{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:04:10Z","timestamp":1784203450307,"version":"3.55.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"\n Background<\/jats:title>\n Rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are systemic autoimmune diseases characterized by chronic inflammation, immune dysregulation, and heterogeneous clinical manifestations. Despite differences in target organs, both share overlapping pathogenic mechanisms, such as cytokine imbalance and autoantibody production. However, integrated evaluation of cytokine signatures and autoantibody profiles remains limited, limiting their translational application in precision medicine.<\/jats:p>\n <\/jats:sec>\n \n Aim<\/jats:title>\n This study aimed to characterize immune network dysregulation in RA and SLE using an integrated cross-disease analytical framework combining cytokine signatures, autoantibody profiles, network-based immune analysis, and immune sub-phenotype stratification, and to evaluate their translational relevance for precision medicine.<\/jats:p>\n <\/jats:sec>\n \n Methodology<\/jats:title>\n \n This retrospective observational study included 875 patients (RA:\n n<\/jats:italic>\n \u202f=\u202f450; SLE:\n n<\/jats:italic>\n \u202f=\u202f425) diagnosed between January 2021 and December 2025. Cytokine levels were measured using multiplex immunoassays and normalized to z-scores, while validated assays were used to determine autoantibody profiles. Correlation analysis, multivariate regression, principal component analysis (PCA), and hierarchical clustering were applied to identify immune interactions and sub-phenotypes. Statistical analyses were performed using SPSS and R, with adjustment for confounders and multiple testing.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Distinct, disease-specific immune signatures were observed. Pro-inflammatory cytokines, particularly IL-6 and IL-17, were significantly elevated in RA and were positively correlated with disease activity (\n r<\/jats:italic>\n \u202f=\u202f0.48 and\n r<\/jats:italic>\n \u202f=\u202f0.42, respectively). In SLE, interferon-related and regulatory cytokines, including IFN-\n \u03b3<\/jats:italic>\n and MCP-1, showed stronger associations with disease activity and organ involvement (\n r<\/jats:italic>\n \u202f=\u202f0.51 and\n r<\/jats:italic>\n \u202f=\u202f0.52). Autoantibody profiles differed: RF and anti-CCP were predominant in RA, whereas SLE exhibited greater autoantibody diversity and multi-positivity (61.5%). Network analysis revealed IL-6\u2013CRP interactions in RA and IFN-\n \u03b3<\/jats:italic>\n \u2013autoantibody burden interactions in SLE. PCA identified IL-6, IFN-\u03b3, MCP-1, and autoantibody burden as key drivers of immune variability. Clustering defined five immune sub-phenotypes with distinct clinical outcomes, such as high-inflammatory RA and high-risk SLE flare clusters. Cytokines emerged as independent predictors of disease activity, whereas autoantibody burden showed limited predictive value (\n p<\/jats:italic>\n \u202f=\u202f0.08).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Integrated immune profiling highlights both shared and distinct immune dysregulation in RA and SLE. Cytokine signatures outperform autoantibody burden in predicting disease activity and outcomes, supporting their role as key biomarkers for patient stratification and precision medicine.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fmed.2026.1841343","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:10:20Z","timestamp":1784200220000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Immune network dysregulation in rheumatoid arthritis and systemic lupus erythematosus: cytokine signatures, autoantibody profiles, and implications for precision medicine"],"prefix":"10.3389","volume":"13","author":[{"given":"Benjuan","family":"Wu","sequence":"first","affiliation":[{"name":"Rheumatology and Immunology Department, Tianjin First Central Hospital, Naikai","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liya","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Second Department of General Medicine, Tianjin First Central Hospital, Naikai","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuxi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Surgery, Beichen Hospital of Nankai University","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"23475","DOI":"10.1038\/s41598-024-72564-z","article-title":"Cytokine profiles and their correlation with clinical and blood parameters in rheumatoid arthritis and systemic lupus erythematosus","volume":"14","author":"Duarte-Delgado","year":"2024","journal-title":"Sci 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