{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T07:03:48Z","timestamp":1790060628754,"version":"4.0.1"},"reference-count":43,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010767","name":"Innovative Medicines Initiative","doi-asserted-by":"publisher","award":["115565"],"award-info":[{"award-number":["115565"]}],"id":[{"id":"10.13039\/501100010767","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011102","name":"European Union Seventh Framework Programme","doi-asserted-by":"publisher","award":["FP7\/2007\u20132013"],"award-info":[{"award-number":["FP7\/2007\u20132013"]}],"id":[{"id":"10.13039\/100011102","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["acrjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthritis & Rheumatology"],"published-print":{"date-parts":[[2022,10]]},"abstract":"Objective<\/jats:title>Anti\u2010Ro autoantibodies are among the most frequently detected extractable nuclear antigen autoantibodies, mainly associated with primary Sj\u00f6gren's syndrome (SS), systemic lupus erythematosus (SLE), and undifferentiated connective tissue disease (UCTD). This study was undertaken to determine if there is a common signature for all patients expressing anti\u2013Ro 60 autoantibodies regardless of their disease phenotype.<\/jats:p><\/jats:sec>Methods<\/jats:title>Using high\u2010throughput multiomics data collected from the cross\u2010sectional cohort in the PRECISE Systemic Autoimmune Diseases (PRECISESADS) study Innovative Medicines Initiative (IMI) project (genetic, epigenomic, and transcriptomic data, combined with flow cytometry data, multiplexed cytokines, classic serology, and clinical data), we used machine learning to assess the integrated molecular profiling of 520 anti\u2013Ro 60+ patients compared to 511 anti\u2013Ro 60\u2013 patients with primary SS, patients with SLE, and patients with UCTD, and 279 healthy controls.<\/jats:p><\/jats:sec>Results<\/jats:title>The selected clinical features for RNA\u2010Seq, DNA methylation, and genome\u2010wide association study data allowed for a clear distinction between anti\u2013Ro 60+ and anti\u2013Ro 60\u2013 patients. The different features selected using machine learning from the anti\u2013Ro 60+ patients constituted specific signatures when compared to anti\u2013Ro 60\u2013 patients and healthy controls. Remarkably, the transcript Z score of 3 genes (ATP10A<\/jats:italic>, MX1<\/jats:italic>, and PARP14<\/jats:italic>), presenting with overexpression associated with hypomethylation and genetic variation and independently identified using the Boruta algorithm, was clearly higher in anti\u2013Ro 60+ patients compared to anti\u2013Ro 60\u2013 patients regardless of disease type. Our findings demonstrated that these signatures, enriched in interferon\u2010stimulated genes, were also found in anti\u2013Ro 60+ patients with rheumatoid arthritis and those with systemic sclerosis and remained stable over time and were not affected by treatment.<\/jats:p><\/jats:sec>Conclusion<\/jats:title>Anti\u2013Ro 60+ patients present with a specific inflammatory signature regardless of their disease type, suggesting that a dual therapeutic approach targeting both Ro\u2010associated RNAs and anti\u2013Ro 60 autoantibodies should be considered.<\/jats:p><\/jats:sec>","DOI":"10.1002\/art.42243","type":"journal-article","created":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T11:25:07Z","timestamp":1653909907000},"page":"1706-1719","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Machine Learning for the Identification of a Common Signature for Anti\u2013SSA\/Ro 60 Antibody Expression Across Autoimmune Diseases"],"prefix":"10.1002","volume":"74","author":[{"given":"Nathan","family":"Foulquier","sequence":"first","affiliation":[{"name":"B Lymphocytes, Autoimmunity and Immunotherapies laboratory, UMR 1227 Universit\u00e9 de Brest, INSERM Brest France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christelle","family":"Le Dantec","sequence":"additional","affiliation":[{"name":"B Lymphocytes, Autoimmunity and Immunotherapies laboratory, UMR 1227 Universit\u00e9 de Brest, INSERM Brest France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleonore","family":"Bettacchioli","sequence":"additional","affiliation":[{"name":"B Lymphocytes, Autoimmunity and Immunotherapies laboratory, UMR 1227 Universit\u00e9 de Brest, INSERM Brest France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christophe","family":"Jamin","sequence":"additional","affiliation":[{"name":"B Lymphocytes, Autoimmunity and Immunotherapies laboratory, UMR 1227 Universit\u00e9 de Brest, INSERM, and University Hospital of Brest Brest France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta E.","family":"Alarc\u00f3n\u2010Riquelme","sequence":"additional","affiliation":[{"name":"Department of Medical Genomics GENYO Centre for Genomics and Oncological Research Granada Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7287-5541","authenticated-orcid":false,"given":"Jacques\u2010Olivier","family":"Pers","sequence":"additional","affiliation":[{"name":"B Lymphocytes, Autoimmunity and Immunotherapies laboratory, UMR 1227 Universit\u00e9 de Brest, INSERM, and University Hospital of Brest Brest 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