{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T22:29:54Z","timestamp":1792621794183,"version":"4.5.0"},"reference-count":68,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"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":"\n Background<\/jats:title>\n Rheumatoid arthritis (RA) and depression are prevalent diseases that have a negative impact on the quality of life and place a significant economic burden on society. There is increasing evidence that the two diseases are closely related, which could make the disease outcomes worse. In this study, we aimed to identify diagnostic markers and analyzed the therapeutic potential of key genes.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n We assessed the differentially expressed genes (DEGs) specific for RA and Major depressive disorder (MDD) and used weighted gene co-expression network analysis (WGCNA) to identify co-expressed gene modules by obtaining the Gene expression profile data from Gene Expression Omnibus (GEO) database. By using the STRING database, a protein\u2013protein interaction (PPI) network constructed and identified key genes. We also employed two types of machine learning techniques to derive diagnostic markers, which were assessed for their association with immune cells and potential therapeutic effects. Molecular docking and\n in vitro<\/jats:italic>\n experiments were used to validate these analytical results.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n In total, 48 DEGs were identified in RA with comorbid MDD. The PPI network was combined with WGCNA to identify 26 key genes of RA with comorbid MDD. Machine learning-based methods indicated that RA combined with MDD is likely related to six diagnostic markers:\n AURKA<\/jats:italic>\n ,\n BTN3A2<\/jats:italic>\n ,\n CXCL10<\/jats:italic>\n ,\n ERAP2<\/jats:italic>\n ,\n MARCO<\/jats:italic>\n , and\n PLA2G7<\/jats:italic>\n .\n CXCL10<\/jats:italic>\n and\n MARCO<\/jats:italic>\n are closely associated with diverse immune cells in RA. However, apart from\n PLA2G7<\/jats:italic>\n , the expression levels of the other five genes were associated with the composition of the majority of immune cells in MDD. Molecular docking and\n in vitro<\/jats:italic>\n studies have revealed that Aucubin (AU) exerts the therapeutic effect through the downregulation of\n CXCL10<\/jats:italic>\n and\n BTN3A2<\/jats:italic>\n gene expression in PC12 cells.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Our study indicates that six diagnostic markers were the basis of the comorbidity mechanism of RA and MDD and may also be potential therapeutic targets. Further mechanistic studies of the pathogenesis and treatment of RA and MDD may be able to identify new targets using these shared pathways.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fimmu.2023.1007624","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T02:30:00Z","timestamp":1677119400000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics 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