Identification And Analysis Of Key Genes Driving Gastric Cancer Via Bioinformatics

Objective: This aim of this study was to use bioinformatic analyses to identify key genes and pathways driving gastric cancer(GC).Methods: The gene expression profiles,from human gastric tissue samples were downloaded from the Gene Expression Omnibus(GSE)29272 dataset.These data revealed284 differentially-expressed genes(DEGs)that included a group upregulated in cancer tissues(n = 142)and another group that were down-regulated in cancer tissues.(n = 142).These DEGs were identified using the GEO2 R.We used multiple online analysis tools including,Gene Ontology(GO),Kyoto Encyclopedia of Genes and Genomes(KEGG),protein-protein interaction(PPI)networks,gene expression profiling interactive analysis(GEPIA),and the c Bio Cancer Genomics Portal(c Bioportal)database.Next,we identified the most significantly differentially-expressed genes using the Kaplan-Meier plotter(KMplotter)database.Multiple bioinformatic platforms were used to identify candidate prognostic marker genes.We then analyzed freshly frozen gastric cancer tissues for the expression of these marker genes to validate the informatics findings.Results: We identified three DEGs(BGN、ATP4A、COL6A3)related to overall survival from our analyses of the GEO data.Next,we analyzed these three DEGs in GEPIA and the c Bioportal database and found that the biglycan(BGN)gene was related to invasion and metastases of gastric cancers.This finding of differential gene expression was confirmed in a separate laboratory analysis of normal and gastric cancer tissues.In this analysis we found that high levels of BGN expression were correlated with GC clinicopathologic characteristics,including microvascular tumor thrombus(p = 0.018),lymph node metastases(p = 0.013)and vessel invasion(p = 0.004).Conclusions: BGN expression levels appear to be an independent prognostic factor for predicting the survival times of GC patients.