In silico identification of oncogenic potential of fyn-related kinase in hepatocellular carcinoma

Jia Shing Chen, Wei Shiang Hung, Hsiang Han Chan, Shaw Jenq Tsai, H. Sunny Sun

Research output: Contribution to journalArticlepeer-review

104 Citations (Scopus)

Abstract

Motivation: Cancer development is a complex and heterogeneous process. It is estimated that 5-10% of human genes probably contribute to oncogenesis, whereas current experimentally validated cancer genes only cover 1% of the human genome. Thus hundreds of cancer genes may still remain to be identified. To search for new genes that play roles in carcinogenesis and facilitate cancer research, we developed a systematic workflow to use information saved in a previously established tumor-associated gene (TAG) database.Results: By exploiting the information of conserved protein domains from the TAG, we identified 183 potential new TAGs. As a proof-of-concept, one predicted oncogene, fyn-related kinase (FRK), which shows an aberrant digital expression pattern in liver cancer cells, was selected for further investigation. Using 68 paired hepatocellular carcinoma samples, we found that FRK was up-regulated in 52% of cases (P < 0.001). Tumorigenic assays performed in Hep3B and HepG2 cell lines revealed a significant correlation between the level of FRK expression and invasiveness, suggesting that FRK is a positive regulator of invasiveness in liver cancer cells.Conclusion: These findings implied that FRK is a multitalented signal transduction molecule that produces diverse biological responses in different cell types in various microenvironments. In addition, our data demonstrated the accuracy of computational prediction and suggested that other predicted TAGs can be potential targets for future cancer research.Availability: The TAG database is available online at the Bioinformatics Center website: http://www.binfo.ncku.edu.tw/TAG/.

Original languageEnglish
Pages (from-to)420-427
Number of pages8
JournalBioinformatics
Volume29
Issue number4
DOIs
Publication statusPublished - 2013 Feb 15

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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