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Related Concept Videos

Cancer-Critical Genes I: Proto-oncogenes01:33

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Related Experiment Video

Updated: Dec 24, 2025

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
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Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and

Wu-Hsiung Wu1, Fan-Yu Li1, Yi-Chen Shu1

  • 1Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan.

Royal Society Open Science
|April 10, 2020
PubMed
Summary

Researchers developed a new computational method to identify cancer driver genes by analyzing metabolic reprogramming in cancer cells. This approach successfully predicted oncogenes, offering insights into cancer development and potential therapeutic targets.

Keywords:
cancer cell metabolismflux balance analysishead and neck squamous cell carcinomamultiple-level optimization

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Area of Science:

  • Computational biology
  • Cancer genomics
  • Metabolic engineering

Background:

  • Cancer cells exhibit distinct metabolic activity compared to normal cells due to genetic and epigenetic alterations.
  • Understanding cancer-specific metabolic reprogramming is crucial for identifying oncogenes.
  • Constraint-based modeling offers a potential approach to simulate metabolic changes and predict cancer drivers.

Purpose of the Study:

  • To develop and evaluate a novel computational framework for inferring oncogenes based on metabolic reprogramming.
  • To identify potential cancer driver genes by analyzing genome-scale metabolic networks of normal and cancer cells.
  • To validate the predictive power of the developed algorithm using a case study of head and neck squamous cells.

Main Methods:

  • Reconstruction of tissue-specific genome-scale metabolic network models for normal and cancer states using Recon 2.2 and the Human Protein Atlas.
  • Development of a tri-level optimization problem to infer oncogenes based on metabolic reprogramming templates.
  • Application of a nested hybrid differential evolution algorithm to solve the complex optimization problem.
  • Case study using head and neck squamous cells to evaluate the algorithm's performance.

Main Results:

  • The algorithm successfully identified 13 top-ranked one-hit dysregulations and 17 top-ranked two-hit oncogenes.
  • Inferred oncogenes showed high similarity ratios to metabolic reprogramming templates.
  • Most identified oncogenes are consistent with known cancer gene observations across various tissues.
  • Inferred oncogenes demonstrated significant connections to the TP53/AKT/IGF/MTOR signaling pathway via PTEN.

Conclusions:

  • The developed computational method effectively infers oncogenes by analyzing metabolic reprogramming.
  • The findings highlight the role of metabolic alterations in cancer development and provide a list of potential cancer driver genes.
  • The identified oncogenes' association with key signaling pathways underscores their importance in tumorigenesis and suggests potential therapeutic targets.