Wei Changjin, Zhu Yongfeng, Chen Caiming, Li Feipeng, Zheng Li
Department of Respiratory Medicine, Jiangbin Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
Front Med (Lausanne). 2025 Jul 21;12:1592802. doi: 10.3389/fmed.2025.1592802. eCollection 2025.
This study aims to investigate the potential roles and mechanisms of inflammatory genes in COPD.
Transcriptome data from the airway epithelial tissues of COPD patients and normal individuals were downloaded from the GEO database. Differential gene expression analysis was performed using R software and its limma package, followed by GO, KEGG, and GSEA enrichment analyses. Inflammatory-related differentially expressed genes were screened based on literature data and analyzed for pathway enrichment using the Metascape database. Inflammatory-related COPD feature genes were selected using Lasso regression and random forest algorithms, and a COPD risk prediction model was constructed. Differences between the immune microenvironment of COPD and normal samples were analyzed using the ESTIMATE algorithm, the CIBERSORT method, and single-cell sequencing data. COPD patients were clustered using the ConsensusClusterPlus algorithm, and the pathway activity differences of different inflammatory subtypes were analyzed using GSVA. Potential traditional Chinese medicine monomer components capable of targeting key biomarker proteins were screened using the HERB database, and their binding potential was evaluated through molecular docking and molecular dynamics simulations.
A total of 495 significantly differentially expressed genes were identified, showing distinct expression patterns between COPD patients and healthy individuals. Functional and pathway enrichment analyses revealed significant enrichment of processes such as keratinocyte differentiation, arachidonic acid metabolism, IL-17 signaling pathway, and TNF signaling pathway in COPD. Fourteen inflammatory-related COPD genes were identified, which were significantly enriched in immune system processes and inflammatory responses. Using Lasso regression and random forest algorithms, seven feature genes were selected to construct a COPD risk prediction model, which demonstrated good accuracy. Immune cell infiltration analysis revealed a significant increase in monocytes, M0 macrophages, and eosinophils in COPD patients. Clustering analysis identified two inflammatory subtypes, with genes such as CLEC5A and CXCL8 significantly upregulated in the C2 subtype. Cinnamaldehyde, a potential traditional Chinese medicine monomer component, was identified to potentially exert anti-inflammatory effects by targeting the CXCL8 protein.
This study reveals significantly enriched biological processes and pathways in COPD patients, identifies multiple inflammatory-related COPD feature genes, and finds that cinnamaldehyde may have potential therapeutic effects on inflammatory subtypes of COPD.
本研究旨在探讨炎症基因在慢性阻塞性肺疾病（COPD）中的潜在作用及机制。
从基因表达综合数据库（GEO数据库）下载COPD患者和正常个体气道上皮组织的转录组数据。使用R软件及其limma包进行差异基因表达分析，随后进行基因本体（GO）、京都基因与基因组百科全书（KEGG）和基因集富集分析（GSEA）。基于文献数据筛选炎症相关差异表达基因，并使用Metascape数据库进行通路富集分析。使用套索回归和随机森林算法选择炎症相关的COPD特征基因，并构建COPD风险预测模型。使用ESTIMATE算法、CIBERSORT方法和单细胞测序数据分析COPD与正常样本免疫微环境的差异。使用ConsensusClusterPlus算法对COPD患者进行聚类，并使用基因集变异分析（GSVA）分析不同炎症亚型的通路活性差异。使用中药系统药理学数据库（HERB数据库）筛选能够靶向关键生物标志物蛋白的潜在中药单体成分，并通过分子对接和分子动力学模拟评估它们的结合潜力。
共鉴定出495个显著差异表达基因，显示出COPD患者与健康个体之间不同的表达模式。功能和通路富集分析显示，COPD中角质形成细胞分化、花生四烯酸代谢、白细胞介素-17信号通路和肿瘤坏死因子信号通路等过程显著富集。鉴定出14个炎症相关的COPD基因，它们在免疫系统过程和炎症反应中显著富集。使用套索回归和随机森林算法，选择了7个特征基因构建COPD风险预测模型，该模型显示出良好的准确性。免疫细胞浸润分析显示，COPD患者中单核细胞、M0巨噬细胞和嗜酸性粒细胞显著增加。聚类分析确定了两种炎症亚型，C2亚型中C型凝集素结构域家族5成员A（CLEC5A）和CXC趋化因子配体8（CXCL8）等基因显著上调。肉桂醛是一种潜在的中药单体成分，被确定可能通过靶向CXCL8蛋白发挥抗炎作用。
本研究揭示了COPD患者中显著富集的生物学过程和通路，鉴定出多个炎症相关的COPD特征基因，并发现肉桂醛可能对COPD的炎症亚型具有潜在治疗作用。
