Identification of gastric cancer-related genes by multiple high throughput analysis and data mining

OBJECTIVE: To investigate gastric cancer-related genes by combined multiple high throughput analysis and data mining, and to further identify gene markers that may be useful in the diagnosis and treatment of gastric cancer. METHODS: Data of expressed sequence tags (EST) and serial analysis of gene expression (SAGE) in Cancer Genome Anatomy Project (CGAP) were employed to analyze differential gene expression between normal and cancerous gastric epithelium,the obtained genes were further analyzed by virtual Northern blotting and compared with microarray data from Stanford Microarray Database (SMD). RESULTS: NCBI digital differential display (DDD), cDNA digital gene expression displayed (DGED) and SAGE DGED produced 165,286 and 181 differential expression genes.All these genes were analyzed by virtual Northern blotting and 45 genes were obtained. Comparing with microarray data, candidate genes were reduced to 12. Further RT-PCR analyses validated 4 genes, including ANXA1, MSMB, ANXA10 and PSCA, were differentially expressed in normal and cancerous gastric tissues. CONCLUSIONS: Combined multiple high throughput analysis and data mining is an effective strategy for identification of gastric cancer-related genes. Further analyses of these genes from data mining will provide biomarkers for the diagnosis and treatment of gastric cancer.