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DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph.

Chenye Wang1, Junhan Shi1, Jiansheng Cai2

  • 1School of Mathematics and Statistics, Shandong University, Weihai, 264209, China.

BMC Bioinformatics
|July 13, 2022
PubMed
Summary

DriverRWH, a new random walk algorithm, effectively identifies cancer driver genes by utilizing mutation co-occurrence data. This method significantly improves upon existing tools, aiding in the development of targeted cancer therapies.

Keywords:
Cancer driver genesCandidate gene prioritizationGene networkHypergraph modelRandom walkSomatic mutation

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Next-generation sequencing generates vast cancer genomic data.
  • Identifying cancer driver genes is crucial but challenging.
  • Existing methods often underutilize co-mutation data, leading to false positives.

Purpose of the Study:

  • To develop a novel algorithm for prioritizing cancer driver genes.
  • To leverage co-mutation information for improved accuracy.
  • To enhance the identification of genes driving tumor growth.

Main Methods:

  • Developed DriverRWH, a random walk algorithm.
  • Utilized a weighted gene mutation hypergraph model.
  • Integrated somatic mutation and molecular interaction network data.

Main Results:

  • DriverRWH outperforms state-of-the-art methods in prioritizing cancer driver genes.
  • Achieved high area under the curve scores and recovered known drivers effectively.
  • Identified potential novel driver genes enriched in cancer-related pathways.
  • Demonstrated robustness to data perturbations.

Conclusions:

  • DriverRWH is an effective tool for prioritizing cancer driver genes across various cancer types.
  • Offers improved precision and sensitivity compared to existing tools.
  • Facilitates the detection of potential driver genes for targeted cancer therapies.