Abstract

Gastric cancer (GC) is the fifth most common cancer type worldwide. The aim of this study was to identify gastric-related therapeutic indicators on the basis of the ego network analysis. The microarray data related to GC was downloaded from ArrayExpress database. All human protein-protein interaction (PPI) networks were downloaded from the STRING database. Ego genes were identified on the basis of PPI networks and the gene expression in GC, and then co-expression networks (ego networks) were constructed using these ego genes. On the basis of ego networks, the optimal GO terms and genes were predicted by affinity predictions and cold read predictions. Finally, the predicted genes as effective biomarkers for GC were verified by the bioinformatics analysis. The differential expression networks were conducted and comprised of 365 edges and 232 nodes, which resulted in 218 ego genes. Although there was no significant difference in the expression of top ten ego genes among different groups of GC samples, it was eventually confirmed that top three optimal GO terms with highest cool read values were translational termination (cool read value = 0.987), translational elongation (cool read value = 0.986), and macromolecular complex disassembly (cool read value = 0.985) and top five optimal genes were UBA52, RPS27A, MAPK1, UBC, and UBB. UBA52, RPS27A, and MAPK1 were verified by the bioinformatics analysis to be related to the progression and metastasis of GC. An ego network analysis approach is a very effective method for screening GC and the screened genes might be biomarkers for GC diagnosis and treatment.

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