Du Fawang, Wang Hanchao, Chen Zhihong, Xiong Wei, Wang Qin, Li Bo, Li Rong, Li Li, Shen Yongchun, Zhu Tao
Department of Respiratory Medicine and Critical Care Medicine, Suining Central Hospital, Suining, Sichuan, 629000, People's Republic of China.
GK Health and Medical Big Data Research Center of Suining, Suining Central Hospital, Suining, Sichuan, 629000, People's Republic of China.
J Asthma Allergy. 2025 Jun 24;18:1051-1064. doi: 10.2147/JAA.S517140. eCollection 2025.
Asthma severity assessment is essential for asthma management. Transcriptomics contributes substantially to asthma pathogenesis. Then, this study aimed to explore asthma severity-associated transcriptomics profile and promising biomarkers for asthma severity prediction.
In discovery cohort, induced sputum cells from 3 non-severe and 3 severe asthma patients were collected and analyzed using RNA-seq. Multivariate analysis was performed to explore asthma severity-associated transcriptomics profile and differential expressed genes (DEGs). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used for pathway enrichment analysis. Subsequently, based on the previous study and clinical experience, the mRNA expressions of 6 overlapped asthma severity-associated DEGs and in induced sputum cells and serum C3 were verified in validation cohort.
Distinct asthma severity-associated transcriptomics profile was identified in induced sputum cells in discovery cohort. Then, 345 DEGs were found, of which 38 terms and 32 pathways were enriched using GO and KEGG, respectively. In validation cohort, the mRNA expressions of , and were increased, and and were decreased in induced sputum cells in severe asthma. Meanwhile, the AUC of ROC was 0.890 for serum C3 in asthma severity prediction, with the best cut-off of 1.272 g/L.
Collectively, this study provides the first identification of the association between induced sputum cells transcriptomics profile and asthma severity, indicating the potential value of transcriptomics for asthma management. The study also reveals the promising value of serum C3 for predicting asthma severity in clinical practice.
哮喘严重程度评估对于哮喘管理至关重要。转录组学对哮喘发病机制有重要贡献。因此,本研究旨在探索与哮喘严重程度相关的转录组学特征以及用于预测哮喘严重程度的有前景的生物标志物。
在发现队列中,收集3名非重度哮喘患者和3名重度哮喘患者的诱导痰细胞,并用RNA测序进行分析。进行多变量分析以探索与哮喘严重程度相关的转录组学特征和差异表达基因(DEGs)。使用京都基因与基因组百科全书(KEGG)和基因本体论(GO)进行通路富集分析。随后,基于先前的研究和临床经验,在验证队列中验证了诱导痰细胞中6个重叠的与哮喘严重程度相关的DEGs以及血清C3的mRNA表达。
在发现队列的诱导痰细胞中鉴定出了与哮喘严重程度不同的转录组学特征。然后,发现了345个DEGs,其中分别使用GO和KEGG富集了38个术语和32条通路。在验证队列中,重度哮喘患者诱导痰细胞中、和的mRNA表达增加,而和的表达降低。同时,血清C3在哮喘严重程度预测中的ROC曲线下面积为0.890,最佳截断值为1.272 g/L。
总体而言,本研究首次确定了诱导痰细胞转录组学特征与哮喘严重程度之间的关联,表明转录组学在哮喘管理中的潜在价值。该研究还揭示了血清C3在临床实践中预测哮喘严重程度的有前景的价值。