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.

Senescence in chronic liver disease: Is the future in aging?

“…A study using a murine [71]. Whether on-going insult leads to further DNA damage in already senescent hepatocytes, which help these cells overcome senescence barriers is not yet known.…”

Rac1-Mediated DNA Damage and Inflammation Promote Nf2 Tumorigenesis but Also Limit Cell-Cycle Progression

“…As senescence involves global transcription reprogramming, we next compiled a high confidence hepatocyte senescence gene signature (HSGS) by intersecting genes differentially expressed in hepatocytes that had undergone H 2 O 2 or replication induced senescence from two independent studies (66 up and 42 down) (Aravinthan et al, 2014; Yildiz et al, 2013). GSEA showed that the upregulated genes within our HSGS (Hepatocyte_Senescence_Up) were strongly enriched with genes expressed higher in Nf2 KO tumors than in DKO livers, whereas the downregulated genes within the HSGS (Hepatocyte_Senescence_Down) significantly coincided with genes with decreased expression in Nf2 KO tumors compared to DKO livers (Figure 4B).…”

Functionally conserved effects of rapamycin exposure on zebrafish

“…For DAVID based pathways and GO analyses, P-values corrected using the Benjamini Hochberg method were reported; Benjamini<0.05 was considered to indicate a statistically significant difference. Zebrafish and mouse GO term lists [biological process (BP); cellular compartment (CC); molecular function (MF)] were matched with each other; and shared terms having P≤0.05 were tested for a significant positive association using odds ratios (OR, based on the conditioned maximum likelihood estimate) calculated using two-sided Fisher's exact test from the exact2x2 package in R software, version 3.1.2 (30,31).…”