Multilayer network analysis of miRNA and protein expression profiles in breast cancer patients

MiRNAs and proteins play important roles in different stages of tumor development and serve as biomarkers for the early diagnosis of 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 exclude unrelated miRNAs and proteins, and the most significant candidates were retained for the further analysis. Given these candidates’ possible functional relationships to one other, 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. Maximal information coefficient (MIC) was applied to assess intralayer and interlayer connection. The betweenness centrality was used as the first measurement of the importance of the nodes within each single layer. To further characterize the interlayer interaction between miRNAs and proteins, 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.