{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:50:06Z","timestamp":1781106606750,"version":"3.54.1"},"reference-count":83,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"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) is an autoimmune disease with chronic presentation, involving symmetric joints and systemic involvement. Ferroptosis is iron-dependent programmed cell death through lipid peroxide accumulation, implicated in inflammatory diseases, including RA. However, its underlying mechanisms and gene-level contributions to RA pathogenesis remain largely unexplored. Therefore, this study emphasizes identifying ferroptosis-related genes associated with RA, evaluating their diagnostic, prognostic, and therapeutic potential, and exploring their role in immune modulation.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>The transcriptomic dataset (GSE89408) from the peripheral blood gene expression was downloaded from the Gene Expression Omnibus (GEO) database. We extracted the differentially expressed genes (DEGs) using R software and the most relevant modules relevant to RA were identified through weighted gene coexpression network analysis (WGCNA). We also identified the differentially expressed ferroptosis genes. The gene ontology and pathways involving the common genes were identified and the protein-protein interaction network was constructed. The hub genes were identified using three machine learning algorithms, least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine (SVM), after which the diagnostic efficiency of the hub genes and the correlation with immune infiltrating cells were predicted.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>A total of 9176 DEGs and a module of 314 genes were obtained which has a significant correlation with RA and 17 genes were selected after the intersection. Using the three machine-learning algorithms, we retrieved 8 hub genes (CISD2, LACTB, PRNP, SAT1, NAMPT, MITD1, SOD2, and FASN) between RA and ferroptosis which showed good diagnostic performance using the ROC curve and nomogram plots. Functional annotation analysis was utilized to inspect the biological functions of the hub genes and the genes showed a substantial association with the immune infiltrating cells.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>NAMPT, CISD2, LACTB, PRNP, SAT1, SOD2, MITD1, and FASN may modulate ferroptosis and RA by influencing immunity, and NAMPT and SAT1 contribute significantly to the diagnosis and treatment of the disease. Future studies focusing on validating these genes in larger cohorts and exploring their therapeutic potential will provide deeper insights.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0333556","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T17:31:16Z","timestamp":1759167076000},"page":"e0333556","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine learning-based transcriptomic analysis identifies NAMPT and SAT1 as potential biomarkers and therapeutic targets in ferroptosis-associated rheumatoid arthritis"],"prefix":"10.1371","volume":"20","author":[{"given":"Devi","family":"Soorya Narayana Sasikumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0015-8460","authenticated-orcid":true,"given":"Vino","family":"Sundararajan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"issue":"10055","key":"pone.0333556.ref001","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.1016\/S0140-6736(16)30173-8","article-title":"Rheumatoid arthritis","volume":"388","author":"JS Smolen","year":"2016","journal-title":"Lancet"},{"key":"pone.0333556.ref002","doi-asserted-by":"crossref","first-page":"1051082","DOI":"10.3389\/fimmu.2022.1051082","article-title":"Systemic complications of rheumatoid arthritis: Focus on pathogenesis and treatment","volume":"13","author":"D Wu","year":"2022","journal-title":"Front 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