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Identifying stage-specific biomarkers and pathogenic cellular interactions is crucial for precision medicine.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To comprehensively characterize the transcriptomic landscape and cellular composition of early versus late RA synovium, identify diagnostic biomarkers, and elucidate key pathogenic cell\u2013cell interactions driving disease chronicity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Synovial tissues from 51 RA patients (13 early, 38 late-stage) were analyzed using histopathology, immunohistochemistry, bulk RNA sequencing (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200919),and single-cell RNA sequencing (scRNA-seq,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20096; 3 Early RA vs. 3 Late-stage RA).Machine learning algorithms (LASSO, SVM-RFE, random forest) were employed to identify diagnostic biomarkers. An artificial neural network (ANN) model was constructed and validated. Cell\u2013cell communication analysis was performed using CellChat.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Histopathological analysis revealed significantly increased infiltration of macrophages (CD68\u2009+) and plasma cells (CD138\u2009+) in late-stage RA (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05). RNA sequencing identified 87 differentially expressed genes, with interferon-stimulated genes significantly upregulated. Integrated machine learning identified a minimal three-gene signature (CXCL10, ISG15, IFIH1) as a promising candidate model for RA staging. The three-gene ANN model showed excellent diagnostic performance (AUC\u2009=\u20090.922). Notably, CXCL10 emerged as the most critical component, demonstrating potentially high classification accuracy in this cohort (AUC\u2009=\u20090.767) and standing as the sole independent predictor in multivariable analysis (OR\u2009=\u20097.271,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.022). CXCL10 high expression was strongly associated with M1 macrophage infiltration (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.446,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.005) and enriched in chemokine and JAK-STAT pathways. scRNA-seq revealed macrophages as the primary source of CXCL10, with upstream stimulation from CD8\u2009+\u2009T cells via the IFN-\u03b3-CXCL10-CXCR3 axis. Critically, we identified an expanded TREM2\u2009+\u2009macrophage subset in late RA, which highly expressed APRIL (TNFSF13) and expanded in parallel with plasma cells expressing APRIL receptors (BCMA\u2009+\u2009\/TACI\u2009+). This TREM2\u2009+\u2009macrophage-plasma cell niche may represent a potential pathogenic circuit that could contribute to autoimmune chronicity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Late-stage RA appears to be characterized by a CXCL10-driven inflammatory signature and an expanded TREM2\u2009+\u2009macrophage-plasma cell survival niche. CXCL10 represents a promising candidate biomarker for disease staging that may have mechanistic links to pathogenesis. The IFN-\u03b3-CXCL10-CXCR3 axis and the APRIL-BCMA\/TACI pathway may constitute potential therapeutic targets for refractory RA.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13075-026-03764-3","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T05:24:49Z","timestamp":1770873889000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Integrative multi-omics analysis reveals CXCL10-driven inflammation and TREM2\u2009+\u2009macrophage-plasma cell survival niche as hallmarks of late-stage rheumatoid arthritis"],"prefix":"10.1186","volume":"28","author":[{"given":"Jianbin","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengxia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"issue":"10055","key":"3764_CR1","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1016\/S0140-6736(16)30173-8","volume":"388","author":"JS Smolen","year":"2016","unstructured":"Smolen JS, Aletaha D, McInnes IB. 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