Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
Ann Clin Lab Sci. 2021 Mar;51(2):245-254.
Ulcerative colitis (UC) is a chronic, relapsing, and non-specific inflammatory bowel disease. To date, the pathogenesis of UC has not been fully understood. This study aimed to identify crucial genes and related transcription factors in UC by bioinformatic methods.
Datasets GSE75214 and GSE48958 were used to identify the common differentially expressed genes (DEGs). GO and KEGG pathway enrichment analyses were performed using the STRING database. The protein-protein interaction (PPI) network was constructed to screen hub genes using the STRING database and Cytoscape software. The expressions of the identified hub genes were verified using dataset GSE73661, and their correlations with Mayo scores were analyzed using dataset GSE92415. The transcriptional factor (TF) regulatory network of the hubgenes was constructed by Network Analyst.
A total of 147 common DEGs, including 114 up-regulated and 33 down-regulated genes, were screened out, among which CXCL9, TIMP1, PTGS2, ICAM1, CXCL1, MMP9, IL1B, CXCL8, and IL6 were identified as hub genes with high degrees in the PPI network. Correlation analysis showed that the expressions of these hub genes were significantly correlated with Mayo scores in UC patients. Finally, RELA, FLI1, and BACH1 were predicted to be the key TFs regulating these nine hub genes.
This study systematically analyzed the differential gene expression pattern and associated key TFs in UC, which may provide new insights into the pathogenesis and offer opportunities for discovering novel biomarkers and therapeutic targets for UC.
溃疡性结肠炎(UC)是一种慢性、复发性和非特异性炎症性肠病。迄今为止,UC 的发病机制尚未完全阐明。本研究旨在通过生物信息学方法鉴定 UC 中的关键基因和相关转录因子。
使用数据集 GSE75214 和 GSE48958 鉴定共同差异表达基因(DEGs)。使用 STRING 数据库进行 GO 和 KEGG 通路富集分析。使用 STRING 数据库和 Cytoscape 软件构建蛋白质-蛋白质相互作用(PPI)网络,筛选枢纽基因。使用数据集 GSE73661 验证鉴定的枢纽基因的表达,并使用数据集 GSE92415 分析其与 Mayo 评分的相关性。通过 Network Analyst 构建枢纽基因的转录因子(TF)调控网络。
筛选出 147 个共同的 DEGs,包括 114 个上调基因和 33 个下调基因,其中 CXCL9、TIMP1、PTGS2、ICAM1、CXCL1、MMP9、IL1B、CXCL8 和 IL6 被鉴定为 PPI 网络中具有高度数的枢纽基因。相关性分析表明,这些枢纽基因的表达与 UC 患者的 Mayo 评分显著相关。最后,预测 RELA、FLI1 和 BACH1 是调节这 9 个枢纽基因的关键 TF。
本研究系统分析了 UC 中差异基因表达模式及相关关键 TF,可能为发病机制提供新的见解,并为发现 UC 的新型生物标志物和治疗靶点提供机会。