Understanding the mechanisms of cancers based on function sub-pathways. (February 2019)

• Record Type: Journal Article
• Authors: Liu, Wenbin; Xu, Peng; Bao, Zhenshen
• Is Part Of: Computational biology and chemistry. Volume 78(2019)
• Page Start: 491
• Page End: 496
• Publication Date: 2019-02
• Subjects: Signaling pathway analysis -- Biological functions -- Perturbation degree -- Differentially expressed genes; 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.2018.11.011
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Pathway analysis has become a popular technology tool for gaining insight into the underlying biology of differentially expressed genes and proteins. Although many sub-pathways analysis methods have been proposed, the function of these sub-pathways is generally implicit. In this paper, we propose a function sub-pathway analysis (FSPA) method which includes all nodes reaching a specific function node at the downstream of pathways. The perturbation degree of a sub-pathway is defined as the negative of the log p-value of the sub-pathway. The proposed FSPA allows analyzing the differentially expressed genes in a sub-pathway with diseases in explicit function level. Results from six datasets of colorectal cancer, lung cancer and pancreatic cancer show that the proposed FSPA could identify more cancer associated pathways. And more importantly, it could identify which sub-pathways lead to a specific abnormal function, and to what extent it affects the function. Furthermore, the proposed perturbation degree could also analyze the imbalance of some functions involved in some biological process. The results by FSPA are helpful for elucidating the underlying mechanisms of cancers and designing therapeutic strategies.