Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
Department of Obstetrics and Gynaecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Front Immunol. 2022 Sep 27;13:967357. doi: 10.3389/fimmu.2022.967357. eCollection 2022.
To study the tissue-infiltrating immune cells of the emphysema phenotype of chronic obstructive pulmonary disease (COPD) and find the molecular mechanism related to the development of emphysema to offer potential targets for more precise treatment of patients with COPD.
Combined analyses of COPD emphysema phenotype lung tissue-related datasets, GSE47460 and GSE1122, were performed. CIBERSORT was used to assess the distribution of tissue-infiltrating immune cells. Weighted gene co-expression network analysis (WGCNA) was used to select immune key genes closely related to clinical features. Rt-qPCR experiments were used for the validation of key genes. Emphysema risk prediction models were constructed by logistic regression analysis and a nomogram was developed.
In this study, three immune cells significantly associated with clinical features of emphysema (FEV1 post-bronchodilator % predicted, GOLD Stage, and DLCO) were found. The proportion of neutrophils (p=0.025) infiltrating in the emphysema phenotype was significantly increased compared with the non-emphysema phenotype, while the proportions of M2 macrophages (p=0.004) and resting mast cells (p=0.01) were significantly decreased. Five immune-related differentially expressed genes (DEGs) were found. WGCNA and clinical lung tissue validation of patients with emphysema phenotype were performed to further screen immune-related genes closely related to clinical features. A key gene (SERPINA3) was selected and included in the emphysema risk prediction model. Compared with the traditional clinical prediction model (AUC=0.923), the combined prediction model, including SERPINA3 and resting mast cells (AUC=0.941), had better discrimination power and higher net benefit.
This study comprehensively analyzed the tissue-infiltrating immune cells significantly associated with emphysema phenotype, including M2 macrophages, neutrophils, and resting mast cells, and identified SERPINA3 as a key immune-related gene.
研究慢性阻塞性肺疾病（COPD）肺气肿表型的组织浸润免疫细胞，寻找与肺气肿发生发展相关的分子机制，为 COPD 患者的更精准治疗提供潜在靶点。
对 COPD 肺气肿表型肺组织相关数据集 GSE47460 和 GSE1122 进行联合分析。采用 CIBERSORT 评估组织浸润免疫细胞的分布。采用加权基因共表达网络分析（WGCNA）筛选与临床特征密切相关的免疫关键基因。通过实时荧光定量 PCR（Rt-qPCR）实验对关键基因进行验证。采用逻辑回归分析构建肺气肿风险预测模型，并开发列线图。
本研究发现了 3 种与肺气肿临床特征显著相关的免疫细胞。与非肺气肿表型相比，肺气肿表型中中性粒细胞（p=0.025）的浸润比例显著增加，而 M2 巨噬细胞（p=0.004）和静止肥大细胞（p=0.01）的比例显著降低。发现了 5 个免疫相关差异表达基因（DEGs）。通过 WGCNA 和肺气肿表型患者的临床肺组织验证，进一步筛选与临床特征密切相关的免疫相关基因。选择了一个关键基因（SERPINA3）并纳入肺气肿风险预测模型。与传统的临床预测模型（AUC=0.923）相比，包括 SERPINA3 和静止肥大细胞（AUC=0.941）的联合预测模型具有更好的判别能力和更高的净收益。
本研究全面分析了与肺气肿表型显著相关的组织浸润免疫细胞，包括 M2 巨噬细胞、中性粒细胞和静止肥大细胞，并确定 SERPINA3 为关键免疫相关基因。
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