Chen Zi-An, Sun Yu-Feng, Wang Quan-Xu, Ma Hui-Hui, Ma Zhi-Zhao, Yang Chuan-Jie
Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Front Genet. 2021 Jul 9;12:697514. doi: 10.3389/fgene.2021.697514. eCollection 2021.
Ulcerative colitis (UC) is a chronic, complicated, inflammatory disease with an increasing incidence and prevalence worldwide. However, the intrinsic molecular mechanisms underlying the pathogenesis of UC have not yet been fully elucidated. All UC datasets published in the GEO database were analyzed and summarized. Subsequently, the robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between UC patients and controls. Gene functional annotation and PPI network analysis were performed to illustrate the potential functions of the DEGs. Some important functional modules from the protein-protein interaction (PPI) network were identified by molecular complex detection (MCODE), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), and analyses were performed. The results of CytoHubba, a plug for integrated algorithm for biomolecular interaction networks combined with RRA analysis, were used to identify the hub genes. Finally, a mouse model of UC was established by dextran sulfate sodium salt (DSS) solution to verify the expression of hub genes. A total of 6 datasets met the inclusion criteria (GSE38713, GSE59071, GSE73661, GSE75214, GSE87466, GSE92415). The RRA integrated analysis revealed 208 significant DEGs (132 upregulated genes and 76 downregulated genes). After constructing the PPI network by MCODE plug, modules with the top three scores were listed. The CytoHubba app and RRA identified six hub genes: LCN2, CXCL1, MMP3, IDO1, MMP1, and S100A8. We found through enrichment analysis that these functional modules and hub genes were mainly related to cytokine secretion, immune response, and cancer progression. With the mouse model, we found that the expression of all six hub genes in the UC group was higher than that in the control group ( < 0.05). The hub genes analyzed by the RRA method are highly reliable. These findings improve the understanding of the molecular mechanisms in UC pathogenesis.
溃疡性结肠炎（UC）是一种慢性、复杂的炎症性疾病，在全球范围内发病率和患病率都在上升。然而，UC发病机制的内在分子机制尚未完全阐明。对基因表达综合数据库（GEO）中发布的所有UC数据集进行了分析和总结。随后，使用稳健秩聚合（RRA）方法来识别UC患者和对照组之间的差异表达基因（DEG）。进行基因功能注释和蛋白质-蛋白质相互作用（PPI）网络分析，以阐明DEG的潜在功能。通过分子复合物检测（MCODE）、基因本体论（GO）和京都基因与基因组百科全书（KEGG）从蛋白质-蛋白质相互作用（PPI）网络中识别出一些重要的功能模块，并进行了分析。使用CytoHubba（一种结合RRA分析的生物分子相互作用网络综合算法插件）的结果来识别枢纽基因。最后，通过葡聚糖硫酸钠（DSS）溶液建立UC小鼠模型，以验证枢纽基因的表达。共有6个数据集符合纳入标准（GSE38713、GSE59071、GSE73661、GSE75214、GSE87466、GSE92415）。RRA综合分析揭示了208个显著的DEG（132个上调基因和76个下调基因）。通过MCODE插件构建PPI网络后，列出了得分最高的三个模块。CytoHubba应用程序和RRA识别出6个枢纽基因：LCN2、CXCL1、MMP3、IDO1、MMP1和S100A8。我们通过富集分析发现，这些功能模块和枢纽基因主要与细胞因子分泌、免疫反应和癌症进展有关。通过小鼠模型，我们发现UC组中所有6个枢纽基因的表达均高于对照组（<0.05）。通过RRA方法分析的枢纽基因高度可靠。这些发现增进了对UC发病机制中分子机制的理解。
