Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and is associated with various clinico-pathological characteristics such as genetic mutations and viral infections. Therefore, numerous laboratories look out for identifying always new putative markers for the improvement of HCC diagnosis/prognosis. Many molecular profiling studies investigated gene expression changes related to HCC. HepG2 represents a pure cell line of human liver carcinoma, often used as HCC model due to the absence of viral infection. In this study we compare gene expression profiles associated with HepG2 (as HCC model) and normal hepatocyte cells by microarray technology. Hierarchical cluster analysis of genes evidenced that 2646 genes significantly down-regulated in HepG2 cells compared to hepatocytes whereas a further 3586 genes significantly up-regulated. By using the Ingenuity Pathway Analysis (IPA) program, we have classified the genes that were differently expressed and studied the functional networks correlating these genes in the complete human interactome. Moreover, to confirm the differentially expressed genes as well as the reliability of our microarray data, we performed a quantitative Real time RT-PCR analysis on 9 up-regulated and 11 down-regulated genes, respectively. In conclusion this work i) provides a gene signature of human hepatoma cells showing genes that change their expression as a consequence of liver cancer in the absence of any genetic mutations or viral infection, ii) evidences new differently expressed genes found in our signature compared to previous published studies and iii) suggests some genes on which to focus future studies to understand if they can be used to improve the HCC prognosis/diagnosis.
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