5). Figure 5 Comparative analysis of core enriched gene sets in Huh7 clones Ganetespib HSP (e.g. HSP90) inhibitor (senescent versus immortal) and diseased liver tissues (cirrhosis versus HCC) indicated that retinoid metabolism genes (��KEGG_RETINOL_METABOLISM��) undergo systematic changes … A Senescence-to-immortality Switch between Dysplasia and Hepatocellular Carcinoma Hepatocellular carcinogenesis is a multi-step process that is usually manifested by progressive histological changes in the liver from the cirrhosis stage to dysplasia followed by HCC [47]. Based on close association of cirrhosis with senescence and that of HCC with immortality, we hypothesized that the relative expression of senescence- and immortality-associated genes in different liver lesions may serve as a powerful means to dissect the timing of transition from a senescent state to an immortal phenotype during hepatocellular carcinogenesis.

With this aim, we first generated a list of ��senescence-related genes�� by comparing differential gene expression between senescent and immortal Huh7 clones. Then, we analyzed the expression patterns of these ��senescence-related genes�� in a spectrum of hepatic lesions representing different steps of HCC development. The list of senescence-related genes was established by class comparison analysis of in vitro gene expression data. Multivariate permutation tests identified 1220 genes represented by 1813 probe sets with statistically significant expression changes between senescent and immortal clones (P-values<10?7; fold-changes between senescent and immortal clones: >2.0).

The selected probe sets were then tested against a publicly available gene expression dataset for tissues at different histological stages of HCC development in HCV patients [35]. The tissue set was composed of 10 normal liver samples, 13 cirrhotic tissues, 17 dysplastic lesions (originally described low- and high-grade dysplasia cases combined), 17 early HCCs (originally described Drug_discovery very early and early HCC cases combined) and 18 advanced HCCs (originally described advanced and very advanced cases combined). Unsupervised clustering analysis applied to compare these hepatic tissue samples (n=75 in total) generated two major clusters. Cluster 1 grouped together 39 out of the 40 non-HCC samples (97.5%) and 1 out of the 35 (3%) HCC samples. Conversely, cluster 2 was composed of 34 out of the 35 HCCs (97%) and one of 40 (2.5%) of the non-HCC samples (Fig. 6). Dysplastic lesions together with a subset of cirrhosis tissues formed a homogenous subgroup under cluster 1, while normal liver samples shared similarities with either cirrhotic or dysplastic tissue. HCC samples formed several minor clusters, with a tendency of early and advanced tumors to form distinct sub-clusters.