Koth, L. L. | bioRxiv Science

bioRxiv2025

New proteomic biomarkers identified in plasma extracellular vesicles in sarcoidosis: a case-control matched study

BACKGROUNDSarcoidosis is a heterogeneous disease with unknown mechanisms, nonspecific therapies, and multiple etiologies. The role of blood extracellular vesicles (EVs) in the diagnosis and pathogenesis of sarcoidosis remains obscure. AIMS/OBJECTIVES. This study aims to test the hypothesis that the EV proteins in the blood can serve as phenotypic biomarkers of sarcoidosis. METHODS. We combined EV proteomics with machine learning algorithms to identify and prioritize biomarkers, enrich their functions, and cluster networks in case-control matched ACCESS patients. RESULTS. In total, 278 plasma EV proteins were significantly upregulated or downregulated in 40 sarcoidosis patients compared with 40 matched healthy controls. We identified 97 proteins that could serve as biomarkers with an AUC > 0.75. Of these, the AUC was > 0.90 for 13 proteins. 62 differentially expressed EV proteins strongly correlated with 20 clinical variables of severity, chest X-ray findings, and/or laboratory results. Functional annotation and network analysis suggest that these differentially expressed proteins regulate endocytosis, host responses to external stimuli, and transcription processes. Moreover, the top three ranked pathways were clathrin-mediated endocytosis, Hsp90 chaperone cycle, and spliceosome. CONCLUSIONS. This study demonstrates that plasma EV proteins can serve as biomarkers of various clinical phenotypes of the disease. At a Glance CommentaryCurrent Scientific Knowledge on the Subject: Sarcoidosis is a heterogeneous condition affecting multiple organs. The role of blood extracellular vesicles in the diagnosis and pathogenesis of this condition remains unknown. What This Study Adds to the Field: We identified differentially expressed proteins in plasma EVs by combining proteomics and machine learning algorithms. Top-ranked proteins can serve as diagnostic biomarkers and potential mechanisms for the development of sarcoidosis.

molecular biology↗

bioRxiv2025

Plasma Proteomic Profiling Reveals Distinct Signatures of Chest CT Phenotypes in Sarcoidosis

BackgroundSarcoidosis is a granulomatous disease of unknown cause with a highly variable clinical course. The inability to predict progressive inflammation, fibrosis, or both underscores the limited understanding of the underlying molecular mechanisms. ObjectiveWe aimed to identify novel protein signatures associated with distinct pulmonary phenotypes of sarcoidosis, including progressive inflammation, progressive fibrosis, and disease resolution. MethodsWe performed the SomaScan 11K Assay to measure more than 10,000 unique human plasma proteins and compared protein expression between chest CT-defined phenotypes using principal component analysis, differential expression, correlation analysis, and gene set enrichment analysis. ResultsWe identified distinct proteomic signatures that differentiate progressive fibrosis from progressive nodular inflammation in sarcoidosis. Enrichment and differential expression analyses revealed that progressive fibrosis was associated with epithelial-mesenchymal transition pathways, while progressive nodular disease was linked to mTORC1 and MYC signaling, as well as metabolic activation. Additionally, expression of 44 proteins correlated moderately to strongly with thoracic lymph node enlargement, suggesting that lymph node- driven immune activity may be a major source of circulating proteomic signals. ConclusionsThis study leverages a unique longitudinal imaging approach to define extreme pulmonary phenotypes based on serial chest CT scoring, enabling the discovery of proteomic signals linked to distinct trajectories of sarcoidosis progression. Once validated, these findings could inform the development of blood-based biomarkers for disease stratification, monitoring, and therapeutic targeting in sarcoidosis.

immunology↗