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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
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.
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.
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