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Rheumatol
Autoimmune
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Rheumatology & Autoimmunity
This special review is from the inaugural issue of Rheumatology & Autoimmunity in 2021, and the author is the associate editor of RAI and the team of Professor Yu Di of the University of Queensland, Australia.
Classification of SLE patients by omics data and artificial intelligence
Brief introduction
Systemic lupus erythematosus (SLE) is a systemic autoimmune disease that is highly heterogeneous due to its complex pathogenesis and diverse manifestations. Stratification of patients with treatment and prognosis is a major challenge in managing SLE. Routine biomarkers for disease diagnosis and activity assessment have limited efficacy on immunopathogenesis and response to treatment.
Advances in "omics" technologies, including genomics, transcriptomics, proteomics, and metabolomics, have provided unprecedented opportunities to evaluate individual immunopathology in SLE patients. In fact, genomic studies have revealed that some potentially immunodysregulated SLE patients carry one or more functional single nucleotide polymorphisms (SNPs), while transcriptomic studies have revealed that a subset of SLE patients exhibit different features of type I interferon pathway activation or B-cell aberrant differentiation into plasma cells.
This review summarises the latest findings on the use of omics techniques to understand the heterogeneity of SLE. In addition, the authors propose that the application of artificial intelligence can further strengthen the analysis of omics big data. The combination of new technologies and innovative assays can lead to breakthroughs in SLE stratification to better monitor disease activity and more precisely design treatment regimens, not only for conventional immunosuppression, but also for new immunotherapies targeting B-cell activating factor (BAFF), interferon type I, and interleukin-2 (IL-2).
FIGURE 1 Simplified depiction of Immunopathogenesis of SLE.
FIGURE 2 Conventional clinical features and immunological biomarkers to classify SLE patients.
FIGURE 3 A proposed new classification of SLE patients by omics data and artificial intelligence.
FIGURE 4 Basics for dimensionality reduction.
Welcome to use
How to cite this article: Puri P, Jiang SH, Yang Y, Mackay F, Yu D. Understand SLE heterogeneity in the era of omics, big data, and artificial intelligence. Rheumatol & Autoimmun. 2021; 1:40‐51. doi: 10.1002/rai2.12010