{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T22:53:42Z","timestamp":1791759222354,"version":"4.3.4"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82374402"],"award-info":[{"award-number":["82374402"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We sought to leverage machine learning algorithms to identify the complex clinical and serological signature of anti-centromere antibody (ACA) positivity in Sj\u00f6gren\u2019s syndrome (SS) patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This multicenter study analyzed clinical data from a cohort of 616 patients diagnosed SS, comprising 81 ACA-positive and 535 ACA-negative cases. To ensure robust model development, we randomly partitioned the dataset into training and validation subsets in a 7:3 ratio. We implemented and compared six machine learning models after identifying optimal predictors using the LASSO regression. We mainly evaluate the performance of the model through the AUC and a series of comprehensive indicators. To ensure clinical interpretability, we also employed the SHAP analysis method to quantify the influence of each feature on the model\u2019s outcome.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among the evaluated models, GBDT exhibited superior predictive efficacy. The model achieved an AUC value of 0.812 in the training set and maintained a robust AUC of 0.811 (95% CI: 0.699\u20130.906) in the validation cohort. At the same time, the model has the highest sensitivity (0.750 in the validation test). The SHAP analysis revealed that the top predictors influencing the ACA-positive profile included a series of serological markers (anti-SSA\/Ro52, anti-SSA\/Ro60, anti-AMA-M2, anti-SSB, and IgM), demographic factors (age), and Raynaud\u2019s phenomenon (RP). Furthermore, SHAP interactions captured non-linear synergies, such as the predictive contribution of RP is significantly potentiated by advancing age, and the amplified predictive value of anti-AMA-M2 under lower IgM levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      Our machine learning approach effectively structures and quantifies clinical and serological associations, capturing a complex predictive profile for the SS-ACA\n                      <jats:sup>+<\/jats:sup>\n                      subgroup. These findings highlight the value of ML in identifying non-linear patterns within clinical variables, providing a robust quantitative framework for future prospective evaluations.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1803065","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:36:26Z","timestamp":1776922586000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Identifying the clinical signature of anti-centromere antibody-positive Sj\u00f6gren\u2019s syndrome: a machine learning-based analysis of a multicenter cohort"],"prefix":"10.3389","volume":"17","author":[{"given":"Wenlong","family":"Zhu","sequence":"first","affiliation":[{"name":"The Second Clinical Medical College, Zhejiang Chinese Medical University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Fang","sequence":"additional","affiliation":[{"name":"The Second Clinical Medical College, Zhejiang Chinese Medical University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinchao","family":"Zhu","sequence":"additional","affiliation":[{"name":"The Second Clinical Medical College, Zhejiang Chinese Medical University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Yang","sequence":"additional","affiliation":[{"name":"The Second Clinical Medical College, Zhejiang Chinese Medical University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayao","family":"Wang","sequence":"additional","affiliation":[{"name":"The Second Clinical Medical College, Zhejiang Chinese Medical University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinchang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Rheumatology, The Second Affiliated Hospital of Zhejiang Chinese Medical University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.e3821","article-title":"Primary sjogren syndrome","volume":"344","author":"Ramos-Casals","year":"2012","journal-title":"BMJ"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1136\/annrheumdis-2001.306","article-title":"Raynaud\u2019s phenomenon in primary sj\u00f6gren\u2019s syndrome. 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