Open AccessMultilayer network analysis of miRNA and protein expression profiles in breast cancer patients
Author(s) -
Yang Zhang,
Jiannan Chen,
Yu Wang,
Dehua Wang,
Weihui Cong,
Bo Shiun Lai,
Yingzhong Zhao
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0202311
Subject(s) - betweenness centrality , breast cancer , microrna , computational biology , cancer , network analysis , bioinformatics , computer science , centrality , complex network , biology , mathematics , gene , genetics , physics , statistics , quantum mechanics , world wide web
MiRNAs and proteins play important roles in different stages of breast tumor development and serve as biomarkers for the early diagnosis of breast cancer. A new algorithm that combines machine learning algorithms and multilayer complex network analysis is hereby proposed to explore the potential diagnostic values of miRNAs and proteins. XGBoost and random forest algorithms were employed to screen the most important miRNAs and proteins. Maximal information coefficient was applied to assess intralayer and interlayer connection. A multilayer complex network was constructed to identify miRNAs and proteins that could serve as biomarkers for breast cancer. Proteins and miRNAs that are nodes in the network were subsequently categorized into two network layers considering their distinct functions. The betweenness centrality was used as the first measurement of the importance of the nodes within each single layer. The degree of the nodes was chosen as the second measurement to map their signalling pathways. By combining these two measurements into one score and comparing the difference of the same candidate between normal tissue and cancer tissue, this novel multilayer network analysis could be applied to successfully identify molecules associated with breast cancer.