作者：蒙秀东1, 李昕1, 袁旭1, 苗苗1, 张月林1, 袁红霞2

单位：1.天津中医药大学 研究生院, 天津 301617;  2.天津中医药大学 管理学院, 天津 301617

Authors:  Meng Xiudong, Li Xin, Yuan Xu, Miao Miao, Zhang Yuelin, Yuan Hongxia

Unit:  Graduate College Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China

摘要：

目的 利用生物信息学方法探讨胃癌前病变的差异基因及潜在治疗靶点。方法 使用GEO2R挖掘GEO数据库中的基因芯片数据(GSE55696和GSE130823)筛选差异表达基因;然后使用DAVID数据库对DEGs进行GO和KEGG途径富集分析;使用STRING数据库构建DEGs的PPI网络,并将其导入Cytoscape进行网络分析和模块分析;使用Kaplan-Meier、GEPIA和HPA数据库分析核心DEG。结果 共筛选出273个差异表达基因,其中74个上调基因和199个下调基因。这些基因主要参与胃酸分泌、白细胞跨内皮迁移、甘油酯代谢、细胞黏附分子、PI3K-Akt信号通路和糖酵解/糖异生通路; 生存分析发现4个危险基因(EFNA3、PPP2R3A、THBS2、EPOR)和5个保护基因(CCND2、MYB、OSM、ITGB8和PIK3R3),并且发现EFNA3、ITGB8、PIK3R3、PCK1和THBS2在细胞质/膜组织中过度表达。结论 EFNA3、PPP2R3A、THBS2和EPOR可能是PLGC发病的危险基因,CCND2、MYB、OSM、ITGB8和PIK3R3可能是PLGC发病的保护基因。

关键词：  胃癌前病变; 蛋白质组学; 生物信息学分析; 生物标志物

Abstract：

Objective  To explore the differential genes of precancerous lesions of gastric cancer (PLGC) patients and predict the potential therapeutic targets of PLGC by bioinformatics analysis. Methods Two microarray data (GSE55696 and GSE130823) GEO database were selected to screen the differentially expressed genes (DEGs) by using GEO2R online analysis tool, the cut-off criteria of DEGs were |log2 FC| >1 and adjusted value <0.05; Then the GO and KEGG pathway enrichment analysis of DEGs were performed using the DAVID database. The PPI network of DEGs was constructed using the STRING database, which was imported into Cytoscape for network analysis and module analysis; and core DEGs were analyzed using Kaplan Meier Plotter, GEPIA and HPA databases. Results A total of 273 differentially expressed genes were screened including 74 up-regulated genes and 199 down-regulated genes. These genes were mainly involved in gastric acid secretion, leukocyte trans endothelial migration, glyceride metabolism, cell adhesion molecules, PI3K-Akt signaling pathway and glycolysis/ gluconeogenesis pathway, it was found that 4 risky genes (EFNA3, PPP2R3A, THBS2), EPOR and 5 protected genes (CCND2, MYB, OSM, ITGB8 and PIK3R3). The overexpression of EFNA3, ITGB8, PIK3R3, PCK1 and THBS2 was found in cytoplasmic/membranous. Conclusion EFNA3, PPP2R3A, THBS2 and EPOR in PI3K-Akt pathway may be risk genes for PLGC, while CCND2, MYB, OSM, ITGB8 and PIK3R3 may be protective genes for PLGC.

Key Words:  Precancerous lesions of gastric cancer; Proteomics; Bioinformatics analysis; Biomarkers