›› 2020, Vol. 40 ›› Issue (2): 194-.doi: 10.3969/j.issn.1674-8115.2020.02.008
• Original article (Basic research) • Previous Articles Next Articles
LI Qian, GAO Jing-ze, LI Yun, SONG Kun, SHEN Qian-cheng
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Published:
Abstract: Objective · To explore the mechanism of esophageal squamous cell carcinoma (ESCC) and its potential targeted drugs, and to provide the theoretical basis for diagnosis and treatment of ESCC. Methods · Two GEO sets GSE38129 and GSE20347 were selected, and differentially expressed genes (DEGs) were screenedR language. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis were conducted for DEGs. The most significant module genes and key genes were analyzedprotein-protein interaction (PPI) network for DEGs. Targeted drug prediction was made for phosphorylase B kinase (PBK). Results · A total of 670 DEGs were identified, consisting of 342 down-regulated genes and 328 up-regulated genes. The enriched functions and pathways of DEGs included extracellular structure organization, extracellular matrix organization, p53 signaling pathway, IL-17 signaling pathway and cell cycle. Twenty key genes were identifiedanalyzing DEGs’ PPI network. The key gene PBK was related to the cell cycle, and the of PBK was up-regulated in the two data sets. The potential drug Compound 1 of PBK was predictedallosteric site detection and compound library virtual screening. Conclusion · The key genes of ESCC can be effectively analyzedbioinformatics. The prediction results of targeted drugs of key gene PBK may provide reference for the targeted therapy of ESCC.
Key words: esophageal squamous cell carcinoma (ESCC), differentially expressed gene (DEG), GO enrichment analysis, KEGG enrichment analysis, protein-protein interaction (PPI) network, targeted drug prediction
LI Qian, GAO Jing-ze, LI Yun, SONG Kun, SHEN Qian-cheng. Bioinformatics analysis of esophageal squamous cell carcinoma genomic chip and prediction of targeted drug[J]. , 2020, 40(2): 194-.
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URL: https://xuebao.shsmu.edu.cn/EN/10.3969/j.issn.1674-8115.2020.02.008
https://xuebao.shsmu.edu.cn/EN/Y2020/V40/I2/194