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山东大学学报 (医学版) ›› 2024, Vol. 62 ›› Issue (5): 43-53.doi: 10.6040/j.issn.1671-7554.0.2024.0079

• 慢性气道疾病的精准个体化诊疗——临床研究 • 上一篇    

基于生物信息学探索支气管哮喘中的潜在差异免疫基因和免疫浸润特征

石硕川1*,曾荣2*,张锦涛1,张东2,潘云2,刘晓菲1,许长娟1,王莹1,董亮1,2   

  1. 1.山东第一医科大学第一附属医院呼吸与危重症医学科 山东省呼吸疾病研究所, 山东 济南 250014;2.山东大学 山东省千佛山医院呼吸与危重症医学科, 山东 济南 250014
  • 发布日期:2024-05-29
  • 通讯作者: 董亮. E-mail:dl5506@126.com*共同第一作者.
  • 基金资助:
    山东省重点研发计划(2021SFGC0504);国家自然科学基金(82270032);山东省自然科学基金联合基金(ZR2021LSW015);济南市呼吸疾病临床医学研究中心项目(202132002)

Bioinformatics-based exploration of potential differential immune genes and immune infiltration signatures in bronchial asthma

SHI Shuochuan1*, ZENG Rong2*, ZHANG Jintao1, ZHANG Dong2, PAN Yun2, LIU Xiaofei1, XU Changjuan1, WANG Ying1, DONG Liang1,2   

  1. 1. Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University, Shandong Institute of Respiratory Diseases, Jinan 250014, Shandong, China;
    2. Department of Respiratory, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan 250014, Shandong, China
  • Published:2024-05-29

摘要: 目的 识别支气管哮喘发生发展过程中差异表达的免疫关键基因和免疫细胞,并探讨两者之间的相关性。 方法 从公共基因表达数据库(gene expression omnibus, GEO)和Import数据库中分别下载哮喘相关数据集和免疫相关基因,利用R软件分析获得GSE76262中差异表达的免疫相关基因(differentially expressed immune-related genes, DE-IRGs)。在STRING数据库中明确DE-IRGs间的相互作用。使用Cytoscape软件中的CytoHubba插件筛选关键的DE-IRGs,并在GSE137268中进行验证。利用受试者工作特征(receiver operating characteristic, ROC)曲线评估关键DE-IRGs作为生物标志物的潜力。此外,单样本基因集富集分析(single-sample gene set enrichment analysis, ssGSEA)算法被用来分析28种免疫细胞在哮喘和健康者中的表达差别,使用斯皮尔曼相关系数评估关键免疫基因与免疫细胞之间的相关性。 结果 在GSE76262中鉴定出17个DE-IRGs,经PPI网络的筛选和在GSE137268中的验证,CCL22、CCR7、IL1R2、IL18R1、TNFAIP3和VEGFA被识别为哮喘患者诱导痰中的关键DE-IRGs且具有较高的诊断价值。此外,ssGSEA的结果提示哮喘患者存在明显的免疫失衡,与健康者相比,11种免疫细胞在哮喘患者诱导痰中的浸润明显增加。同时,CCL22、CCR7、IL1R2、IL18R1、VEGFA、TNFAIP3与浸润的免疫细胞呈显著正相关。 结论 CCL22、CCR7、IL1R2、IL18R1、VEGFA、TNFAIP3可作为哮喘的潜在生物标志物,且可能参与调控其发病过程中免疫细胞的浸润。

关键词: 支气管哮喘, 免疫基因, 生物标志物, 免疫浸润

Abstract: Objective To identify immunologically critical genes and immune cells that are differentially expressed during the development of asthma and to explore the correlation between them. Methods The asthma-related datasets and immune-related genes were downloaded from the Gene Expression Omnibus database(GEO)and Import database, respectively, and analyzed using R software to obtain differentially expressed immune-related genes(DE-IRGs)in GSE76262. The interactions between DE-IRGs were clarified in the STRING database. Key DE-IRGs were screened using the CytoHubba plugin in Cytoscape software and validated in GSE137268. Receiver operating characteristic(ROC)curves were used to assess the potential of critical DE-IRGs as biomarkers. The single-sample gene set enrichment analysis(ssGSEA)algorithm was used to examine the differential expression of 28 immune cells in asthmatic and healthy individuals. Spearman correlation coefficients were used to evaluate the correlation between key immune genes and immune cells. Results Seventeen DE-IRGs were identified in GSE76262, and CCL22, CCR7, IL1R2, IL18R1, TNFAIP3, and VEGFA were identified as critical DE-IRGs in induced sputum from asthmatics with high diagnostic value, as screened by the PPI network and validated in GSE137268. In addition, the results of ssGSEA suggested a significant immune imbalance in asthmatics, with 11 types of immune cells significantly infiltrated in the induced sputum of asthmatics compared to healthy individuals. Meanwhile, CCL22, CCR7, IL1R2, IL18R1, VEGFA, and TNFAIP3 were positively correlated with infiltrated immune cells. Conclusion CCL22, CCR7, IL1R2, IL18R1, VEGFA, and TNFAIP3 serve as potential biomarkers of asthma and may modulate infiltration of immune cells in its pathogenesis.

Key words: Bronchial asthma, Immune-related genes, Biomarkers, Immune infiltration

中图分类号: 

  • R562.2
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