Identification of Biomarkers for Diagnosis of Gastric Cancer by Bioinformatics

Abstract

<b>Background:</b> We aimed to discover potential gene biomarkers for gastric cancer (GC) diagnosis. Materialsand <br/><b>Methods</b>: Genechips of 10 GC tissues and 10 gastric mucosa (GM, para-carcinoma tissue, normal control)tissues were generated using an exon array of Affymetrix containing 30,000 genes. The differentially expressedgenes (DEGs) between GC tissues and normal control were identified by the Limma package and analyzedby hierarchical clustering analysis. Gene ontology (GO) and pathway enrichment analyses were performedfor investigating the functions of DEGs. Receiver operating characteristics (ROC) analysis was performed tomeasure the effects of biomarker candidates for diagnosis of GC. <br/><b>Results</b>: Totals of 896 up-regulated and 60down-regulated DEGs were identified to be differentially expressed between GC samples and normal control.Hierarchical clustering analysis showed that DEGs were highly differentially expressed and most DEGs wereup-regulated. The most significantly enriched GO-BP term was revealed to be mitotic cell cycle and the mostsignificantly enriched pathway was cell cycle. The intersection analysis showed that most significant DEGs werecyclin B1 (CCNB1) and cyclin B2 (CCNB2). The sensitivities and specificities of CCNB1 and CCNB2 were bothhigh (p<0.0001). Areas under the ROC curve for CCNB1 and CCNB2 were both greater than 0.9 (p<0.0001).<br/><b>Conclusions</b>: CCNB1 and CCNB2, which were involved in cell cycle, played significant roles in the progressionand development of GC and these genes may be potential biomarkers for diagnosis and prognosis of GC.