Search for new biomarkers of gastric cancer through serial analysis of gene expression and its clinical implications - PubMed (original) (raw)

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Search for new biomarkers of gastric cancer through serial analysis of gene expression and its clinical implications

Wataru Yasui et al. Cancer Sci. 2004 May.

Abstract

Gastric cancer is one of the most common human cancers and is the second most frequent cause of cancer-related death in the world. Serial analysis of gene expression (SAGE) is a powerful technique to allow genome-wide analysis of gene expression in a quantitative manner without prior knowledge of the gene sequences. SAGE on 5 samples of gastric cancer with different histology and clinical stages have created large SAGE libraries of gastric cancer that enable us to identify new cancer biomarkers. Commonly up-regulated genes in gastric cancer in comparison with normal gastric epithelia included CEACAM6, APOC1 and YF13H12. By comparing gene expression profiles of gastric cancers at early and advanced stages, several genes differentially expressed by tumor stage were also identified, including FUS, CDH17, COL1A1 and COL1A2, which should be novel genetic markers for high-grade malignancy. Regenerating gene type IV (REGIV) is one of the most up-regulated genes in a SAGE library of a scirrhous-type gastric cancer. In vitro studies using RegIV-transfected cells revealed that RegIV is secreted by cancer cells and inhibits apoptosis, suggesting that RegIV may serve as a novel biomarker and therapeutic target for gastric cancer. Production of RNA aptamers could be a useful approach to establish a detection system in blood. A custom-made array, named Ex-STOMACHIP, consisting of 395 genes, including highly differentially expressed genes identified by our SAGE and other known genes related to carcinogenesis and chemosensitivity, is useful to study the molecular pathogenesis of gastric cancer and to obtain information about biological behavior and sensitivity to therapy in the clinical setting. Combined analyses of gene expression profile, genetic polymorphism and genetic instability will aid not only cancer detection, but also characterization of individual cancers and patients, leading to personalized medicine and cancer prevention.

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