A distinguishing profile of chemokines, cytokines and biomarkers in the saliva of children with Sjögren’s syndrome

Abstract

Objectives Sjögren’s syndrome is an autoimmune disease most commonly diagnosed in adults but can occur in children. Our objective was to assess the presence of chemokines, cytokines, and biomarkers (CCBMs) in saliva from these children that were associated with lymphocyte and mononuclear cell functions. Methods Saliva was collected from 11 children diagnosed with Sjögren’s syndrome prior to age 18 years and 16 normal healthy children. 105 CCBMs were detected in multiplex microparticle-based immunoassays. ANOVA and t test (0.05 level) were used to detect differences. Ingenuity Pathway Analysis (IPA) was used to assess whether elevated CCBMs were in annotations associated with immune system diseases and select leukocyte activities and functions. Machine learning methods were used to evaluate the predictive power of these CCBMs for Sjögren’s syndrome and were measured by receiver operating characteristic (ROC) curve and area under curve (AUC). Results 40.9% (43/105) CCBMs were different (p < 0.05) in children with Sjögren’s syndrome compared to the healthy study controls and could differentiate the two groups (p < 0.05). Elevated CCBMs in IPA annotations were associated with autoimmune diseases and with leukocyte chemotaxis, migration, proliferation, and regulation of T-cell activation. The best AUC value in ROC analysis was 0.93, indicating that there are small numbers of CCBMs that may be useful for diagnosis of Sjögren’s syndrome. Conclusion While 35/43 CCBMs have been previously reported in Sjögren’s syndrome, 8 CCBMs had not. Additional studies focusing on these CCBMs may provide further insight into disease pathogenesis and may contribute to diagnosis of Sjögren’s syndrome in children.

Characterization of Type I Interferon-Associated Chemokines and Cytokines in Lacrimal Glands of Nonobese Diabetic Mice

“…Among the most highly DE genes and the highest DE cytokine gene was Il21, which encodes IL-21, a pleiotropic cytokine with varied roles in innate and adaptive immunity and implicated in autoimmunity [22,35]. Specifically in SD, IL-21 was elevated in both serum [36,37] and labial minor salivary gland biopsy specimens of individuals with SD compared to controls [37] and in saliva of children with SD compared to controls [38]. A role for IL-21 in ocular inflammation in SD was demonstrated by elevation of IL-21 in tears and increased expression of IL21 in conjunctival epithelial cells of individuals with SD compared to controls [39].…”

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

“…Utilizing AI algorithms with salivary biomarkers to generate models that can be validated and applied potentially in clinical practice is more common than its use for biomarker discovery (Arias-Bujanda et al, 2020;Banavar et al, 2021;Bostanci et al, 2018;Carnielli et al, 2018;da Costa et al, 2022;de Dumast et al, 2018;Eriksson et al, 2022;Gomez Hernandez et al, 2021;Grier et al, 2021;Kim et al, 2021;Kistenev et al, 2018;Koller et al, 2021;Koopaie et al, 2021;Kouznetsova et al, 2021;Lee et al, 2021;Liu, Tong, et al, 2021;Lyashenko et al, 2020;Monedeiro et al, 2021;Nakano et al, 2014Nakano et al, , 2018Pang et al, 2021;Schulte et al, 2020;Shoukri et al, 2019;Song et al, 2020;Sonis et al, 2013;Tamaki et al, 2009;Winck et al, 2015;Wu et al, 2021;Zhang et al, 2021;Zhou et al, 2021;Zlotogorski-Hurvitz et al, 2019) AI-based biomarker platforms constructed during biomarker discovery may be executed for biomarker validation on the premise that each additional saliva sample would be subjected to similar large-scale analysis and used as input for the same models (Adeoye, Wan, et al, 2022). While this may not be cost-effective, feasible, or encourage validation, promising biomarkers selected using conventional statistical approaches or AI-based exploratory analysis (in biomarker discovery) may be used to develop new models for biomarker validation and potential clinical application (Koopaie et al, 2021;Tamaki et al, 2009).…”

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

“…The predictive power of chemokines, cytokines, and biomarkers in saliva from pSS patients (n = 11) was evaluated using eight different classifiers (i.e., SVM, RF, NB, Gaussian process, AdaBoost, LR) [80]. From an initial set of 105 predictors and after applying five feature selection methods, 43 predictors remained.…”

“…The study of differences in terms of biomarker composition between children and adults is a topic that could elucidate the pathophysiological mechanisms of diseases such as pSS. For instance, authors in [131] hypothesised that the saliva of pSS children would have chemokines, cytokines, and biomarkers similar to those of pSS adults. Under this hypothesis, the predictive power of chemokines, cytokines, and biomarkers in saliva from pSS patients (n = 11) and healthy controls (n = 16) was evaluated using eight different classifiers (i.e., SVM, RF, NB, Gaussian process, AdaBoost, LR).…”