In silico analysis and verification of S100 gene expression in gastric cancer

Combined with analysis of series analysis of gene expression, virtual Northern blot and microarray data, the expression levels of S100 family members in normal and malignant stomach tissues were systematically investigated. The expression of S100A3 was further evaluated by quantitative RT-PCR.At least 5 S100 genes were found to be upregulated in gastric cance by in silico analysis. Among them, four genes, including S100A2, S100A4, S100A7 and S100A10, were reported to overexpressed in gastric cancer previously. The expression of S100A3 in eighty patients of gastric cancer was further examined. The results showed that the mean expression levels of S100A3 in gastric cancer tissues were 2.5 times as high as in adjacent non-tumorous tissues. S100A3 expression was correlated with tumor differentiation and TNM (Tumor-Node-Metastasis) stage of gastric cancer, which was relatively highly expressed in poorly differentiated and advanced gastric cancer tissues (P < 0.05).To our knowledge this is the first report of systematic evaluation of S100 gene expressions in gastric cancers by multiple in silico analysis. The results indicated that overexpression of S100 gene family members were characteristics of gastric cancers and S100A3 might play important roles in differentiation and progression of gastric cancer.Gastric cancer is the second most common cause of cancer death worldwide. Environmental and genetic factors are both important in gastric carcinogenesis [1,2]. In the past two decades, much progress has been made in identifying genes involved in the development of gastric cancer. These identified genes are useful in understanding the pathogenesis of gastric cancer and defining its molecular signature. They can also serve as biomarkers for early diagnosis and targets for drug development.Recently, large-scale gene expression analyses have emerged as important tools for screening genes related to cancer [3]. The two experimental technologies available for large-scale gene exp