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Published in: Arthritis Research & Therapy 1/2023

Open Access 01-12-2023 | Ankylosing Spondylitis | Research

Quantitative proteomic screening uncovers candidate diagnostic and monitoring serum biomarkers of ankylosing spondylitis

Authors: Mark Hwang, Shervin Assassi, Jim Zheng, Jessica Castillo, Reyna Chavez, Kamala Vanarsa, Chandra Mohan, John Reveille

Published in: Arthritis Research & Therapy | Issue 1/2023

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Abstract

Background

We sought to discover serum biomarkers of ankylosing spondylitis (AS) for diagnosis and monitoring disease activity.

Methods

We studied biologic-treatment-naïve AS and healthy control (HC) patients’ sera. Eighty samples matched by age, gender, and race (1:1:1 ratio) for AS patients with active disease, inactive disease, and HC were analyzed with SOMAscan™, an aptamer-based discovery platform. T-tests tests were performed for high/low-disease activity AS patients versus HCs (diagnosis) and high versus low disease activity (Monitoring) in a 2:1 and 1:1 ratio, respectively, to identify differentially expressed proteins (DEPs).
We used the Cytoscape Molecular Complex Detection (MCODE) plugin to find clusters in protein–protein interaction networks and Ingenuity Pathway Analysis (IPA) for upstream regulators. Lasso regression analysis was performed for diagnosis.

Results

Of the 1317 proteins detected in our diagnosis and monitoring analyses, 367 and 167 (317 and 59, FDR-corrected q < .05) DEPs, respectively, were detected. MCODE identified complement, IL-10 signaling, and immune/interleukin signaling as the top 3 diagnosis PPI clusters. Complement, extracellular matrix organization/proteoglycans, and MAPK/RAS signaling were the top 3 monitoring PPI clusters. IPA showed interleukin 23/17 (interleukin 22, interleukin 23A), TNF (TNF receptor-associated factor 3), cGAS-STING (cyclic GMP-AMP synthase, Stimulator of Interferon Gene 1), and Jak/Stat (Signal transducer and activator of transcription 1), signaling in predicted upstream regulators. Lasso regression identified a Diagnostic 13-protein model predictive of AS. This model had a sensitivity of 0.75, specificity of 0.90, a kappa of 0.59, and overall accuracy of 0.80 (95% CI: 0.61–0.92). The AS vs HC ROC curve was 0.79 (95% CI: 0.61–0.96).

Conclusion

We identified multiple candidate AS diagnostic and disease activity monitoring serum biomarkers using a comprehensive proteomic screen. Enrichment analysis identified key pathways in AS diagnosis and monitoring. Lasso regression identified a multi-protein panel with modest predictive ability.
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Metadata
Title
Quantitative proteomic screening uncovers candidate diagnostic and monitoring serum biomarkers of ankylosing spondylitis
Authors
Mark Hwang
Shervin Assassi
Jim Zheng
Jessica Castillo
Reyna Chavez
Kamala Vanarsa
Chandra Mohan
John Reveille
Publication date
01-12-2023
Publisher
BioMed Central
Published in
Arthritis Research & Therapy / Issue 1/2023
Electronic ISSN: 1478-6362
DOI
https://doi.org/10.1186/s13075-023-03044-4

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