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Objective This study employs bioinformatics analysis with the objective of identifying commonly differentially expressed genes (DEGs) in ulcerative colitis (UC) and rheumatoid arthritis (RA), as well as exploring their underlying molecular mechanisms. By doing so, it aims to provide a theoretical basis for investigating the potential associations between these two diseases and developing novel therapeutic strategies. Materials and methods We downloaded multiple gene expression datasets for Rheumatoid Arthritis (RA) and Ulcerative Colitis (UC) from the Gene Expression Omnibus (GEO) database. For RA, GSE77298, GSE12021, and GSE55457 were selected as the training sets, with GSE89408 serving as the validation set. For UC, GSE36807, GSE87473, and GSE92415 were chosen as the training sets, and GSE13367 as the validation set.During data processing, we first merged the RA and UC data from each training set with standardized data, eliminated batch effects, and obtained combined datasets of differentially expressed genes (DEGs). Subsequently, we conducted a cross-analysis of the DEGs from RA and UC to identify commonly up-regulated and down-regulated genes. To gain a deeper understanding of these DEGs, we constructed a protein-protein interaction (PPI) network and identified hub genes.For further analysis of these hub genes, we utilized the GENEMANIA platform to obtain functional annotations and interaction information. Finally, we validated our analysis results using the GSE89408 and GSE13367 datasets. Results After a thorough analysis of the differentially expressed genes in the cells of patients with rheumatoid arthritis (RA) and ulcerative colitis (UC) we found that genes such as CCR7, CD19, CXCL13, CXCR4, and SELL were significantly up-regulated, suggesting their crucial roles in the pathology of both diseases. This discovery not only underscores the importance of these genes as biomarkers for the differential diagnosis of RA and UC, but also highlights key nodes worthy of further validation. In the future, it may be possible to slow or halt disease progression by modulating the expression of these genes. Conclusion The results of this study reveal potential common molecular mechanisms underlying rheumatoid arthritis (RA) and ulcerative colitis (UC). The key target genes CCR7, CD19, CXCL13, CXCR4, and SELL highlight common underlying factors associated with both diseases. Further investigation and exploration of these findings can pave the way for new candidate targets and directions in therapeutic research aimed at treating RA and UC. This study emphasizes the importance of utilizing bioinformatics approaches to uncover the mechanisms of complex diseases, providing a promising pathway for the development of more effective and targeted treatments.研究目的 本研究采用生物信息学分析方法，旨在筛选溃疡性结肠炎（ulcerative colitis, UC）与类风湿关节炎（rheumatoid arthritis, RA）中共有的差异表达基因（differentially expressed genes, DEGs），并探究其潜在的分子机制，以期为阐明两种疾病间的潜在关联、开发新型治疗策略提供理论依据。 材料与方法 本研究从基因表达汇编（Gene Expression Omnibus, GEO）数据库下载了类风湿关节炎与溃疡性结肠炎的多组基因表达数据集。其中，针对类风湿关节炎，选取GSE77298、GSE12021及GSE55457作为训练集，以GSE89408作为验证集；针对溃疡性结肠炎，选取GSE36807、GSE87473及GSE92415作为训练集，以GSE13367作为验证集。 数据处理阶段，首先将各训练集的类风湿关节炎与溃疡性结肠炎数据进行标准化整合，去除批次效应，得到整合后的差异表达基因数据集。随后，对两类疾病的差异表达基因进行交叉分析，以筛选共有的上调及下调基因。为深入解析这些差异表达基因，本研究构建了蛋白质-蛋白质相互作用（protein-protein interaction, PPI）网络，并筛选得到枢纽基因。针对上述枢纽基因，本研究借助GENEMANIA平台获取其功能注释与互作信息。最后，利用GSE89408与GSE13367数据集对分析结果进行验证。 研究结果 通过对类风湿关节炎与溃疡性结肠炎患者细胞中的差异表达基因进行全面分析，本研究发现CCR7、CD19、CXCL13、CXCR4及SELL等基因呈现显著上调状态，提示这些基因在两种疾病的病理进程中发挥关键作用。该发现不仅凸显了上述基因作为类风湿关节炎与溃疡性结肠炎鉴别诊断生物标志物的重要价值，同时也指明了值得进一步验证的关键节点。未来可通过调控这些基因的表达，延缓甚至阻断疾病的进展。 研究结论 本研究结果揭示了类风湿关节炎与溃疡性结肠炎潜在的共有分子机制。关键靶基因CCR7、CD19、CXCL13、CXCR4及SELL，为阐明两种疾病的共有潜在关联因素提供了重要线索。对上述发现的进一步研究与探索，可为类风湿关节炎与溃疡性结肠炎的治疗研究提供全新的候选靶点与研究方向。本研究强调了利用生物信息学方法解析复杂疾病机制的重要意义，为开发更高效、更具靶向性的治疗方案提供了极具前景的路径。