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Objective The aim of this study was to use bioinformatic analyses to identify key genes and pathways driving gastric cancer (GC). Materials and Methods The gene expression profiles, from human gastric tissue samples were downloaded from the Gene Expression Omnibus (GSE)29272 dataset. These data revealed 284 differentially expressed genes (DEGs) that included a group upregulated in cancer tissues (n = 142) and another group that were downregulated in cancer tissues. (n = 142). These DEGs were identified using the GEO2R. We used multiple online analysis tools
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