{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T17:35:27Z","timestamp":1780508127359,"version":"3.54.1"},"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Science Foundation of Hubei","award":["Nos. 2023AFD155"],"award-info":[{"award-number":["Nos. 2023AFD155"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Rheumatoid arthritis (RA) and ulcerative colitis (UC) are chronic inflammatory diseases with shared immune pathologies but limited common diagnostic biomarkers, which hinders the development of targeted therapies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Public gene expression datasets were analyzed to identify differentially expressed genes (DEGs) common to both RA and UC. Functional enrichment and immune infiltration analyses revealed dysregulated pathways. A comprehensive machine learning framework that incorporated 12 algorithms and cross-validation was applied to screen for robust diagnostic biomarkers. Further, RA- and UC-related molecular subtypes were delineated, and the relationship between these shared biomarkers and immune infiltration characteristics was explored. Key findings were validated using single-cell RNA sequencing (scRNA-seq) of UC tissue to localize gene expression and qRT-PCR in cell models mimicking RA and UC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Analysis identified 19 shared DEGs, with functional enrichment analysis highlighting IL-17 signaling. Machine learning prioritized four key biomarkers (DUOX2, IDO1, NPY1R, SELL) with high diagnostic performance. scRNA-seq localized these genes predominantly to a pro-inflammatory \u201cMacrophage-High\u201d subpopulation and revealed VEGF-mediated crosstalk with endothelial cells. qRT-PCR confirmed significant expression changes of IDO1 and NPY1R in both RA-like and UC-like inflammation models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This integrative approach identifies DUOX2, IDO1, NPY1R, and SELL as shared RA-UC biomarkers potentially linked to macrophage-driven inflammation and VEGF signaling. These findings provide insights into the common pathogenesis and potential targets for dual-disease diagnostics and therapeutics.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0336243","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T18:25:36Z","timestamp":1762799136000},"page":"e0336243","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrative machine learning and multi-omics framework identifies shared biomarkers for rheumatoid arthritis and ulcerative 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