Insights into the etiology‐associated gene regulatory networks in hepatocellular carcinoma from The Cancer Genome Atlas

2018 ◽  
Vol 33 (12) ◽  
pp. 2037-2047 ◽  
Author(s):  
Veerabrahma Pratap Seshachalam ◽  
Karthik Sekar ◽  
Kam M Hui
Keyword(s):  
Hepatocellular Carcinoma ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Gene Regulatory ◽  
Genome Atlas

scholarly journals Dynamics and predicted drug response of a gene network linking dedifferentiation with β-catenin dysfunction in hepatocellular carcinoma

2018 ◽  
Author(s):  
Claude Gérard ◽  
Mickaël Di-Luoffo ◽  
Léolo Gonay ◽  
Stefano Caruso ◽  
Gabrielle Couchy ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
The Cancer Genome Atlas ◽  
Pharmacological Agents ◽  
Distinct Cell ◽  
Cancer Genome Atlas ◽  
Gene Regulatory ◽  
Using Data ◽  
Insight Into

AbstractAlterations of individual genes variably affect development of hepatocellular carcinoma (HCC), prompting the need to characterize the function of tumor-promoting genes in the context of gene regulatory networks (GRN). Here, we identify a GRN which functionally links LIN28B-dependent dedifferentiation with dysfunction of CTNNB1 (β-CATENIN). LIN28B and CTNNB1 form a functional GRN with SMARCA4 (BRG1), Let-7b, SOX9, TP53 and MYC. GRN activity is detected in HCC and gastrointestinal cancers; it negatively correlates with HCC prognosis and contributes to a transcriptomic profile typical of the proliferative class of HCC. Using data from The Cancer Genome Atlas and from transcriptomic, transfection and mouse transgenic experiments, we generated and validated a quantitative mathematical model of the GRN. The model predicts how the expression of GRN components changes when the expression of another GRN member varies or is inhibited by a pharmacological drug. The dynamics of GRN component expression reveal distinct cell states that can switch reversibly in normal condition, and irreversibly in HCC. We conclude that identification and modelling of the GRN provides insight into prognosis, mechanisms of tumor-promoting genes and response to pharmacological agents in HCC.

scholarly journals A Primer on a Hepatocellular Carcinoma Bioresource Bank Using the Cancer Genome Atlas Guidelines: Practical Issues and Pitfalls

2011 ◽  
Vol 35 (8) ◽  
pp. 1732-1737 ◽  
Author(s):  
N. Thao T. Nguyen ◽  
Ron T. Cotton ◽  
Theresa R. Harring ◽  
Jacfranz J. Guiteau ◽  
Marie-Claude Gingras ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals The Cancer Genome Atlas (TCGA) based m6A methylation-related genes predict prognosis in hepatocellular carcinoma

Bioengineered ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 759-768
Author(s):  
Jun Liu ◽  
Guili Sun ◽  
Shangling Pan ◽  
Mengbin Qin ◽  
Rong Ouyang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals Master Regulators, Regulatory Networks, and Pathways of Glioblastoma Subtypes

2014 ◽  
Vol 13s3 ◽  
pp. CIN.S14027 ◽  
Author(s):  
Serdar Bozdag ◽  
Aiguo Li ◽  
Mehmet Baysan ◽  
Howard A. Fine
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Gene Set Enrichment ◽  
Master Regulators ◽  
Cancer Genome Atlas ◽  
Gene Regulatory ◽  
Comprehensive Study

Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic and epigenetic profiles. Despite numerous studies to better characterize GBM biology, a comprehensive study to identify GBM subtype-specific master regulators, gene regulatory networks, and pathways is missing. Here, we used FastMEDUSA to compute master regulators and gene regulatory networks for each GBM subtype. We also ran Gene Set Enrichment Analysis and Ingenuity Pathway Analysis on GBM expression dataset from The Cancer Genome Atlas Project to compute GBM- and GBM subtype-specific pathways. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes.

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