Integrating Extracellular Vesicles Proteomics and Clinical Parameters to Develop a High-Precision Predictive Model...

Area of Science:

• Pulmonary Medicine
• Biomarker Discovery
• Proteomics

Background:

• Asthma severity varies, necessitating accurate differentiation for effective management.
• Extracellular vesicles (EVs) and their proteomic cargo offer potential biomarkers.

Purpose of the Study:

• To identify biomarkers and develop a predictive model for distinguishing severe asthma (SA) from non-severe asthma (NSA).
• To integrate clinical data with EV proteomics for enhanced diagnostic accuracy.

Main Methods:

• Isolated plasma-derived EVs from healthy controls, SA patients, and NSA patients.
• Performed proteomic profiling of EVs using proximity barcoding assay (PBA).
• Constructed a multivariate model combining clinical indicators and EV proteins, validated with ROC analysis.

Main Results:

• Specific EV proteins (SELL, PECAM1, ITGB3, CD9) were elevated in SA.
• Protein combinations (ITGB3&CLDN1, ESAM&ITGA6) showed strong discriminatory power (AUC > 0.8).
• An integrative model achieved high predictive accuracy (AUC = 0.97 ± 0.075), with IL6, NGFR, NFASC, PCDHA1 identified as key proteins.

Conclusions:

• A reliable multi-parameter model integrating clinical data and EV proteomics can distinguish SA from NSA.
• EV-based biomarkers show promise for early diagnosis and personalized management of severe asthma.