Growing evidence indicates that
microRNAs (
miRNAs) play critical roles in the initiation and progression of
breast carcinoma (BC) and are promising diagnostic
biomarkers. In the present study, we aimed to identify a multi-marker
miRNA pool with high diagnostic performance for BC. We collected
miRNA expression profiles of BC samples and normal breast tissues from The
Cancer Genome Atlas (TCGA) and screened differentially expressed
miRNAs by conducting two‑sample t-tests and by calculating log2 fold-change (log2FC) ratios. Statistical significance was established at p<0.001 and |log2FC| >1. Then, we generated receiver operating characteristic (ROC) curves, calculated the area under the curve (AUC) with a 95% confidence interval (95% CI), and calculated the diagnostic sensitivity and specificity using MedCalc software. Additionally, we predicted the targets of candidate
miRNAs using 10 online databases: TarBase, miRTarBase, TargetScan, TargetMiner,
microRNA.org, RNA22, PicTar-vert, miRDB, PITA and PolymiRTS. Target genes that were predicted by at least four algorithms were chosen, and cooperative targets of multiple
miRNAs were further selected for GO and KEGG pathway analyses through the DAVID online tool. Eventually, a total of 66 differentially expressed
miRNAs were identified after
miRNA expression profiles were analyzed in BC and normal breast samples. Of these, we selected nine dysregulated
miRNAs as candidate diagnostic markers: seven upregulated
miRNAs (hsa-miR-21, hsa-miR-96, hsa-miR-183, hsa-miR‑182, hsa-miR-141, hsa-miR-200a and hsa-miR-429) and two downregulated
miRNAs (hsa-miR-139 and hsa-miR‑145). The ROC curve for the combination of these nine differently expressed
miRNAs showed extremely high diagnostic accuracy, with an AUC of 0.995 (95% CI, 0.988‑0.999) and diagnostic sensitivity and specificity of 98.7 and 98.9%, respectively. In conclusion, the combination of these nine
miRNAs significantly improved the accuracy of
breast cancer diagnosis.