基于GEO数据库的胃肠上皮化生相关基因与通路的生物信息学分析

Bioinformatics Analysis of Gastric Intestinal Metaplasia-related Genes and Pathways Based on GEO Database

目的挖掘胃黏膜肠化过程中的差异基因、探索其发病机制并验证差异基因是否在胃癌发生过程中持续发挥作用。方法在美国国立生物技术信息中心(NCBI)的GEO数据库中检索正常胃黏膜肠化表达谱芯片,并通过GEO2R分析得到差异基因,以及在不同芯片数据中均差异表达的关键基因。将差异基因利用生物学信息注释数据库DAVID进行GO生物学过程富集分析和KEGG通路富集分析,探索正常胃组织向肠化转变的相关生物学通路。并通过TCGA数据库分析关键基因在胃癌组织中的差异变化,通过KM plotter分析关键基因与胃癌患者预后的关系。结果检索到3个涉及正常胃黏膜组织发生肠化有关基因芯片,通过差异分析得到在肠化中差异表达的基因共1188个,其中ALDOB、CLCA1、CLDN7、DMBT1、KRT20、MTTP、OLFM4、REG3A和TFF3这9个关键基因在三个芯片中均差异表达。GO富集及KEGG通路分析显示,差异基因主要参与营养物质的消化吸收、蛋白质的水解与合成、物质转运调节等过程。TCGA数据库分析显示,上述9个关键基因在胃癌组织中亦具有差异变化,且通过KM plotter分析证实其与患者预后密切相关。结论本研究获取了在肠化中异常表达的差异基因及其相关通路,并证实关键基因与胃癌患者预后密切相关。

Objective To investigate the related genes and potential mechanism in the course of gastric intestinal metaplasia and confirm whether these differential genes continusously function in the development of gastric cancer. Methods The microarray data of gene expression profile sets containing normal and intestinal gastric mucosa tissue were searched in Gene Expression Omnibus（GEO） database. Differentially expressed genes（DEGs） of each set of gene expression profile and key gene shared by all sets were identified by GEO2 R analysis. Every differentially expressed gene were analyzed to identify the related pathways in gastric intestinal metaplasia by Gene Oncology（GO） and KEGG pathway based on the Database for Annotation Visualization and Integrated Discovery（DAVID） program. The expression distinction of all key genes in gastric cancer tissues and normal gastric tissues was assessed based on The Cancer Genome Altas（TCGA） dataset. And the correlation between key genes and prognosis of gastric cancer patients was calculated by KM plotter analysis. Results A total of three microarray datasets involved in intestinal metaplasia of normal gastric mucosa were identified in GEO database. The GEO2 R analysis targeted 1188 DEGs and nine key genes（ALDOB, CLCA1, CLDN7, DMBT1, KRT20, MTTP, OLFM4, REG3 A and TFF3）. The GO and KEGG analysis implied that the functions of DEGs were closely related with digestion and absorption of nutrition, hydrolysis and synthesis of proteins, and xenobiotic metabolic process. The TCGA dataset analysis showed that all the nine key genes had significantly different expression in gastric cancer tissues when compared with normal gastric tissues, and the KM plotter analysis confirmed their correlation with the prognosis of gastric cancer patients. Conclusion This study identifies the potential differentially expressed genes and related pathways that play important roles in the course of gastric intestinal metaplasia from normal to gastric cancer, and confirms their correlation with clinical prognosis.