LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network

BMC Bioinformatics

Research

Chun-Hou Zheng¹  Di Zhang¹  Pi-Jing Wei²  Junfeng Xia³ 

[1] College of Computer Science and Technology, Anhui University, 230601, Hefei, Anhui, China;College of Electrical Engineering and Automation, Anhui University, 230601, Hefei, Anhui, China;Institute of Health Sciences, Anhui University, 230601, Hefei, Anhui, China;

关键词: Cancer;    Driver genes;    Mutation data;    Expression data;    Interaction network;   

DOI  :  10.1186/s12859-016-1332-y

来源: Springer

【 摘 要 】

BackgroundCancer is a complex disease which is characterized by the accumulation of genetic alterations during the patient’s lifetime. With the development of the next-generation sequencing technology, multiple omics data, such as cancer genomic, epigenomic and transcriptomic data etc., can be measured from each individual. Correspondingly, one of the key challenges is to pinpoint functional driver mutations or pathways, which contributes to tumorigenesis, from millions of functional neutral passenger mutations.ResultsIn this paper, in order to identify driver genes effectively, we applied a generalized additive model to mutation profiles to filter genes with long length and constructed a new gene-gene interaction network. Then we integrated the mutation data and expression data into the gene-gene interaction network. Lastly, greedy algorithm was used to prioritize candidate driver genes from the integrated data. We named the proposed method Length-Net-Driver (LNDriver).ConclusionsExperiments on three TCGA datasets, i.e., head and neck squamous cell carcinoma, kidney renal clear cell carcinoma and thyroid carcinoma, demonstrated that the proposed method was effective. Also, it can identify not only frequently mutated drivers, but also rare candidate driver genes.

【 授权许可】

CC BY   
© The Author(s). 2016

【 图 表 】

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