114027
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
整合细胞外囊泡蛋白质组学和临床参数以开发针对重症哮喘的高精度预测模型
Authors Qian Y , Wang F, Gao J, Jiang Z , Gao X , Chen Z
Received 4 August 2025
Accepted for publication 11 December 2025
Published 23 December 2025 Volume 2025:18 Pages 17945—17960
DOI https://doi.org/10.2147/JIR.S556255
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Cynthia Koziol-White
Yuhao Qian,1,* Fang Wang,2,* Jiameng Gao,2 Zhilong Jiang,2 Xiwen Gao,1 Zhihong Chen3
1Department of Respiratory and Critical Care Medicine, Minhang Hospital, Fudan University, Shanghai, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine of Zhongshan Hospital, Shanghai Institute of Respiratory Disease, Fudan University, Shanghai, People’s Republic of China; 3Department of Pulmonary and Critical Care Medicine, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Zhihong Chen, Department of Pulmonary and Critical Care Medicine, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China, Email chenzhihong@xinhuamed.com.cn Xiwen Gao, Department of Respiratory and Critical Care Medicine, Minhang Hospital, Fudan University, Shanghai, People’s Republic of China, Email xiwengao@sina.com
Introduction: This study aimed to identify biomarkers and develop a predictive model for distinguishing severe asthma (SA) from non-severe asthma (NSA) by integrating clinical data and extracellular vesicles (EVs) proteomics.
Methods: Plasma-derived EVs were isolated from 44 individuals, including 15 healthy controls, 15 SA patients, and 14 NSA patients. Proteomic profiling of EVs was performed using proximity barcoding assay (PBA). Clinical indicators such as FEV1/FVC ratio, DLCO% predicted, and blood neutrophil count were recorded. A multivariate model incorporating both clinical and EV-derived protein data was constructed and evaluated using ROC curve analysis. Candidate biomarkers were further validated in cell-based and murine SA models.
Results: Although total EV counts and protein load did not differ significantly across groups, specific EV proteins (eg, SELL, PECAM1, ITGB3, CD9) were consistently elevated. Notably, protein combinations such as ITGB3&CLDN1 and ESAM&ITGA6 showed strong discriminatory power between SA and NSA (AUC > 0.8). The integrative model combining clinical metrics and EV proteins (IL6, NGFR, NFASC, PCDHA1) achieved a high predictive accuracy (AUC = 0.97 ± 0.075). Expression of IL6, NGFR, and NFASC was significantly upregulated in SA cellular and animal models, aligning with patient data.
Conclusion: This study presents a reliable multi-parameter model for distinguishing severe from non-severe asthma, leveraging both clinical indicators and EV proteomics. These findings support the potential of EV-based biomarkers in early diagnosis and personalized management of SA.
Keywords: extracellular vesicle, EVs, proximity barcoding assay, PBA, asthma, severe asthma, Aspergillus flavus
1Department of Respiratory and Critical Care Medicine, Minhang Hospital, Fudan University, Shanghai, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine of Zhongshan Hospital, Shanghai Institute of Respiratory Disease, Fudan University, Shanghai, People’s Republic of China; 3Department of Pulmonary and Critical Care Medicine, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Zhihong Chen, Department of Pulmonary and Critical Care Medicine, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China, Email chenzhihong@xinhuamed.com.cn Xiwen Gao, Department of Respiratory and Critical Care Medicine, Minhang Hospital, Fudan University, Shanghai, People’s Republic of China, Email xiwengao@sina.com
Introduction: This study aimed to identify biomarkers and develop a predictive model for distinguishing severe asthma (SA) from non-severe asthma (NSA) by integrating clinical data and extracellular vesicles (EVs) proteomics.
Methods: Plasma-derived EVs were isolated from 44 individuals, including 15 healthy controls, 15 SA patients, and 14 NSA patients. Proteomic profiling of EVs was performed using proximity barcoding assay (PBA). Clinical indicators such as FEV1/FVC ratio, DLCO% predicted, and blood neutrophil count were recorded. A multivariate model incorporating both clinical and EV-derived protein data was constructed and evaluated using ROC curve analysis. Candidate biomarkers were further validated in cell-based and murine SA models.
Results: Although total EV counts and protein load did not differ significantly across groups, specific EV proteins (eg, SELL, PECAM1, ITGB3, CD9) were consistently elevated. Notably, protein combinations such as ITGB3&CLDN1 and ESAM&ITGA6 showed strong discriminatory power between SA and NSA (AUC > 0.8). The integrative model combining clinical metrics and EV proteins (IL6, NGFR, NFASC, PCDHA1) achieved a high predictive accuracy (AUC = 0.97 ± 0.075). Expression of IL6, NGFR, and NFASC was significantly upregulated in SA cellular and animal models, aligning with patient data.
Conclusion: This study presents a reliable multi-parameter model for distinguishing severe from non-severe asthma, leveraging both clinical indicators and EV proteomics. These findings support the potential of EV-based biomarkers in early diagnosis and personalized management of SA.
Keywords: extracellular vesicle, EVs, proximity barcoding assay, PBA, asthma, severe asthma, Aspergillus flavus