TOPDRIVER: the novel identifier of cancer driver genes in Gastric cancer and Melanoma

Seyed Mohammad Razavi,Changiz Eslahchi,Seyede Houri Razavi,Farzaneh Rami

doi:10.1007/s41109-019-0200-x

Seyed Mohammad Razavi, Changiz Eslahchi + Show 2 more

Open Access

https://doi.org/10.1007/s41109-019-0200-x

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Abstract

Nowadays, research has found a strong relationship between genomic status and occurrence of disease. Cancer is one of the most common diseases that leads to a high annual mortality rate worldwide, and the disease’s genetic content remains challenging. Detecting driver genes of different cancers could help in early diagnosis and treatment. In this paper, we proposed TOPDRIVER, a network-based algorithm, to detect cancer driver genes in cancers. An initial network was constructed by integrating four different omic datasets: HPRD, NCBI, KEGG, and GTEx. This integration created a gene similarity profile that provided a comprehensive perspective of gene interaction in each subtype of cancer and allocated weights to the edges of the network. The vertex scores were calculated using a gene-disease association dataset (DisGeNet) and a molecular functional disease similarity. In this step, the genes network was jagged and faced with a zero-one gap problem. A diffusion kernel was implemented to smooth the vertex scores to overcome this problem. Finally, potential driver genes were extracted according to the topology of the network, genes overall biological functions, and their involvement in cancer pathways. TOPDRIVER has been applied to two subtypes of gastric cancer and one subtype of melanoma. The method could nominate a considerable number of well-known driver genes of these cancers and also introduce novel driver genes. NKX3-1, KIDINS220, and RIPK4 have introduced for gastrointestinal cancer, UBA3, UBE2M, and RRAGA for hereditary gastric cancer and CIT for invasive melanoma. Biological evidences represents TOPDRIVER’s efficiency in a subtype-specific manner.

Highlights

Cancer is one of the most common multifactorial diseases (Ji et al 2010), which could be caused by environmental factors and/or genomic aberrations (Greenman et al 2007; Haverty et al 2008; Beroukhim et al 2007; Charames and Bapat 2003)
Several methods have been proposed to identify potential driver genes based on systematic integration of genome scale data of Copy number aberrations (CNA), gene expression profiles, protein-protein interaction, epigenetic, metabolism pathways, sequence similarity and Gene Ontology
We apply the method on two subtypes of gastric cancer and an invasive melanoma subtype

Summary

Introduction

Cancer is one of the most common multifactorial diseases (Ji et al 2010), which could be caused by environmental factors and/or genomic aberrations (e.g., sequence mutations, aberrant promoter methylation, and structural lesions such as gains/losses, inversions, and translocations) (Greenman et al 2007; Haverty et al 2008; Beroukhim et al 2007; Charames and Bapat 2003). These aberrations play critical roles in both initiation and progression of cancer. Distinguishing driver genes from passenger genes has been considered an important goal of cancer genome analysis and has a major impact on cancer prediction, diagnosis, and treatment, especially in the field of personalized medicine and therapy (Santarius et al 2010; Futreal et al 2004)

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