学院实验动物学教研室,重庆 [3]陆军军医大学(第三军医大学)第二附属医院临床医学研究中心,重庆

出　　处：《陆军军医大学学报》2025年第9期948-958,共11页Journal of Army Medical University

基　　金：重庆市自然科学基金博士后科学基金(cstc2021jcyjbshX0071)。

摘　　要：目的 利用生物信息学筛选诊断慢性阻塞性肺病(chronic obstructive pulmonary disease,COPD)合并肺动脉高压(pulmonary arterial hypertension, PAH)的关键生物标志物,并验证其临床意义。方法 通过高通量测序数据分析鉴定了COPD合并PAH的差异表达基因(differentially expressed genes, DEGs),并通过功能富集分析探讨了这些基因的生物学功能。利用最小绝对收缩与选择算子(least absolute shrinkage and selection operator, LASSO)、随机森林(random forest, RF)、支持向量机递归特征消除(support vector machine-recursive feature elimination, SVM-RFE)机器学习方法筛选出5个潜在的生物标志物。通过单细胞分析揭示了关键基因在巨噬细胞中的表达模式。提取肺组织巨噬细胞进行细胞水平验证,观察肺PAH关键生物标志物表达差异。通过外周血单个核细胞(peripheral blood mononuclear cells, PBMC)数据验证关键生物标志物临床意义。利用缺氧方法建立COPD合并PAH小鼠模型,将16只8周龄普通级COPD小鼠(雌雄不限,体质量20~22 g)按随机数字抽样法分为(n=8):缺氧组(氧浓度为10%±0.5%,COPD合并PAH组)和常氧组(COPD组)。使用免疫荧光技术标记关键生物标志物,计算表达情况。结果 COPD合并PAH筛选出28个DEGs(|Log_(2)FC|≥2, P<0.05)。功能富集分析显示,COPD合并PAH的DEGs与主要组织相容性复合体(majorhistocompatibility complex,MHC)Ⅱ和细胞分裂相关,参与溶酶体、氧化磷酸化及细胞周期通路(P<0.05)。机器学习得到5个潜在的生物标志物(GRN、KLF4、SHTN1、LRP1和GPNMB),进一步的单细胞分析揭示了这些标志物在疾病发展中存在逆向表达模式。从PBMCs中构建的诺模图模型,用于诊断COPD合并PAH[受试者操作曲线下面积(area under the subject curve,AUC)为0.907]。COPD合并PAH组GRN、KLF4、SHTN1、LRP1和GPNMB蛋白和基因显著高表达(P<0.05)。结论 本研究发现了GRN、KLF4、SHTN1、LRP1和GPNMB可作为COPD合并PAH预Objective To identify the key biomarkers for diagnosing chronic obstructive pulmonary disease(COPD) complicated with pulmonary arterial hypertension(PAH) using bioinformatics, and validate their clinical significance. Methods High-throughput sequencing data analysis was employed to identify differentially expressed genes(DEGs) in COPD-PAH. Functional enrichment analysis was then conducted to explore the biological functions of these DEGs. Machine learning methods, including least absolute shrinkage and selection operator(LASSO), random forest(RF), and support vector machine-recursive feature elimination(SVM-RFE), were utilized to screen 5 potential biomarkers. Single-cell analysis was performed to reveal the expression patterns of these key genes in macrophages. The clinical significance of these biomarkers was further validated using peripheral blood mononuclear cells(PBMC) data. A mouse model of COPD-PAH was established using hypoxia exposure. Sixteen mice(either sexes, 8 weeks old, weighing 20~22 g) were randomly divided into a hypoxia group [O_(2)(10. 0±0. 5)%, COPD-PAH, n=8] and a normoxia group(COPD,n=8). Immunofluorescence assay was used to label the key biomarkers, and their expression levels were quantified. Results A total of 28 DEGs(|Log_(2)FC| ≥2, P<0. 05) were identified in COPD-PAH patients.Functional enrichment analysis indicated that DEGs in COPD were primarily associated with major histocompatibility complex(MHC) Ⅱ and cell division, and involved in lysosomes, oxidative phosphorylation,and cell cycle pathways(P<0. 05). Machine learning identified 5 potential biomarkers(GRN, KLF4, SHTN1,LRP1, and GPNMB), and subsequent single-cell analysis revealed that these markers exhibited reverse expression patterns during disease progression. A nomogram model constructed based on PBMC data yielded an area under the curve(AUC) of 0. 907 in diagnosing COPD-PAH. GRN, KLF4, SHTN1, LRP1 and GPNMB were significantly upregulated in the COPD-PAH group(P<0. 05).Conclusion GRN, KLF4, SHTN1, LRP1and GPNMB are iden

关 键 词：慢性阻塞性肺病合并肺动脉高压 诊断标志物 机器学习 生物信息学分析 肺巨噬细胞 

分 类 号：R318.04[医药卫生—生物医学工程] R544[医药卫生—基础医学] R563.9