Liu Xinning, Li Bing, Liu Shuya, Zong Jinbao, Zheng Xin
Central Laboratory, Clinical Laboratory and Qingdao Key Laboratory of Immunodiagnosis, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, 266034, China.
Department of Neurology, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, 266034, China.
Heliyon. 2024 Jul 20;10(15):e34766. doi: 10.1016/j.heliyon.2024.e34766. eCollection 2024 Aug 15.
Asthma is a heterogeneous airway inflammatory disease that can be classified according to the inflammatory phenotype. The pathogenesis, clinical features, response to hormone therapy, and prognosis of different inflammatory phenotypes differ significantly. This condition also refers to age-related chronic ailments. Here, we intend to identify the function of aging-related genes in different inflammatory phenotypes of asthma using bioinformatic analyses. Initially, the research adopted the GSEA analysis to understand the fundamental mechanisms that govern different inflammatory phenotypes of asthma pathogenesis and use the CIBERSORT algorithm to assess the immune cell composition. The differentially expressed genes (DEGs) of eosinophilic asthma (EA), neutrophilic asthma (NA), and paucigranulocytic asthma (PGA) were identified through the limma R package. Aging-related genes, screened from multiple databases, were intersected with DEGs of asthma to obtain the asthma-aging-related DEGs. Then, the GO and KEGG pathway enrichment analyses showed that the NA- and EA-aging-related DEGs are involved in the various cytokine-mediated signaling pathways. PPI network and correlation analysis were performed to identify and evaluate the correlation of the hub genes. Further, the clinical characteristics of asthma-aging-related DEGs were explored through ROC analysis. 3 and 12 aging-related DEGs in EA and NA patients show high diagnostic accuracy, respectively (AUC >0.7). This study provided valuable insights into aging-related gene therapy for phenotype-specific asthma. Moreover, the study suggests that effective interventions against asthma may operate by disrupting the detrimental cycle of "aging induces metabolic diseases, which exacerbate aging".
哮喘是一种异质性气道炎症性疾病，可根据炎症表型进行分类。不同炎症表型的发病机制、临床特征、对激素治疗的反应及预后差异显著。这种疾病也指与年龄相关的慢性疾病。在此，我们打算通过生物信息学分析确定衰老相关基因在哮喘不同炎症表型中的作用。最初，该研究采用基因集富集分析（GSEA）来了解哮喘发病机制中不同炎症表型的基本调控机制，并使用CIBERSORT算法评估免疫细胞组成。通过limma R包鉴定嗜酸性粒细胞性哮喘（EA）、中性粒细胞性哮喘（NA）和少粒细胞性哮喘（PGA）的差异表达基因（DEG）。从多个数据库中筛选出的衰老相关基因与哮喘的DEG进行交集分析，以获得与哮喘-衰老相关的DEG。然后，基因本体（GO）和京都基因与基因组百科全书（KEGG）通路富集分析表明，与NA和EA衰老相关的DEG参与了各种细胞因子介导的信号通路。进行蛋白质-蛋白质相互作用（PPI）网络和相关性分析以鉴定和评估枢纽基因的相关性。此外，通过受试者工作特征（ROC）分析探索了与哮喘-衰老相关的DEG的临床特征。EA和NA患者中分别有3个和12个与衰老相关的DEG显示出较高的诊断准确性（曲线下面积>0.7）。本研究为针对特定表型哮喘的衰老相关基因治疗提供了有价值的见解。此外，该研究表明，针对哮喘的有效干预措施可能通过打破“衰老诱发代谢疾病，进而加剧衰老”的有害循环来发挥作用。
