Dataset Information

Genomic landscape of copy number aberrations enables the identification of oncogenic drivers in hepatocellular carcinoma [cell line]

ABSTRACT: Cancer is a genetic disease with frequent somatic alterations in DNA. Study of recurrent copy number aberrations (CNAs) in human cancers would enable the elucidation of disease mechanisms and the identification of key oncogenic drivers with causal roles in oncogenesis. We have comprehensively and systematically characterized CNAs and accompanied gene expression changes in the tumors and their matched non-tumor liver tissues from 286 hepatocellular carcinoma (HCC) patients. Our analysis identified 29 recurrently amplified regions and 22 deleted regions with a high level of copy number changes, harboring established oncogenes and tumor suppressors, including CCND1, MET, CDKN2A and CDKN2B, as well as many other genes not previously reported to be involved in liver carcinogenesis. Cis-acting genes in the amplification and deletion peaks were enriched in core cancer pathways, including cell cycle, p53, PI3K, MAPK, Wnt and TGFβ signaling in large proportions of HCCs. We further validated two candidate driver genes, BCL9 and MTDH, from the recurrent focal amplification peaks and showed that they play a significant role in HCC growth and survival. In summary, we have demonstrated that characterizing the CNA landscape in HCC will facilitate the understanding of disease mechanisms and the identification of oncogenic drivers that may serve as potential therapeutic targets for the treatment of this devastating disease.

ORGANISM(S): Homo sapiens

PROVIDER: GSE38207 | GEO | 2013/05/22

SECONDARY ACCESSION(S): PRJNA167447

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Hepatocellular carcinoma (HCC) is a prevalent human cancer with rising incidence worldwide. Human HCC is frequently associated with chronic liver inflammation and cirrhosis, pathophysiological processes that are a consequence of chronic viral infection, disturbances in metabolism, or exposure to chemical toxicants. To better characterize the pathogenesis of HCC, we used a human disease-relevant mouse model of fibrosis-associated hepatocarcinogenesis. In this model, marked liver tumor response caused by a pro-mutagenic chemical N-nitrosodiethylamine in presence of liver fibrosis was associated with epigenetic events indicative of genomic instability. Therefore, we hypothesized that DNA copy number alterations (CNAs), a feature of genomic instability and a common characteristic of cancer, are concordant between human HCC and mouse models of fibrosis-associated hepatocarcinogenesis. We evaluated DNA CNAs and changes in gene expression in the mouse liver (normal, tumor and non-tumor cirrhotic tissues). In addition, we compared our findings to those in human HCC (tumor and non-tumor cirrhotic/fibrotic tissues). We observed that while fibrotic liver tissue is largely devoid of DNA CNAs, highly frequently occurring DNA CNAs are found in mouse tumors, which is indicative of a profound increase in chromosomal instability in HCC. When compared to CNAs in human HCC, we found that 33% of genes in these segments are similarly affected in the mouse tumors. Our results suggest that CNAs most commonly arise in neoplastic tissue rather than in fibrotic liver, and demonstrate the utility of this mouse model in replicating the molecular features of human HCC. Genomic DNA was extracted from frozen liver samples from vehicle-control and DEN+CCl4-treated mice using a DNEasy kit (Qiagen, Valencia, CA). Eighteen mouse tumor samples were included in the study, as well as 18 matched non-tumor samples taken from fibrotic, non-tumorous, surrounding liver tissue from the same mice. Liver DNA from 6 vehicle control-mice was pooled and used as the reference genome in the aCGH experiments. Copy number alterations were identified using the normalized data.

Dataset Information

Genomic landscape of copy number aberrations enables the identification of oncogenic drivers in hepatocellular carcinoma [cell line]

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets