{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T19:25:51Z","timestamp":1791833151210,"version":"4.3.4"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T00:00:00Z","timestamp":1633478400000},"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. Immunol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Rheumatoid arthritis (RA) refers to an autoimmune rheumatic disease that imposes a huge burden on patients and society. Early RA diagnosis is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively. In the present study, the aim was at examining RA\u2019s diagnostic signatures and the effect of immune cell infiltration in this pathology.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Gene Expression Omnibus (GEO) database provided three datasets of gene expressions. Firstly, this study adopted R software for identifying differentially expressed genes (DEGs) and conducting functional correlation analyses. Subsequently, we integrated bioinformatic analysis and machine-learning strategies for screening and determining RA\u2019s diagnostic signatures and further verify by qRT-PCR. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. Moreover, this study employed cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) website for assessing the inflammatory state of RA, and an investigation was conducted on the relationship of diagnostic signatures and infiltrating immune cells.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>On the whole, 54 robust DEGs received the recognition. Lymphocyte-specific protein 1 (LSP1), Granulysin (GNLY), and Mesenchymal homobox 2 (MEOX2) (AUC = 0.955) were regarded as RA\u2019s diagnostic markers and showed their statistically significant difference by qRT-PCR. As indicated from the immune cell infiltration analysis, resting NK cells, neutrophils, activated NK cells, T cells CD8, memory B cells, and M0 macrophages may be involved in the development of RA. Additionally, all diagnostic signatures might be different degrees of correlation with immune cells.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>In conclusion, LSP1, GNLY, and MEOX2 are likely to be available in terms of diagnosing and treating RA, and the infiltration of immune cells mentioned above may critically impact RA development and occurrence.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2021.724934","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T08:43:59Z","timestamp":1633596239000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":56,"title":["Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies"],"prefix":"10.3389","volume":"12","author":[{"given":"Rongguo","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youguang","family":"Zhuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1038\/ncprheum0047","article-title":"Mechanisms of Disease: The Molecular and Cellular Basis of Joint Destruction in Rheumatoid Arthritis","volume":"1","author":"M\u00fcller-Ladner","year":"2005","journal-title":"Nat Clin Pract Rheumatol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.berh.2018.10.005","article-title":"Update on the Epidemiology, Risk Factors, and Disease Outcomes of Rheumatoid Arthritis","volume":"32","author":"van der Woude","year":"2018","journal-title":"Best Pract Res Clin Rheumatol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(16)30173-8","article-title":"Rheumatoid Arthritis","volume":"388","author":"Smolen","year":"2016","journal-title":"Lancet (London England)"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2018.13103","article-title":"Diagnosis and Management of Rheumatoid Arthritis: A Review","volume":"320","author":"Aletaha","year":"2018","journal-title":"Jama"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1136\/ard.2010.138461","article-title":"2010 Rheumatoid Arthritis Classification Criteria: An American College of Rheumatology\/European League Against Rheumatism Collaborative Initiative","volume":"69","author":"Aletaha","year":"2010","journal-title":"Ann Rheum Dis"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2021.692242","article-title":"Pathogenic Role of Circulating Citrullinated Antigens and Anti-Cyclic Monoclonal Citrullinated Peptide Antibodies in Rheumatoid Arthritis","volume":"12","author":"Won","year":"2021","journal-title":"Front Immunol"},{"key":"B7","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1186\/ar2333","article-title":"Cells of the Synovium in Rheumatoid Arthritis. 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