Genome-wide transcriptional reorganization associated with senescence-to-immortality switch during human hepatocellular carcinogenesis

Abstract

Senescence is a permanent proliferation arrest in response to cell stress such as DNA damage. It contributes strongly to tissue aging and serves as a major barrier against tumor development. Most tumor cells are believed to bypass the senescence barrier (become "immortal") by inactivating growth control genes such as TP53 and CDKN2A. They also reactivate telomerase reverse transcriptase. Senescence-to-immortality transition is accompanied by major phenotypic and biochemical changes mediated by genome-wide transcriptional modifications. This appears to happen during hepatocellular carcinoma (HCC) development in patients with liver cirrhosis, however, the accompanying transcriptional changes are virtually unknown. We investigated genome-wide transcriptional changes related to the senescence-to-immortality switch during hepatocellular carcinogenesis. Initially, we performed transcriptome analysis of senescent and immortal clones of Huh7 HCC cell line, and identified genes with significant differential expression to establish a senescence-related gene list. Through the analysis of senescence-related gene expression in different liver tissues we showed that cirrhosis and HCC display expression patterns compatible with senescent and immortal phenotypes, respectively; dysplasia being a transitional state. Gene set enrichment analysis revealed that cirrhosis/senescence-associated genes were preferentially expressed in non-tumor tissues, less malignant tumors, and differentiated or senescent cells. In contrast, HCC/immortality genes were up-regulated in tumor tissues, or more malignant tumors and progenitor cells. In HCC tumors and immortal cells genes involved in DNA repair, cell cycle, telomere extension and branched chain amino acid metabolism were up-regulated, whereas genes involved in cell signaling, as well as in drug, lipid, retinoid and glycolytic metabolism were down-regulated. Based on these distinctive gene expression features we developed a 15-gene hepatocellular immortality signature test that discriminated HCC from cirrhosis with high accuracy. Our findings demonstrate that senescence bypass plays a central role in hepatocellular carcinogenesis engendering systematic changes in the transcription of genes regulating DNA repair, proliferation, differentiation and metabolism.

Figure 1. Flow chart summarizing the study design.

We analyzed genome-wide gene expression in isogenic Huh7 clones with senescent and immortal phenotypes, as well as in cirrhotic tissues and HCC tumors. Both sets of data were subjected to GSEA using “C2-All” curated gene sets (“C2_All”) of molecular signature database (MSigDB; www.broadinstitute.org/gsea/). In order to assess senescence- and immortality-related gene expression changes during hepatocellular carcinogenesis, genes differentially expressed in the model of cellular senescence and immortality were identified and the evolution of their expression profiles in pre-neoplastic and neoplastic liver lesions were examined. Finally, a 15-gene senescence-based signature was generated using a training set of cirrhosis and HCC samples, and validated using independently generated test datasets.

Figure 2. Gene expression profile analysis by gene set enrichment analysis assay (GSEA) established that senescent and immortal Huh7 clones displayed differential expression of previously identified senescence- and immortality-associated gene sets respectively, as well as those regulating telomere maintenance.

(a) Heat map representation of the top 100 deregulated genes in immortal Huh7 clones (Immortal) versus senescent Huh7 (Senescent) clones. Red: up-regulated; blue: down-regulated; arrow indicates TERT gene whose expression is down-regulated in senescent clones. Previously identified gene sets (available at molecular signature database (MSigDB; www.broadinstitute.org/gsea/) were screened to identify those that are differentially enriched in senescent or immortal Huh7 clones by the analysis of their relative expression levels using GSEA method. (b) Gene set enrichment plots showing the up-regulated expression of two previously known senescence-associated gene sets in senescent Huh7 clones, including genes that are commonly up-regulated in senescent cells (“FRIDMAN_SENESCENCE_UP”) and p53-responsive genes up-regulated during replicative senescence arrest (“TANG_SENESCENCE_TP53_TARGERTS_UP”) . In addition, genes known to be down-regulated during immortalization in general (“FRIDMAN_IMMORTALIZATION_DN”) , and by human papillomavirus 31 (“CHANG_IMMORTALIZED_BY_HPV_DN”) were also up-regulated in senescent Huh7 clones. (c) Genes known to be down-regulated by TERT-mediated immortalization (“KANG_IMMORTALIZED_BY_TERT_DN”) and TERT-repressed target genes (“SMITH_TERT_TARGETS_DN”) were also enriched in senescent Huh7 clones. (d) Genes involved in telomere end packaging (“REACTOME_PACKAGING_OF_TELOMERE_ENDS”; www.reactome.org) were upregulated in senescent Huh7 clones. (e) In contrast, genes involved in telomere extension (“REACTOME_EXTENSION_OF_TELOMERES”; www.reactome.org) were enriched in immortal Huh7 clones. Enrichment scores (ES) are shown on the y-axis. Positive and negative ES indicate enrichment in immortal and senescent Huh7 clones, respectively. X-axis bars represent individual genes of the indicated gene sets. FDR: False discovery rate, p: nominal p-value. Three biological replicates from each clone were analyzed for genome-wide gene expression using Affymetrix 54 K microarrays and normalized data were used for gene set enrichment analysis (GSEA).

Figure 3. Gene expression profile analysis by gene set enrichment analysis (GSEA) revealed the overexpression of senescence-upregulated genes in cirrhosis, but over-expression of senescence-downregulated genes and telomere extension genes in HCC tissues.

(a) Heat map representation of the top 100 deregulated genes in hepatocellular carcinoma (HCC) versus cirrhosis samples. Red: up-regulated; blue: down-regulated. Previously identified gene sets (available at molecular signature database (MSigDB; www.broadinstitute.org/gsea/) were screened to identify those that are up-regulated in cirrhosis or HCC tissues by the analysis of their relative expression levels using GSEA method. (b) Enrichment plot of p53-responsive genes up-regulated during replicative senescence arrest (“TANG_SENESCENCE_TP53_TARGERTS_UP”) showing over-expression in cirrhosis. (b) In contrast, p53-responsive genes down-regulated during replicative senescence arrest (“TANG_SENESCENCE_TP53_TARGERTS_DN”) and those involved in telomere extension (“REACTOME_EXTENSION_OF_TELOMERES”; www.reactome.org) were overexpressed in HCC tumors. Enrichment scores (ES) are shown on the y-axis. Positive and negative ES indicate enrichment in HCC and cirrhosis samples, respectively. X-axis bars represent individual genes of the indicated gene sets. FDR: False discovery rate, p: nominal p-value. Fifteen cirrhosis and fifteen HCC samples were analyzed for genome-wide gene expression using Affymetrix 54 K microarrays and normalized data were used for gene set enrichment analysis (GSEA).

Figure 4. Comparative analysis of gene sets enriched in Huh7 clones and diseased liver tissues associated cirrhosis with senescence and HCC with immortality phenotypes, respectively.

(a). This analysis revealed also that cirrhosis/senescence- and HCC/immortality-associated gene sets implicated distinct biological features specific to each phenotype (b). (a) Scatter plot compares enrichment scores of 74 gene sets commonly enriched in Huh7 clones (senescent or immortal) and diseased liver tissues (cirrhosis or HCC) with a P value less than 0.05. Thirty-nine gene sets (53%) were significantly enriched in both HCC and immortal samples whereas 34 (46%) gene sets were significantly enriched in both cirrhosis and senescent samples (correlation value r = 0.97, p = 2×10⁻⁴³). Only one gene set (1%) was enriched in both HCC and senescent clones. (b) Distribution of biological features defined by different gene sets in cirrhosis/senescence (blue columns) and HCC/immortality (red columns) phenotypes, respectively.

Figure 5. Comparative analysis of core enriched gene sets in Huh7 clones (senescent versus immortal) and diseased liver tissues (cirrhosis versus HCC) indicated that retinoid metabolism genes (“KEGG_RETINOL_METABOLISM”) undergo systematic changes in immortal cells and HCC, when compared to senescent cells and cirrhosis, respectively.

(a) Heat map of core enriched retinoid metabolism genes in Huh7 clones (left) and diseased liver tissues (right). Red: up-regulated; blue: down-regulated. Genes commonly deregulated in both Huh7 clones and diseased liver tissues are indicated with a dot. (b) A simplified view of retinoid metabolism. Enzyme-encoding genes down-regulated in HCC are shown in blue. LRAT: lechitin retinol acetyl transferase, PNPLA4: patatin-like phospholipase domain containing-4, RDHs: retinol dehydrogenases; ADHs: alcohol dehydrogenases; DHRS4: dehydrogenase/reductase (SDR family) member-4; BCMO1: beta-carotene 15,15′-monooxygenase-1; CYPs: Cytochrome P-450 family proteins; UGTs: UDP glucoronosyltransferases.

Figure 6. Hierarchical clustering of 75 non-malignant and malignant liver tissue samples using 1813 senescence-associated gene probe sets.

Hepatocellular carcinoma and non-tumor liver tissues formed two distinct clusters (1 and 2) with the exception of one dysplasia and two early HCC samples. The rows and columns represent genes and samples, respectively on the cluster map. Tissue samples are normal liver (pink), cirrhosis (blue), dysplasia (yellow), early HCC (gray), and advanced HCC (black). Red: over-expressed, green: under-expressed probe set in the heat map.

Figure 7. Generation and validation of a senescence-based gene classifier for differential diagnosis of cirrhosis and HCC.

(a) Scatter plot graphic compares relative expression levels (Log2 ratios) of 18 classifier probe sets representing 17 genes in Huh7 clones (immortal versus senescent) and diseased liver tissues (HCC versus cirrhosis). Expression ratios of classifier genes showed a linear correlation (correlation value r = 0.7, p = 0.0017) with ratios observed in Huh7 clones (immortal/senescent) and diseased liver tissues (HCC/cirrhosis). The classifier set was identified by PAM analysis of 1813 senescence-associated probe sets using a training tissue set composed of cirrhosis (n = 13) and HCC (n = 35) samples described by Wurmbach et al. . Two probe sets which did not show expression patterns compatible with our in vivo senescence model were discarded to define a final signature set composed of 16 probe sets representing 15 genes. (b) Validation of molecular prediction of HCC and cirrhosis by 15-classifier gene set. Using the nearest template prediction method , we compared expression levels of sixteen probe sets representing 15 classifier genes in a test tissue set composed of 20 cirrhosis and 25 HCC samples originating from Turkish (TR) patients described in this report, and Japanese (JP) patients described elsewhere . BH FDR (Benjamini-Hochberg false discovery rates) values (top), clinical versus predicted phenotypes (middle) and heatmaps of classifier gene expression levels (bottom) are shown. The test provided a diagnostic result for 40 out of 45 samples (89%) with 97.5% (39/40) accuracy.

Figure 8. Association of ATAD2 RNA and protein expressions with HCC and cellular immortality.

(a) Amplified expression of ATAD2 RNA in HCC cell lines, as compared to normal hepatocytes and MRC-5 fibroblasts. Total RNAs were extracted from freshly isolated adult human hepatocytes (Hepatocytes), MRC-5 human embryonic lung fibroblast cells (PD44) and 14 HCC cell lines; reverse transcribed into cDNA; and ATAD2 RNA was quantified by quantitative real-time PCR using specific primers. ATAD2 expression values for each sample were normalized with housekeeping gene GAPDH RNA values. Relative expression of ATAD2 in MRC-5 and HCC cell lines was expressed in reference to its expression in hepatocytes. Averages of three measurements. Error bars: SD. (b) Amplified expression of ATAD2 protein in HCC cells, as compared to normal hepatocytes. Total proteins were extracted from freshly isolated adult human hepatocytes (Hepatocytes), untreated (Hep3B) and ATAD2 siRNA1-treated (Hep3B-si) Hep3B and eight other HCC cell lines, and ATAD2 protein levels were tested by western blot analysis using a specific anti-ATAD2 antibody (ATAD2). Western blot analysis of calnexin protein from the same blots was used for loading control (Calnexin). (c, d) Comparative analysis by western blotting demonstrated that ATAD2 protein is overexpressed in immortal Huh7 cells as compared to senescence-arrested Huh7 cells. (c) Huh7 cells were treated with Adriamycin (0.1 µM) or DMSO (Control) for three days and subjected to senescence assay by SA-β-Gal staining (blue). Cells were counterstained with fast red (red). (d) Total protein was extracted from control and Adriamycin-treated Huh7 cells, and ATAD2 and Calnexin proteins were tested as described in (b).