Serum N‐glycan profiling as a diagnostic biomarker for the identification and assessment of psoriasis

• Published in: Journal of clinical laboratory analysis Vol. 35; no. 4; pp. e23711 - n/a
• Main Authors: Zou, Chengyun, Huang, Chenjun, Yan, Li, Li, Xin, Xing, Meng, Li, Bin, Gao, Chunfang, Wang, Haiying
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.04.2021; John Wiley and Sons Inc
• Subjects: Age; biomarker; Biomarkers; Capillary electrophoresis; diagnostic model; Glycosylation; Hospitals; Laboratories; Neutrophils; Nucleotide sequence; N‐glycan profiling; Patients; Proteins; Psoriasis; Reagents; Standard deviation; Statistical analysis; Uric acid; glycosylation; N-glycan profiling; biomarker; diagnostic model; psoriasis
• ISSN: 0887-8013; 1098-2825; 1098-2825
• DOI: 10.1002/jcla.23711

Background Glycosylation is an important post‐translational modification of protein. The change in glycosylation is involved in the occurrence and development of various diseases, and this study verified that N‐glycan markers might be a diagnostic marker in psoriasis. Methods A total of 76 psoriasis patients were recruited. We used Psoriasis Area Severity Index (PASI) scores to evaluate the state of psoriasis, 41 of whom were divided into three subgroups: mild, moderate, and severe. At the same time, 76 healthy subjects were enrolled as a control group. We used DNA sequencer–assisted fluorophore‐assisted carbohydrate electrophoresis (DSA‐FACE) to analyze serum N‐glycan profiling. Results Compared with the healthy controls, the relative abundance of structures in peaks 5(NA2), 9(NA3Fb), 11(NA4), and 12(NA4Fb) was elevated (p < .05), while that in peaks 3(NG1A2F), 4(NG1A2F), 6(NA2F), and 7(NA2FB) was decreased (p < .05) in the psoriasis group. The abundance of peak 5 (NA2) increased gradually with the aggravation of disease severity though there was no statistically significant, was probably correlated with the disease severity. The best area under the receiver operating characteristic (ROC) curve (AUC) of the logistic regression model (PglycoA) to diagnose psoriasis was 0.867, with a sensitivity of 72.37%, a specificity of 85.53%, a positive predictive value(PPV) of 83.33%, a negative predictive value(NPV) of 75.58%, and an accuracy of 78.95%. Conclusions Our study indicated that the N‐glycan–based diagnostic model would be a new, valuable, and noninvasive alternative for diagnosing psoriasis. Furthermore, the characteristic distinctive N‐glycan marker might be correlated with the severity gradation of the psoriasis disease. We used DNA sequencer–assisted fluorophore‐assisted carbohydrate electrophoresis (DSA‐FACE) to analyzed serum N‐glycan profiling. Characteristic changes were revealed in the serum N‐glycome of psoriasis, and we established a diagnostic model based on N‐glycan profiling. We also found that the relative abundance of structure in peak 5 (NA2) increased gradually with the increase in disease severity. Our study indicated that the N‐glycan–based diagnostic models would be a new, valuable, and noninvasive alternative for diagnosing psoriasis. Furthermore, the characteristic distinctive N‐glycan marker might be correlated with the severity gradation of the psoriasis disease.