PUBLICATION

Transcriptomic Analyses in Zebrafish Cancer Models for Global Gene Expression and Pathway Discovery

Authors
Huang, X., Agrawal, I., Li, Z., Zheng, W., Lin, Q., Gong, Z.
ID
ZDB-PUB-160512-25
Date
2016
Source
Advances in experimental medicine and biology   916: 147-68 (Chapter)
Registered Authors
Gong, Zhiyuan, Li, Zhen
Keywords
Cancer, Embryonal rhabdomyosarcoma, Hepatocellular carcinoma, Leukemia, Melanoma, Microarray, Pathway, RNA-Seq, Transcriptome, Zebrafish
MeSH Terms
  • Animals
  • Disease Models, Animal*
  • Gene Expression*
  • Neoplasms/genetics*
  • Transcriptome*
  • Zebrafish
PubMed
27165353 Full text @ Adv. Exp. Med. Biol.
Abstract
The past decade has witnessed a remarkable advancement of the zebrafish model in cancer research. With the rapid development of genomic tools, it is increasingly feasible to perform genome-wide analyses to identify changes associated with cancer in a wide array of model organisms. These genomic tools, particularly transcriptomic analyses using DNA microarray and RNA sequencing platforms, have now become widely used in zebrafish cancer models to uncover novel biology and common molecular pathways underlying hepatocellular carcinoma, intrahepatic cholangiocarcinoma, melanoma, embryonal rhabdomyosarcoma (ERMS), T cell acute lymphoblastic leukemia (T-ALL), Ewing's sarcoma and glioma. An important finding from these studies is the high similarity and conservation of molecular pathways that underlie cancer in complementary zebrafish models and their human counterparts. Finally, these transcriptomic tools have also proven effective in the development and the validation of specific assays for chemical compound screening. In the future, other genomic tools, such as epigenetic, proteomic and metabolomic tools will likely be incorporated into zebrafish cancer studies, further refining our understanding of cancer.
Genes / Markers
Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping