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

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.

Exploring complement biomarkers in suspected axial spondyloarthritis

“…18 Previous studies have shown elevated complement activation (ie, soluble MAC) in axSpA 25 ; others have found complement proteins (C5, C6 and C9) to be upregulated in proteomic analysis and serve as potential biomarkers for both diagnosis and disease activity in r-axSpA. 26 We have previously identified the complement lectin pathway PRM L-ficolin as a potential axSpA biomarker in a cohort of Danish patients with axSpA. 27 28 The current study investigated the diagnostic potential in a larger cohort of newly diagnosed, ts/bDMARD-naïve axSpA patients compared with clinically relevant controls suffering from other causes of chronic low back pain (cLBP).…”