Breast cancer resistance protein (BCRP)/ATP-binding cassette subfamily G member 2 (ABCG2) mediates multidrug resistance (MDR) in breast cancers. In this study, we aimed to investigate the role of microRNAs in regulation of BCRP expression and BCRP-mediated drug resistance in breast cancer cells. Microarray analysis was performed to determine the differential expression patterns of miRNAs that target BCRP between the MX-resistant breast cancer cell line MCF-7/MX and its parental MX-sensitive cell line MCF-7. MiR-181a was found to be the most significantly down-regulated miRNA in MCF-7/MX cells. Luciferase activity assay showed that miR-181a mimics inhibited BCRP expression by targeting the 3' untranslated region (UTR) of the BCRP mRNA. Overexpression of miR-181a down-regulated BCRP expression, and sensitized MX-resistant MCF-7/MX cells to MX. In a nude mouse xenograft model, intratumoral injection of miR-181a mimics inhibited BCRP expression, and enhanced the antitumor activity of MX. In addition, miR-181a inhibitors up-regulated BCRP expression, and rendered MX-sensitive MCF-7 cells resistant to MX. These findings suggest that miR-181a regulates BCRP expression via binding to the 3'-UTR of BCRP mRNA. MiR-181a is critical for regulation of BCRP-mediated resistance to MX. MiR-181a may be a potential target for preventing and reversing drug resistance in breast cancer.

Background Ovarian cancer (OC) is a highly malignant disease with a poor prognosis and high recurrence rate. At present, there is no accurate strategy to predict the prognosis and recurrence of OC. The aim of this study was to identify gene-based signatures to predict OC prognosis and recurrence. Methods mRNA expression profiles and corresponding clinical information regarding OC were collected from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) and LASSO analysis were performed, and Kaplan–Meier curves, time-dependent ROC curves, and nomograms were constructed using R software and GraphPad Prism7. Results We first identified several key signalling pathways that affected ovarian tumorigenesis by GSEA. We then established a nine-gene-based signature for overall survival (OS) and a five-gene-based-signature for relapse-free survival (RFS) using LASSO Cox regression analysis of the TCGA dataset and validated the prognostic value of these signatures in independent GEO datasets. We also confirmed that these signatures were independent risk factors for OS and RFS by multivariate Cox analysis. Time-dependent ROC analysis showed that the AUC values for OS and RFS were 0.640, 0.663, 0.758, and 0.891, and 0.638, 0.722, 0.813, and 0.972 at 1, 3, 5, and 10 years, respectively. The results of the nomogram analysis demonstrated that combining two signatures with the TNM staging system and tumour status yielded better predictive ability. Conclusion In conclusion, the two-gene-based signatures established in this study may serve as novel and independent prognostic indicators for OS and RFS.

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