Background: MicroRNAs (miRNAs) are small non-coding RNAs involved in post-transcriptional gene expression\nregulation and have been described as key regulators of carcinogenesis. Aberrant miRNA expression has been\nfrequently reported in sporadic breast cancers, but few studies have focused on profiling hereditary breast cancers.\nIn this study, we aimed to identify specific miRNA signatures in hereditary breast tumors and to compare with\nsporadic breast cancer and normal breast tissues.\nMethods: Global miRNA expression profiling using NanoString technology was performed on 43 hereditary breast\ntumors (15 BRCA1, 14 BRCA2, and 14 BRCAX), 23 sporadic breast tumors and 8 normal breast tissues. These normal\nbreast tissues derived from BRCA1- and BRCA2- mutation carriers (n = 5) and non-mutation carriers (n = 3).\nSubsequently, we performed receiver operating characteristic (ROC) curve analyses to evaluate the diagnostic\nperformance of differentially expressed miRNAs. Putative target genes of each miRNAs considered as potential\nbiomarkers were identified using miRDIP platform and used for pathway enrichment analysis.\nResults: miRNA expression analyses identified several profiles that were specific to hereditary breast cancers. A total\nof 25 miRNAs were found to be differentially expressed (fold change: > 2.0 and p < 0.05) and considered as\npotential biomarkers (area under the curve > 0.75) in hereditary breast tumors compared to normal breast tissues,\nwith an expressive upregulation among BRCAX cases. Furthermore, bioinformatic analysis revealed that these\nmiRNAs shared target genes involved in ErbB, FoxO, and PI3K-Akt signaling pathways.\nConclusions: Our results showed that miRNA expression profiling can differentiate hereditary from sporadic breast\ntumors and normal breast tissues. These miRNAs were remarkably deregulated in BRCAX hereditary breast cancers.\nTherefore, miRNA signatures can be used as potential novel diagnostic biomarkers for the prediction of BRCA1/2-\ngermline mutations and may be useful for future clinical management.
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