Prioritizing breast cancer subtype related miRNAs using miRNA-mRNA dysregulated relationships extracted from their dual expression profiling

Abstract

Identification of miRNA expression-based breast cancer subtypes is considered a critical means of prognostication. So far, the studies on breast cancer subtypes have not been well characterized, and few studies have performed expression profiling of both miRNA and mRNA from the same breast cancer subtypes samples. In this study we analyzed dual expression profiling data of miRNA and mRNA derived from the expression profiling of 489 miRNAs in 41 luminal-A breast tumors samples and 15 basal-like samples. We defined a correlation coefficient ratio (CCR) and used it to examine the correlative dysregulated relationships between miRNAs and mRNAs. A miRNA-mRNA dysregulated network was arisen from 6222 dysregulated relationships, and from this network, miRNA-miRNA networks specialized for luminal-A and basal-like breast cancer subtypes were extracted according to the CCR values. By analyzing the networks, we found that luminal-A trend and basal-like trend miRNA-miRNA network displayed a change in hubs which connected the most miRNAs, and therefore become the potential breast cancer subtype related miRNAs. In addition, we also used other network analysis methods for miRNA expression profiling data, such as weighted correlation network analysis (WGCNA), Bayesian network analysis, and miRNA similarity (MISIM) analysis to validate the identified miRNAs or miRNA clusters. This study provides a new analyzing method to predict candidate miRNAs of breast cancer subtype from a system biology level and help understanding the relationship between miRNA and mRNA in primary breast cancer subtype.