Drugability Assessment of Hub Genes.

The interaction mechanism between Coronavirus Disease (COVID-19) and rheumatoid arthritis (RA) remains inadequately understood. 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)–mRNA and microRNA (miRNA)–mRNA 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 LGMN and NRGN 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 LGMN and NRGN as potential biomarkers for both diseases. Notably, NRGN may play a significant role as a common biomarker involved in the immune response in both disease states. These findings may open new avenues for the diagnosis and treatment of RA and COVID-19.

新型冠状病毒肺炎（COVID-19）与类风湿关节炎（RA）之间的相互作用机制目前仍未得到充分阐明。为此，本研究旨在阐明RA与COVID-19共病的潜在机制，并筛选关键基因、诊断标志物及相关免疫细胞。本研究对来源于GEO数据库的训练集进行差异分析，在RA和COVID-19的基因芯片及测序数据集中筛选出差异表达基因（differentially expressed genes, DEGs）。通过加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）鉴定关键模块基因，蛋白质相互作用（protein-protein interaction, PPI）网络分析则筛选出核心基因，并通过验证集对上述基因进行验证。采用受试者工作特征（Receiver Operating Characteristic, ROC）曲线评估其临床相关性。基于Cytoscape软件构建转录因子（transcription factor, TF）-mRNA及微小RNA（microRNA, miRNA）-mRNA调控网络以筛选潜在治疗靶点，并通过CIBERSORT算法评估免疫细胞浸润情况。差异表达分析显示，RA数据集中共鉴定出2778个DEGs，COVID-19数据集中共鉴定出12733个DEGs；通过WGCNA共筛选出18个共有基因，提示二者可能存在共同的分子机制。验证分析证实，LGMN与NRGN为与RA和COVID-19共病相关的关键基因，凸显了二者的诊断价值。网络分析鉴定出相关miRNA与TF，富集分析则揭示了关键信号通路。本研究采用CIBERSORT算法评估了RA与COVID-19患者的免疫细胞浸润情况。本研究初步探讨了RA与COVID-19共有的致病机制，筛选出LGMN与NRGN作为二者潜在的生物标志物。值得注意的是，NRGN或可作为参与两种疾病免疫反应的通用生物标志物发挥重要作用。本研究结果可为RA与COVID-19的诊断与治疗提供新的研究方向。