Identification of biomarkers for diagnosis of gastric cancer by bioinformatics

Abstract

Background: We aimed to discover potential gene biomarkers for gastric cancer (GC) diagnosis. Materials and Methods: 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 expressed genes (DEGs) between GC tissues and normal control were identified by the Limma package and analyzed by hierarchical clustering analysis. Gene ontology (GO) and pathway enrichment analyses were performed for investigating the functions of DEGs. Receiver operating characteristics (ROC) analysis was performed to measure the effects of biomarker candidates for diagnosis of GC. Results: Totals of 896 up-regulated and 60 down-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 were up-regulated. The most significantly enriched GO-BP term was revealed to be mitotic cell cycle and the most significantly enriched pathway was cell cycle. The intersection analysis showed that most significant DEGs were cyclin B1 (CCNB1) and cyclin B2 (CCNB2). The sensitivities and specificities of CCNB1 and CCNB2 were both high (p < 0.0001). Areas under the ROC curve for CCNB1 and CCNB2 were both greater than 0.9 (p < 0.0001). Conclusions: CCNB1 and CCNB2, which were involved in cell cycle, played significant roles in the progression and development of GC and these genes may be potential biomarkers for diagnosis and prognosis of GC.