Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome

• Published in: Disease markers 2022-11, Vol.2022, p.1-20
• Main Authors: Jiang, Yong, Yan, Qian, Zhang, Miaofen, Lin, Xueying, Peng, Chenwen, Huang, Hui-ting, Liao, Gang, Liu, Qiong, Liao, Huili, Zhan, Shao-feng, Liu, Xiaohong, Huang, Xiufang
• Format: Article
• Language: English
• Subjects: Actomyosin; Adaptive immunity; Algorithms; Asthma; Bioinformatics; Biosynthesis; CD8 antigen; Cluster analysis; Correlation analysis; Datasets; Drug dosages; Epithelial cells; Epithelium; Gene expression; Genes; Helper cells; Immune system; Immunosuppressive agents; Infiltration; Inflammation; Inflammatory diseases; Leukocytes (neutrophilic); Lymphocytes; Lymphocytes T; Mast cells; Modules; Network analysis; Patients; Pluripotency; Protein interaction; Proteins; Respiratory tract; Salmon; Signal transduction; Signaling; Software; Statistical analysis; Stem cells; Steroids; Transcriptomes
• ISSN: 0278-0240; 1875-8630
• DOI: 10.1155/2022/8906064

Background. Severe asthma (SA), a heterogeneous inflammatory disease characterized by immune cell infiltration, is particularly difficult to treat and manage. The airway epithelium is an important tissue in regulating innate and adaptive immunity, and targeting airway epithelial cell may contribute to improving the efficacy of asthma therapy. Methods. Bioinformatics methods were utilized to identify the hub genes and signaling pathways involved in SA. Experiments were performed to determine whether these hub genes and signaling pathways were affected by the differences in immune cell infiltration. Results. The weighted gene coexpression network analysis identified 14 coexpression modules, among which the blue and salmon modules exhibited the strongest associations with SA. The blue module was mainly enriched in actomyosin structure organization and was associated with regulating stem cell pluripotency signaling pathways. The salmon module was mainly involved in cornification, skin development, and glycosphingolipid biosynthesis-lacto and neolacto series. The protein-protein interaction network and module analysis identified 11 hub genes in the key modules. The CIBERSORTx algorithm revealed statistically significant differences in CD8+ T cells (P=0.013), T follicular helper cells (P=0.002), resting mast cells (P=0.004), and neutrophils (P=0.002) between patients with SA and mild-moderate asthma patients. Pearson’s correlation analysis identified 11 genes that were significantly associated with a variety of immune cells. We further predicted the utility of some potential drugs and validated our results in external datasets. Conclusion. Our results may help provide a better understanding of the relationship between the airway epithelial transcriptome and clinical data of SA. And this study will help to guide the development of SA-targeted molecular therapy.