title: Signalling pathway impact analysis based on the strength of interaction between genes.
abstract: Signalling pathway analysis is a popular approach that is used to identify significant cancer-related pathways based on differentially expressed genes (DEGs) from biological experiments. The main advantage of signalling pathway analysis lies in the fact that it assesses both the number of DEGs and the propagation of signal perturbation in signalling pathways. However, this method simplifies the interactions between genes by categorising them only as activation (+1) and suppression (-1), which does not encompass the range of interactions in real pathways, where interaction strength between genes may vary. In this study, the authors used newly developed signalling pathway impact analysis (SPIA) methods, SPIA based on Pearson correlation coefficient (PSPIA), and mutual information (MSPIA), to measure the interaction strength between pairs of genes. In analyses of a colorectal cancer dataset, a lung cancer dataset, and a pancreatic cancer dataset, PSPIA and MSPIA identified more candidate cancer-related pathways than were identified by SPIA. Generally, MSPIA performed better than PSPIA.
authors.affiliation: Department of Physics and Electronic information engineering, Wenzhou University, Wenzhou, Zhejiang, People's Republic of China.
authors.forename: Zhenshen
authors.initials: Z
authors.lastname: Bao
authors.affiliation: Department of Physics and Electronic information engineering, Wenzhou University, Wenzhou, Zhejiang, People's Republic of China.
authors.forename: Xianbin
authors.initials: X
authors.lastname: Li
authors.affiliation: College of Information engineering, Wenzhou University, Wenzhou, Zhejiang, People's Republic of China.
authors.forename: Xiangzhen
authors.initials: X
authors.lastname: Zan
authors.affiliation: College of Information engineering, Wenzhou University, Wenzhou, Zhejiang, People's Republic of China.
authors.forename: Liangzhong
authors.initials: L
authors.lastname: Shen
authors.affiliation: Telecommunication Engineering Institute, Air Force Engineering University, Xi'an, People's Republic of China.
authors.forename: Runnian
authors.initials: R
authors.lastname: Ma
authors.affiliation: Department of Physics and Electronic information engineering, Wenzhou University, Wenzhou, Zhejiang, People's Republic of China. wbliu6910@126.com.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Liu
mesh_terms: D015179:Colorectal Neoplasms / Q000235:genetics*; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D010190:Pancreatic Neoplasms / Q000235:genetics*; D015398:Signal Transduction