Chronic obstructive pulmonary disease (COPD) is closely related to innate and adaptive inflammatory immune responses. It is increasingly becoming evident that metabolic syndrome (MetS) affects a significant portion of COPD patients. Through this investigation, we identify shared immune-related candidate biological markers. The Weighted Gene Co-Expression Network Analysis (WGCNA) was utilized to reveal the co-expression modules linked to COPD and MetS. The commonly expressed genes in the COPD and MetS were utilized to conduct an enrichment analysis. We adopted machine-learning to screen and validate hub genes. We also assessed the relationship between hub genes and immune cell infiltration in COPD and MetS, respectively. Moreover, associations across hub genes and metabolic pathways were also explored. Finally, we chose a single-cell RNA sequencing (scRNA-seq) dataset to investigate the hub genes and shared mechanisms at the level of the cells. We also applied cell trajectory analysis and cell-cell communication analysis to focus on the vital immune cell we were interested in. As a result, we selected and validated 13 shared hub genes for COPD and MetS. The enrichment analysis and immune infiltration analysis illustrated strong associations between hub genes and immunology; Additionally, we applied metabolic pathway enrichment analysis, indicating the significant role of reactive oxygen species (ROS) in COPD with MetS. Through scRNA-seq analysis, we found that ROS might accumulate the most in the alveolar macrophages. In conclusion, the 13 hub genes related to the immune response and metabolism may serve as diagnostic biomarkers and treatment targets of COPD with MetS.

中文翻译：

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慢性阻塞性肺疾病伴代谢综合征的免疫相关基因特征的鉴定：来自综合批量和单细胞 RNA 测序数据的证据。

慢性阻塞性肺疾病（COPD）与先天性和适应性炎症免疫反应密切相关。越来越明显的是，代谢综合征 (MetS) 影响着很大一部分慢性阻塞性肺病 (COPD) 患者。通过这项研究，我们确定了共享的免疫相关候选生物标记。利用加权基因共表达网络分析 (WGCNA) 揭示了与 COPD 和 MetS 相关的共表达模块。利用 COPD 和 MetS 中常见表达的基因进行富集分析。我们采用机器学习来筛选和验证枢纽基因。我们还分别评估了 COPD 和 MetS 中的 hub 基因与免疫细胞浸润之间的关系。此外，还探讨了中心基因和代谢途径之间的关联。最后，我们选择了单细胞 RNA 测序 (scRNA-seq) 数据集来研究细胞水平的枢纽基因和共享机制。我们还应用细胞轨迹分析和细胞间通讯分析来关注我们感兴趣的重要免疫细胞。因此，我们选择并验证了 COPD 和 MetS 的 13 个共享中心基因。富集分析和免疫浸润分析表明中枢基因与免疫学之间存在很强的关联性；此外，我们应用代谢途径富集分析，表明活性氧 (ROS) 在 COPD 伴 MetS 中的重要作用。通过scRNA-seq分析，我们发现ROS可能在肺泡巨噬细胞中积累最多。总之，与免疫反应和代谢相关的 13 个中心基因可以作为 MetS 慢性阻塞性肺病的诊断生物标志物和治疗靶点。