An efficient strategy for identifying cancer-related key genes based on graph entropy. (June 2018)

• Record Type: Journal Article
• Authors: Zhang, Wei; Wang, Shu-Lin
• Is Part Of: Computational biology and chemistry. Volume 74(2018)
• Page Start: 142
• Page End: 148
• Publication Date: 2018-06
• Subjects: Cancer-related key genes -- Gene network -- Parallel maximal cliques -- Graph entropy; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.03.022
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Use the algorithm of parallel maximal cliques for quickly obtaining a series of subgraphs. A novel metric is introduced by combining graph entropy and the influence of upstream gene mutations information. The strategy is effective and easy to identify new genes that can be used as potential markers for prognosis prediction. Abstract: Gene networks are beneficial to identify functional genes that are highly relevant to clinical outcomes. Most of the current methods require information about the interaction of genes or proteins to construct genetic network connection. However, the conclusion of these methods may be bias because of the current incompleteness of human interactome. In this paper, we propose an efficient strategy to use gene expression data and gene mutation data for identifying cancer-related key genes based on graph entropy (iKGGE). Firstly, we construct a gene network using only gene expression data based on the sparse inverse covariance matrix, then, cluster genes use the algorithm of parallel maximal cliques for quickly obtaining a series of subgraphs, and at last, we introduce a novel metric that combine graph entropy and the influence of upstream gene mutations information to measure the impact factors of genes. Testing of the three available cancer datasets shows that our strategy can effectively extract key genes that may play distinct roles in tumorigenesis, and the cancer patient risk groups are well predicted based on key genes.