Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, People's Republic of China.
Department of Pharmacy, First Affiliated Hospital of Kunming Medical University, Kunming, People's Republic of China.
BMC Pulm Med. 2022 Jan 9;22(1):29. doi: 10.1186/s12890-022-01824-3.
Asthma is a heterogeneous disease and different phenotypes based on clinical parameters have been identified. However, the molecular subgroups of asthma defined by gene expression profiles of induced sputum have been rarely reported.
We re-analyzed the asthma transcriptional profiles of the dataset of GSE45111. A deep bioinformatics analysis was performed. We classified 47 asthma cases into different subgroups using unsupervised consensus clustering analysis. Clinical features of the subgroups were characterized, and their biological function and immune status were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single sample Gene Set Enrichment Analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were performed to identify key gene modules and hub genes.
Unsupervised consensus clustering of gene expression profiles in asthma identified two distinct subgroups (Cluster I/II), which were significantly associated with eosinophilic asthma (EA) and paucigranulocytic asthma (PGA). The differentially expressed genes (DEGs) between the two subgroups were primarily enriched in immune response regulation and signal transduction. The ssGSEA suggested the different immune infiltration and function scores between the two clusters. The WGCNA and PPI analysis identified three hub genes: THBS1, CCL22 and CCR7. ROC analysis further suggested that the three hub genes had a good ability to differentiate the Cluster I from the Cluster II.
Based on the gene expression profiles of the induced sputum, we identified two asthma subgroups, which revealed different clinical characteristics, gene expression patterns, biological functions and immune status. The transcriptional classification confirms the molecular heterogeneity of asthma and provides a framework for more in-depth research on the mechanisms of asthma.
哮喘是一种异质性疾病，已经基于临床参数确定了不同的表型。然而，基于诱导痰基因表达谱定义的哮喘分子亚群很少有报道。
我们重新分析了数据集 GSE45111 中的哮喘转录谱。进行了深入的生物信息学分析。我们使用无监督共识聚类分析将 47 例哮喘病例分为不同亚组。对亚组的临床特征进行了表征，并使用基因本体论 (GO)、京都基因与基因组百科全书 (KEGG) 和单样本基因集富集分析 (ssGSEA) 分析了它们的生物学功能和免疫状态。进行加权基因共表达网络分析 (WGCNA) 和蛋白质-蛋白质相互作用 (PPI) 网络分析，以鉴定关键基因模块和枢纽基因。
哮喘基因表达谱的无监督共识聚类鉴定了两个截然不同的亚组 (Cluster I/II)，它们与嗜酸性粒细胞性哮喘 (EA) 和少粒细胞性哮喘 (PGA) 显著相关。两个亚组之间的差异表达基因 (DEGs) 主要富集在免疫反应调节和信号转导中。ssGSEA 提示两个聚类之间的不同免疫浸润和功能评分。WGCNA 和 PPI 分析鉴定了三个枢纽基因：THBS1、CCL22 和 CCR7。ROC 分析进一步表明，这三个枢纽基因能够很好地区分 Cluster I 和 Cluster II。
基于诱导痰的基因表达谱，我们确定了两个哮喘亚组，揭示了不同的临床特征、基因表达模式、生物学功能和免疫状态。转录分类证实了哮喘的分子异质性，并为更深入研究哮喘机制提供了框架。
