| Summary: | The importance of N6-methyladenine (m6A) in mRNA metabolism, physiology, pathology and other life processes is well recognized. However, the exact role of m6A regulators in primary Sjögren's syndrome (PSS) remains unclear. In this study, we used bioinformatics and machine learning random forest approach to screen eight key m6A regulators from the Gene Expression Omnibus GSE7451, GSE40611 and GSE84844 datasets. An accurate nomogram model for predicting PSS risk was established based on these regulators. And using consensus clustering, patients diagnosed with PSS were classified into two different m6A patterns. We found that patients in group B had higher m6A scores compared to those in group A: furthermore, both groups were closely related to immunity and possibly to other diseases. These results emphasise the important role of m6A regulators in the pathogenesis of PSS. Our study of m6A patterns may inform future immunotherapy strategies for PSS.
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