title: LPRP: A Gene-Gene Interaction Network Construction Algorithm and Its Application in Breast Cancer Data Analysis.
abstract: The importance of the construction of gene-gene interaction (GGI) network to better understand breast cancer has previously been highlighted. In this study, we propose a novel GGI network construction method called linear and probabilistic relations prediction (LPRP) and used it for gaining system level insight into breast cancer mechanisms. We construct separate genome-wide GGI networks for tumor and normal breast samples, respectively, by applying LPRP on their gene expression datasets profiled by The Cancer Genome Atlas. According to our analysis, a large loss of gene interactions in the tumor GGI network was observed (7436; 88.7 % reduction), which also contained fewer functional genes (4757; 32 % reduction) than the normal network. Tumor GGI network was characterized by a bigger network diameter and a longer characteristic path length but a smaller clustering coefficient and much sparse network connections. In addition, many known cancer pathways, especially immune response pathways, are enriched by genes in the tumor GGI network. Furthermore, potential cancer genes are filtered in this study, which may act as drugs targeting genes. These findings will allow for a better understanding of breast cancer mechanisms.
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, 130012, China.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China.
authors.forename: Lingtao
authors.identifier: http://orcid.org/0000-0002-9156-8763
authors.initials: L
authors.lastname: Su
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, 130012, China. sulingtao@163.com.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China. sulingtao@163.com.
authors.forename: Xiangyu
authors.initials: X
authors.lastname: Meng
authors.affiliation: The First Clinical Hospital of Jilin University, Changchun, 130021, China.
authors.forename: Qingshan
authors.initials: Q
authors.lastname: Ma
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, 130012, China.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China.
authors.forename: Tian
authors.initials: T
authors.lastname: Bai
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, 130012, China. liugx@jlu.edu.cn.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China. liugx@jlu.edu.cn.
authors.forename: Guixia
authors.initials: G
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics*; D016000:Cluster Analysis; D015536:Down-Regulation / Q000235:genetics; D004843:Epistasis, Genetic; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D058977:Molecular Sequence Annotation; D012984:Software; D013223:Statistics as Topic; D015854:Up-Regulation / Q000235:genetics
keywords: Breast cancer; Gene–gene interaction; Network construction; TCGA dataset