Identifying cancer specific signaling pathways based on the dysregulation between genes. (December 2021)

• Record Type: Journal Article
• Authors: Li, Xiaohan; Zhang, Bing; Yu, Kequan; Bao, Zhenshen; Zhang, Weizhong; Bai, Yunfei
• Is Part Of: Computational biology and chemistry. Volume 95(2021)
• Publication Date: 2021-12
• Subjects: Dysregulation between genes -- Pathway analysis -- Node degree -- Cancer-type specific pathway; 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.2021.107586
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: A large collection of studies has shown that the occurrence of cancer is related to the functional dysfunction of the pathways. Identification of cancer-related pathways could help researchers understand the mechanisms of complex diseases well. Whereas, most current signaling pathway analysis methods take no account of the gene interaction variations within pathways. Furthermore, considering that some pathways have connection with two or more cancer types, while some are likely to be cancer-type specific pathways. Identifying cancer-type specific pathways contributes to interpreting the different mechanisms of different cancer types. In this study, we first proposed a pathway analysis method named Pathway Analysis of Intergenic Regulation (PAIGR) to identify pathways with dysregulation between genes and compared the performance of this method with four existing methods on four colorectal cancer (CRC) datasets. The results showed that PAIGR could find cancer-related pathways more accurately. Moreover, in order to explore the relationship between the identified pathways and the cancer type, we constructed a pathway interaction network, in which nodes and edges represented pathways and interactions between pathways respectively. Highly connected pathways were considered to play a central role in an extensive range of biological processes, while sparsely connected pathways are considered to have certain specificity. Our results showed that pathways identified by PAIGR had a low nodal degree (i.e., a few numbers of interactions), which suggested that most of these pathways were cancer-type specific. Graphical Abstract: ga1 Highlights: A novel approach for cancer-type specific signaling pathway identification. Identification of cancer-related pathways based on changes of intergenic interaction. Excluding hub pathways is beneficial for exploring cancer-type specific pathways.