Iris Publishers- Open access journal of Gastroenterology & Hepatology| Understanding the Integrated Gene Regulatory
Networks for Hepatocellular Carcinoma
Hepatic cancer is a malignant tumor that begins in the cells of
the liver. The leading cause is a viral infection with hepatitis B and
hepatitis C. Hepatocellular carcinoma (HCC) has become the most
common form of hepatic cancer. It is the fastest-growing cause of
cancer deaths in the United States. HCC is also found to be associated
with obesity, type 2 diabetes and fatty liver disease. To understand
the gene expression regulation during HCC development, scientists
have identified a few related transcription factors (TFs) and
characterize their roles such as E2F1[1], Foxm1b [2] and hepatic
nuclear factors [3]. However, these findings still cannot fully explain
the underlying molecular mechanism during liver tumorigenesis.
It is necessary to systematically study the global gene regulatory
network (GRN) during HCC development.
The advancement of next-generation sequencing (NGS) technologies has enabled the rapid examination of the entire human genome. Many NGS applications have been developed to profile cells such as RNA-seq [4], ChIP-seq [5], ATAC-seq [6] etc. These allow scientists to study cells at different levels including genome, transcriptome, and epigenome. For instance, ChIP-seq and ATAC-seq data can be used to learn the chromatin states of certain biological processes by detecting regulatory elements in the genome and corresponding transcriptional regulators. Furthermore, recent developments in high-throughput single-cell technology provide the statistical power to study diverse population of tumor cells. This can greatly help scientists to understand intratumoral heterogeneity.
The advancement of next-generation sequencing (NGS) technologies has enabled the rapid examination of the entire human genome. Many NGS applications have been developed to profile cells such as RNA-seq [4], ChIP-seq [5], ATAC-seq [6] etc. These allow scientists to study cells at different levels including genome, transcriptome, and epigenome. For instance, ChIP-seq and ATAC-seq data can be used to learn the chromatin states of certain biological processes by detecting regulatory elements in the genome and corresponding transcriptional regulators. Furthermore, recent developments in high-throughput single-cell technology provide the statistical power to study diverse population of tumor cells. This can greatly help scientists to understand intratumoral heterogeneity.
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