Abstract：
Recent studies have shown that cellular senescence is involved in the pathogenesis of severe asthma (SA). The objective of this study was to investigate the role of cellular senescence-related genes (CSGs) in the pathogenesis of SA. Here, 54 differentially expressed CSGs were identified in SA patients compared to healthy control individuals. Among the 54 differentially expressed CSGs, 3 CSGs (ETS2, ETS1 and AURKA) were screened using the LASSO regression analysis and logistic regression analysis to establish the CSG-based prediction model to predict severe asthma. Moreover, we found that the protein expression levels of ETS2, ETS1 and AURKA were increased in the severe asthma mouse model. Then, two distinct senescence subtypes of SA with distinct immune microenvironments and molecular biological characteristics were identified. Cluster 1 was characterized by increased infiltration of immature dendritic cells, regulatory T cells, and other cells. Cluster 2 was characterized by increased infiltration levels of eosinophils, neutrophils, and other cells. The molecular biological characteristics of Cluster 1 included aerobic respiration and oxidative phosphorylation, whereas the molecular biological characteristics of Cluster 2 included activation of the immune response and immune receptor activity. Then, we established an Random Forest model to predict the senescence subtypes of SA to guide treatment. Finally, potential drugs were searched for each senescence subgroup of SA patients via the Connectivity Map database. A peroxisome proliferator-activated receptor agonist may be a potential therapeutic drug for patients in Cluster 1, whereas a tachykinin antagonist may be a potential therapeutic drug for patients in Cluster 2. In summary, CSGs are likely involved in the pathogenesis of SA, which may lead to new therapeutic options for SA patients.
摘要：
最近的研究表明，细胞衰老参与了严重哮喘（SA）的发病机制。本研究的目的是探讨细胞衰老相关基因（CSGs）在SA发病机理中的作用。这里,与健康对照个体相比,在SA患者中鉴定了54个差异表达的CSG。在54个差异表达的CSG中，采用LASSO回归分析和logistic回归分析筛选3个CSGs(ETS2、ETS1和AURKA),建立基于CSG的预测模型预测重度哮喘。此外，我们发现,在重度哮喘小鼠模型中,ETS2,ETS1和AURKA蛋白表达水平升高.然后，确定了具有不同免疫微环境和分子生物学特征的两种不同的SA衰老亚型。簇1的特征是未成熟树突状细胞的浸润增加，调节性T细胞,和其他细胞。群集2的特征是嗜酸性粒细胞的浸润水平增加，中性粒细胞，和其他细胞。簇1的分子生物学特征包括有氧呼吸和氧化磷酸化，而簇2的分子生物学特征包括免疫应答的激活和免疫受体的活性。然后，我们建立了随机森林模型来预测SA的衰老亚型以指导治疗。最后,通过ConnectivityMap数据库搜索SA患者的每个衰老亚组的潜在药物.过氧化物酶体增殖物激活受体激动剂可能是第1组患者的潜在治疗药物，而速激肽拮抗剂可能是第2组患者的潜在治疗药物。总之,CSGs可能参与了SA的发病机制，这可能会为SA患者带来新的治疗选择。
