|作者：||Yang Liang-Jun, Li Jing, Zhang Chi, Li Yue-Jun|
1Guangzhou University of Chinese Medicine, Guangzhou 510405, China
2The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha 410007, China
3Zhuzhou Hospital of Traditional Chinese Medicine, Zhuzhou 412008, China.
|刊名：||TMR Cancer, 2020, Vol.3 (1), pp.32-43|
|来源数据库：||TMR publishing group|
|原始语种摘要：||HighlightsThis study explored possible changes in gene expression during the transition from precancerous lesions to gastric cancer. Until now, the underlining mechanisms of precancerous lesions to cancer transition remain unclear. Bioinformatics analysis may find out genes that are differentially expressed in this process, and provide ideas for studying the transformation of precancerous lesions into cancer.(#br)Abstract Objective:(#br) This work aimed to illuminate the potential key genes and pathways in GC tumorigenesis based on bioinformatics analysis. Methods:(#br) The differentially expressed genes (DEGs) between GPL tissue samples and GC tissue samples were investigated using the GSE55696 and GSE87666 microarray data from the Gene Expression Omnibus (GEO) database. DEGs were... identified by an empirical Bayes method based on the Limma R package. Then, KEGG and GO enrichment analyses of DEGs were performed followed by protein-protein interaction (PPI) network construction. Finally, the overall survival (OS) analysis of key genes was performed by the Kaplan-Meier plotter online tool. Results:(#br) A total of 250 DEGs were obtained, of which 216 were up-regulated and 34 were down-regulated. KEGG pathways analysis showed that the up-regulated DEGs were enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, metabolic pathways, PI3K-Akt signaling pathway, NF-kappa B signaling pathway, and other signaling pathways about cancer, while no down-regulated pathways were enriched. A PPI network of DEGs was constructed with 117 nodes and 660 edges, and 20 genes were selected as hub genes owing to high degrees in the network. According to the Kaplan-Meier analysis, 6 out of 20 hub genes including CCR7, FPR1, C3, CXCR5, GNB4, and PPBP with high mRNA expression were associated with poor OS for GC patients. Conclusion:(#br) The results of this study provide possible factors for the occurrence of GC, and the identification of the genes and pathways associated with the progression from GPL to GC provides valuable data for investigating the pathogenesis in future studies.(#br)|