Systemic autoimmune diseases (SADs) are a group of chronic inflammatory conditions with autoimmune aetiology and many common clinical features, leading to a difficult diagnosis or deciding the appropriate treatment. Finding new treatments or applying the existing ones in a more effective way is especially hard in SADs due to the heterogeneity of molecular mechanisms within the same disease class. Based on this premise, the first step towards establishing a precision medicine strategy for SADs is to reclassify these conditions at the molecular level, which might result in a more homogenous stratification in terms of pathological molecular pathways.
It is well known that the interplay of DNA methylation patterns and environmental factors, and between these, is determinant in the regulation of the immune system. This, along with the fact that the genetic contribution to disease is dependent on regulatory variants with very small effects, and the low concordance for autoimmunity in monozygotic twins suggests that epigenetic regulation may play an important role in the development of these diseases. Thus, DNA methylation information might be a valuable marker to reclassify the autoimmune disorders molecularly.
We performed an unsupervised clustering analysis of genome-wide DNA methylation profiling of 437 cases distributed across 7 different clinical entities (rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, primary Sjögren´s syndrome, primary antiphospholipid antibody syndrome, mixed connective tissue disease and undifferentiated connective tissue disease) and 115 healthy individuals. In this analysis we were able to identify new groups of patients composed of the different clinical diagnoses but with common biological features.
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