The use of gene interaction networks to improve the identification of cancer driver genes

PeerJ

article

Emilie Ramsahai¹  Kheston Walkins²  Vrijesh Tripathi¹  Melford John² 

[1] Department of Mathematics & Statistics, The Faculty of Science and Technology, The University of the West Indies, St. Augustine Campus;Department of Preclinical Sciences, The University of the West Indies

关键词: Driver genes;    Interaction network;    Algorithm;    Gene expression;    Mutation;    Weighted network;    Cancer;    Graph;   

DOI  :  10.7717/peerj.2568

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.

【 授权许可】

CC BY