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Consequently, this study sought to elucidate the potential mechanisms underlying the comorbidity between RA and COVID-19, as well as to identify key genes, diagnostic markers, and associated immune cells. Differential analysis of the training set, derived from the GEO database, identified differentially expressed genes (DEGs) in the RA and COVID-19 gene chip and sequencing datasets. Weighted Gene Co-expression Network Analysis (WGCNA) identified key modular genes, while protein-protein interaction (PPI) network analysis revealed hub genes, which were validated by the validation set. Receiver Operating Characteristic (ROC) curves were used to assess clinical relevance. Cytoscape-based transcription factor (TF)\u2013mRNA and microRNA (miRNA)\u2013mRNA regulatory networks were used to identify potential therapeutic targets, and immune cell infiltration was evaluated using the CIBERSORT algorithm. Differential expression analysis identified 2,778 DEGs in RA and 12,733 in COVID-19, with WGCNA identifying 18 shared genes, suggesting possible common molecular mechanisms. Validation analysis confirmed <jats:italic>LGMN<\/jats:italic> and <jats:italic>NRGN<\/jats:italic> as key genes associated with RA and COVID-19 comorbidity, highlighting their diagnostic significance. Network analysis identified related miRNAs and TFs, and enrichment analysis revealed the critical signaling pathways. Immune cell infiltration in patients with RA and COVID-19 was assessed using the CIBERSORT algorithm. This study preliminarily explored the shared pathogenic mechanisms between RA and COVID-19, identifying <jats:italic>LGMN<\/jats:italic> and <jats:italic>NRGN<\/jats:italic> as potential biomarkers for both diseases. Notably, <jats:italic>NRGN<\/jats:italic> may play a significant role as a common biomarker involved in the immune response in both disease states. 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