Inflammation-related proteomics of extracellular vesicles as novel biomarkers for systemic lupus erythematosus revealed by proximity extension assay
Arthritis Research & Therapy, 2025
Zhan S., Wang Z., Xu Y., Zhou S., Ge M., Song Y., Zhu Y., Dou H., Shen H., Yang P.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Immunological & Inflammatory Diseases | Patient Stratification | EV Lysate |  Olink Target 96 |
Abstract
Background
Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by dysregulated inflammatory response lacking reliable diagnosis biomarkers and therapy targets. Extracellular vesicles (EVs)-derived cargo as biomarkers and mediators of SLE have garnered significant attention, however, quantitative inflammatory protein profile of SLE EVs remain uncovered.
Objective
Current study focuses on exploring the inflammatory protein landscape of SLE serum EVs via quantitative proximity extension assay (PEA) and evaluates their diagnostic utility for SLE and lupus nephritis (LN).
Methods
In this cross-sectional study, we first utilized PEA to profile inflammatory proteins derived from serum EVs in 101 individuals, including 70 SLE patients and 33 healthy controls (HCs). Subsequently, candidate EV proteins identified from this analysis were subsequently validated via ELISA in an independent cohort comprising 54 SLE patients and 58 HCs. Furthermore, machine-learning classification was utilized to generate prediction models for SLE diagnosis and LN discrimination. Finally, correlation analysis was applied to evaluate the association between EV-derived inflammatory proteins and clinical parameters.
Results
In the sEV PEA discovery cohort, a total of 49 significantly dysregulated inflammatory proteins with 43 elevated proteins were identified in serum EVs from SLE patients. Two precision prediction models were generated using the random Forest algorithm (RF) for SLE identification and LN discrimination, achieving AUCs of 0.999 and 0.793, respectively. Multiple EV proteins such as CCL23, IL-18R1, SCF and CSF-1 showed a significant correlation with SLE severity parameters including SLEDAI, eGFR and UACR. Furthermore, representative EV proteins including IL-18R1, CCL23 and IFN-γ were further tested in the sEV ELISA validation cohort including 54 SLE patients and 58 HCs.
Conclusions
The present study identified a unique pattern EV-derived inflammatory proteins in patients with SLE, which could serve as novel biomarkers for SLE diagnosis and disease monitoring.