Ulcerative Colitis dataset after normalization.

ObjectiveThis 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 methodsWe 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.ResultsAfter 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.ConclusionThe 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，旨在识别溃疡性结肠炎（Ulcerative Colitis, UC）与类风湿关节炎（Rheumatoid Arthritis, RA）共有的差异表达基因（differentially expressed genes, DEGs），并探究其潜在分子机制，以期为阐明这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：本研究从基因表达综合数据库（Gene Expression Omnibus, GEO）下载类风湿关节炎（RA）与溃疡性结肠炎（UC）的多组基因表达数据集。其中，针对RA数据集，选取GSE77298、GSE12021及GSE55457作为训练集，以GSE89408作为验证集；针对UC数据集，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RA与UC数据进行标准化处理后合并，消除批次效应，得到整合的差异表达基因数据集。随后对RA与UC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，并鉴定枢纽基因（hub genes）。 针对上述枢纽基因的进一步分析中，本研究使用GENEMANIA平台获取其功能注释与相互作用信息。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（RA）与溃疡性结肠炎（UC）患者细胞中的差异表达基因开展全面分析，本研究发现CCR7、CD19、CXCL13、CXCR4及SELL等基因呈显著上调表达，提示其在两种疾病的病理进程中发挥关键作用。该发现不仅彰显了这些基因作为RA与UC鉴别诊断生物标志物的重要价值，同时也指明了需进一步验证的关键靶点。未来可通过调控这些基因的表达，有望延缓甚至阻断疾病进展。 结论：本研究结果揭示了类风湿关节炎（RA）与溃疡性结肠炎（UC）潜在的共同分子机制。核心靶基因CCR7、CD19、CXCL13、CXCR4及SELL，凸显了与两种疾病相关的共同潜在致病因素。对上述发现开展深入研究，可为RA与UC的治疗研究提供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性，为开发更高效、更具靶向性的治疗手段提供了极具前景的路径。