Proteomic Data Analysis for Differential Profiling of the Autoimmune Diseases SLE, RA, SS, and ANCA-Associated Vasculitis
| Autor: | Carl Turesson, Mattias Ohlsson, Cecilia Klint, Thomas Hellmark, Anna Isinger Ekstrand, Elke Theander, Christer Wingren, Anders A. Bengtsson |
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| Rok vydání: | 2020 |
| Předmět: |
Data Analysis
Proteomics 0301 basic medicine Antibody microarray Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis Disease Biochemistry Article Arthritis Rheumatoid 03 medical and health sciences medicine Humans Lupus Erythematosus Systemic autoimmune diseases 030102 biochemistry & molecular biology biology business.industry whole blood Area under the curve General Chemistry medicine.disease Sjogren's Syndrome 030104 developmental biology Rheumatoid arthritis Immunology biology.protein DNA microarray Antibody business antibody microarray Systemic vasculitis |
| Zdroj: | Journal of Proteome Research |
| ISSN: | 1535-3907 1535-3893 |
| DOI: | 10.1021/acs.jproteome.0c00657 |
| Popis: | Early and correct diagnosis of inflammatory rheumatic diseases (IRD) poses a clinical challenge due to the multifaceted nature of symptoms, which also may change over time. The aim of this study was to perform protein expression profiling of four systemic IRDs, systemic lupus erythematosus (SLE), ANCA-associated systemic vasculitis (SV), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), and healthy controls to identify candidate biomarker signatures for differential classification. A total of 316 serum samples collected from patients with SLE, RA, SS, or SV and from healthy controls were analyzed using 394-plex recombinant antibody microarrays. Differential protein expression profiling was examined using Wilcoxon signed rank test, and condensed biomarker panels were identified using advanced bioinformatics and state-of-the art classification algorithms to pinpoint signatures reflecting each disease (raw data set available at https://figshare.com/s/3bd3848a28ef6e7ae9a9.). In this study, we were able to classify the included individual IRDs with high accuracy, as demonstrated by the ROC area under the curve (ROC AUC) values ranging between 0.96 and 0.80. In addition, the groups of IRDs could be separated from healthy controls at an ROC AUC value of 0.94. Disease-specific candidate biomarker signatures and general autoimmune signature were identified, including several deregulated analytes. This study supports the rationale of using multiplexed affinity-based technologies to reflect the biological complexity of autoimmune diseases. A multiplexed approach for decoding multifactorial complex diseases, such as autoimmune diseases, will play a significant role for future diagnostic purposes, essential to prevent severe organ- and tissue-related damage. |
| Databáze: | OpenAIRE |
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