Determination of points for diagnosis and treatment in gastric cancers by in silico methods

Purpose and hypothesis: Gastric cancer (GC) is the fourth most common cause of cancer-related death in the world. Gastric cancer constitutes a significant health burden in the world. Many GC patients are in advanced stages of gastric cancer at the time of diagnosis and miss the opportunity for the best treatment. In this study, we aimed to reveal the molecular mechanisms of GC and to identify specific biomarkers for GC prognosis, early diagnosis and targeted therapy. Material-method: 10 mRNA datasets (GSE79973, GSE34942, GSE38749, GSE35809, GSE22377, GSE66222, GSE54129, GSE42252, GSE19826 and GSE13911) were selected from the Gene Expression Omnibus (GEO) database. The dataset includes 177 normal gastric tissue samples and 360 gastric adenocarcinoma samples, with a total sample number of 537. Differentially expressed genes (DEGs) were selected by the R software. R software and Volcano plot were used to distinguish differentially expressed genes (DEGs). We then performed gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. STRING and Cytoscape software were also used to analyze protein-protein interaction (PPI) networks of DEGs common among the 10 datasets. Results: A total of 520 DEGs were identified, including 260 up-regulated and 260 down-regulated genes from 10 mRNA datasets. DEGs with log FC>2 were detected out of 520 DEGs with R software and Volkan graph. KEGG analysis showed that up-regulated DEGs are mainly enriched in Protein digestion and absorption, ECM-receptor interaction, Drug metabolism, IL-17 signaling pathway, Cytokine-cytokine receptor interaction, and Amoebiasis pathways. GO analysis showed that up-regulated DEGs are mainly enriched in extracellular matrix organization, extracellular structure organization, external encapsulating structure organization and regulation of cell population proliferation, collagen fibril organization, and mitotic sister chromatid pathways. The STRING database and Cytoscape software were used to predict and analyze protein interactions between a total of 83 DEGs, 16 up-regulated (log FC> 2) and 67 down-regulated (log FC <2). We selected 10 DEGs (INHBA, SULF1, COL1A2, CTHRC1, THBS2, COL1A1, COL11A1, SFRP4, FN1, COL10A1) by combining the STRING and Cytoscape analysis results. These genes are up-regulated DEGs with high degree of connectivity, log FC>2 and highest FcS. Conclusion: Using bioinformatics analysis methods in this study, 10 DEGs, including INHBA, SULF1, COL1A2, CTHRC1, THBS2, COL1A1, COL11A1, SFRP4, FN1, COL10A1, were significantly highly expressed in gastric cancer patients. Our study suggests that these 10 DEGs may be potential biomarkers and therapeutic targets for GC. Key words: Gastric cancer (GC), bioinformatics analysis, omnibus gene expression (GEO), differentially expressed genes (DEGs)