Meta-microRNA Biomarker Signatures to Classify Breast Cancer Subtypes

Breast cancer is one of the leading causes of morbidity and mortality that is in need of novel diagnostics and therapeutics. Meta-analysis of microarray data offers promise to combine studies and provide more robust results. We report here a molecular classification of pathological subtypes (estrogen receptor [ER], progesterone receptor [PR], and Human Epidermal Growth Factor Receptor 2 [HER2]) of breast cancers with microRNA (miRNA)-dependent signatures. A ranking-based meta-analysis approach was applied to eight independent microarray data sets and meta-miRNA lists were obtained that are specific to each breast cancer subtype. The comparison of the lists with miRCancer and the PhenomiR 2.0 databases pointed out nine prominent miRNAs: let-7b-5p, let-7c-5p, let-7e-5p, miR-130a-3p, miR-30a-5p, miR-92a-1-5p, miR-211-5p, miR-500a-3p, and miR-516b-3p. Further analysis conducted with the TCGA data showed that these miRNAs can differentiate tumors from normal samples as well as discriminate the molecular subtypes of breast cancer. According to the PAM50 classification, three of these miRNAs (let-7b-5p, let-7c-5p, and miR-30a-5p) downregulated significantly, whereas miR-130a-3p, miR-92a-1-5p, miR-211-5p, and miR-500a-3p upregulated in tumors from the luminal A to the basal-like subtypes. When the prominent meta-miRNAs and their targets were analyzed, they appeared to be taking part in important signaling pathways in cancer such as the PI3K-Akt signaling and the p53 signaling pathways. Furthermore, the regulatory genes, which are key players for ER, PR, and ErBb signaling pathways, were found to be under control of several meta-miRNAs. These meta-miRNAs and the genes they are regulating offer new promise for future translational research and potential targets for precision medicine diagnostics.