Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Integrating the Epigenome to Identify Novel Drivers of Hepatocellular Carcinoma

ABSTRACT: Integrating the Epigenome to Identify Novel Drivers of Hepatocellular Carcinoma

PROVIDER: PRJNA445291 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR6880460_1.fastq.gz	Fastqsanger.gz
	SRR6880460_2.fastq.gz	Fastqsanger.gz
	SRR6880470_1.fastq.gz	Fastqsanger.gz
	SRR6880470_2.fastq.gz	Fastqsanger.gz
	SRR6880480_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 120

Similar Datasets

Integrating the Epigenome to Identify Drivers of Hepatocellular Carcinoma.

Project description:Disruption of epigenetic mechanisms has been intimately linked to the etiology of human cancer. Understanding how these epigenetic mechanisms (including DNA methylation [5mC], hydroxymethylation [5hmC], and histone post-translational modifications) work in concert to drive cancer initiation and progression remains unknown. Hepatocellular carcinoma (HCC) is increasing in frequency in Western countries but lacks efficacious treatments. The epigenome of HCC remains understudied. To better understand the epigenetic underpinnings of HCC, we performed a genome-wide assessment of 5mC, 5hmC, four histone modifications linked to promoter/enhancer function (H3K4me1, H3K27ac, H3K4me3, and H3K27me3), and transcription across normal, cirrhotic, and HCC liver tissue. Implementation of bioinformatic strategies integrated these epigenetic marks with each other and with transcription to provide a comprehensive epigenetic profile of how and when the liver epigenome is perturbed during progression to HCC. Our data demonstrate significant deregulation of epigenetic regulators combined with disruptions in the epigenome hallmarked by profound loss of 5hmC, locus-specific gains in 5mC and 5hmC, and markedly altered histone modification profiles, particularly remodeling of enhancers. Data integration demonstrates that these marks collaborate to influence transcription (e.g., hyper-5hmC in HCC-gained active enhancers is linked to elevated expression) of genes regulating HCC proliferation. Two such putative epigenetic driver loci identified through our integrative approach, COMT and FMO3, increase apoptosis and decrease cell viability in liver-derived cancer cell lines when ectopically re-expressed. Conclusion: Altogether, integration of multiple epigenetic parameters is a powerful tool for identifying epigenetically regulated drivers of HCC and elucidating how epigenome deregulation contributes to liver disease and HCC.

| S-EPMC6351162 | biostudies-literature

Integrating the Epigenome to Identify Novel Drivers of Hepatocellular Carcinoma

Project description:Gene expression, histone modification, DNA methylation, and DNA hydroxymethylation from normal, cirrhotic, and HCC livers

2018-09-09 | GSE112221 | GEO

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

Project description: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. Two hundred and eighty-six hepatocellular carcinoma tumors and their matched non-tumor adjacent liver tissue samples were genotyped using Illumina HumanOmni1-Quad BeadChip to estimate their somatic copy number profiles.

2013-05-22 | E-GEOD-38323 | biostudies-arrayexpress

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

Project description: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. Thirty hepatocellular carcinoma cell lines were genotyped using Illumina HumanOmni1-Quad BeadChip to estimate their copy number profiles relative to pooled Hapmap samples.

2013-05-22 | E-GEOD-38207 | biostudies-arrayexpress

INTEGRATING GENOMIC AND TRANSCRIPTOMIC DATA TO IDENTIFY BREAST CANCER SUSCEPTIBILITY GENES

Project description:INTEGRATING GENOMIC AND TRANSCRIPTOMIC DATA TO IDENTIFY BREAST CANCER SUSCEPTIBILITY GENES

| PRJNA1069853 | ENA

INTEGRATING GENOMIC AND TRANSCRIPTOMIC DATA TO IDENTIFY BREAST CANCER SUSCEPTIBILITY GENES

Project description:INTEGRATING GENOMIC AND TRANSCRIPTOMIC DATA TO IDENTIFY BREAST CANCER SUSCEPTIBILITY GENES

| PRJNA1069854 | ENA

Cooperation between distinct cancer drivers dictates inter-tumor heterogeneity in hepatocellular carcinoma

Project description:Cooperation between distinct cancer drivers dictates inter-tumor heterogeneity in hepatocellular carcinoma

| PRJNA624109 | ENA

Epigenome

Project description:Epigenome

2022-03-31 | OEX00016373 | NODE

Gene Signature to Identify Vascular Invasion in Human Hepatocellular Carcinoma

Project description:Gene Signature to Identify Vascular Invasion in Human Hepatocellular Carcinoma

| PRJNA125739 | ENA

Deep targeted sequencing of multiregional sampling in hepatocellular carcinoma

Project description:The purpose of this experiment of high-coverage DNA sequencing of 58 frequently mutated genes in hepatocellular carcinoma (HCC) is to confirm clonal distribution of the known HCC drivers in samples corresponding to multiple regions of a tumor.

2020-01-16 | E-MTAB-5899 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data