Lidocaine and Bupivacaine Downregulate MYB and DANCR lncRNA by Upregulating miR-187-5p in MCF-7 Cells.

Background: Breast cancer is the most common malignancy and a leading cause of death among women. The majority of patients require surgery, and retrospective studies have revealed an association between anaesthetic techniques during surgery and clinical outcomes. Local anaesthetics (LAs) influence carcinogenesis by interacting with non-coding RNAs (ncRNAs). However, the detailed mechanisms underlying the association between LAs and ncRNAs remain unclear. Methods: In this study, the effects of two commonly used LAs, lidocaine and bupivacaine, on the malignancy of MCF-7 breast cancer cells were investigated. The expression profiles of the microRNAs (miRNAs) that responded to treatment with LAs were determined through next-generation sequencing. Results: Data from the functional assay revealed that the LAs suppressed the proliferation of MCF-7 cells. The result of next-generation sequencing revealed that 131 miRNAs were upregulated, following treatment with the LAs. Validation using polymerase chain reaction (PCR) identified miR-187-5p as a potential biomarker, and it was selected for further analyses. Prediction with bioinformatics tools and luciferase reporter assays revealed that MYB is a direct target gene of miR-187-5p. Based on the hypothesis that lncRNAs acts as miRNA sponges, the target lncRNA, DANCR, of miR-187-5p was predicted using DIANA-LncBase v2 and validated using luciferase reporter assays. In addition, the reciprocal suppressive effect between DANCR and miR-187-5p was determined. Conclusions: This study suggests that one of the anti-tumour mechanisms of lidocaine and bupivacaine is mediated through the DANCR-miR-187-5p-MYB axis. This may provide a novel molecular mechanism of tumour suppression in breast cancer.

Deep Sequencing Reveals a MicroRNA Expression Signature in Triple-Negative Breast Cancer.

Deep sequencing is an advanced technology in genomic biology to detect the precise order of nucleotides in a strand of DNA/RNA molecule. The analysis of deep sequencing data also requires sophisticated knowledge in both computational software and bioinformatics. In this chapter, the procedures of deep sequencing analysis of microRNA (miRNA) transcriptome in triple-negative breast cancer and adjacent normal tissue are described in detail. As miRNAs are critical regulators of gene expression and many of them were previously reported to be associated with the malignant progression of human cancer, the analytical method that accurately identifies deregulated miRNAs in a specific type of cancer is thus important for the understanding of its tumor behavior. We obtained raw sequence reads of miRNA expression from 24 triple-negative breast cancers and 14 adjacent normal tissues using deep sequencing technology in this work. Expression data of miRNA reads were normalized with the quantile-quantile scaling method and were analyzed statistically. A miRNA expression signature composed of 25 differentially expressed miRNAs showed to be an effective classifier between triple-negative breast cancers and adjacent normal tissues in a hierarchical clustering analysis.

Deregulated microRNAs in triple-negative breast cancer revealed by deep sequencing.

BACKGROUND: MicroRNAs (miRNAs) are short, non-coding RNA molecules that play critical roles in human malignancy. However, the regulatory characteristics of miRNAs in triple-negative breast cancer, a phenotype of breast cancer that does not express the genes for estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2, are still poorly understood. METHODS: In this study, miRNA expression profiles of 24 triple-negative breast cancers and 14 adjacent normal tissues were analyzed using deep sequencing technology. Expression levels of miRNA reads were normalized with the quantile-quantile scaling method. Deregulated miRNAs in triple-negative breast cancer were identified from the sequencing data using the Student's t-test. Quantitative reverse transcription PCR validations were carried out to examine miRNA expression levels. Potential target candidates of a miRNA were predicted using published target prediction algorithms. Luciferase reporter assay experiments were performed to verify a putative miRNA-target relationship. Validated molecular targets of the deregulated miRNAs were retrieved from curated databases and their associations with cancer progression were discussed. RESULTS: A novel 25-miRNA expression signature was found to effectively distinguish triple-negative breast cancers from surrounding normal tissues in a hierarchical clustering analysis. We documented the evidence of seven polycistronic miRNA clusters preferentially harboring deregulated miRNAs in triple-negative breast cancer. Two of these miRNA clusters (miR-143-145 at 5q32 and miR-497-195 at 17p13.1) were markedly down-regulated in triple-negative breast cancer, while the other five miRNA clusters (miR-17-92 at 13q31.3, miR-183-182 at 7q32.2, miR-200-429 at 1p36.33, miR-301b-130b at 22q11.21, and miR-532-502 at Xp11.23) were up-regulated in triple-negative breast cancer. Moreover, miR-130b-5p from the miR-301b-130b cluster was shown to directly repress the cyclin G2 (CCNG2) gene, a crucial cell cycle regulator, in triple-negative breast cancer cells. Luciferase reporter assays showed that miR-130b-5p-mediated repression of CCNG2 was dependent on the sequence of the 3'-untranslated region. The findings described in this study implicate a miR-130b-5p-CCNG2 axis that may be involved in the malignant progression of triple-negative breast cancer. CONCLUSIONS: Our work delivers a clear picture of the global miRNA regulatory characteristics in triple-negative breast cancer and extends the current knowledge of microRNA regulatory network.

Molecular characteristics and metastasis predictor genes of triple-negative breast cancer: a clinical study of triple-negative breast carcinomas.

BACKGROUND: Triple-negative breast cancer is a subtype of breast cancer with aggressive tumor behavior and distinct disease etiology. Due to the lack of an effective targeted medicine, treatment options for triple-negative breast cancer are few and recurrence rates are high. Although various multi-gene prognostic markers have been proposed for the prediction of breast cancer outcome, most of them were proven clinically useful only for estrogen receptor-positive breast cancers. Reliable identification of triple-negative patients with a favorable prognosis is not yet possible. METHODOLOGY/PRINCIPAL FINDINGS: Clinicopathological information and microarray data from 157 invasive breast carcinomas were collected at National Taiwan University Hospital from 1995 to 2008. Gene expression data of 51 triple-negative and 106 luminal breast cancers were generated by oligonucleotide microarrays. Hierarchical clustering analysis revealed that the majority (94%) of triple-negative breast cancers were tightly clustered together carrying strong basal-like characteristics. A 45-gene prognostic signature giving 98% predictive accuracy in distant recurrence of our triple-negative patients was determined using the receiver operating characteristic analysis and leave-one-out cross validation. External validation of the prognostic signature in an independent microarray dataset of 59 early-stage triple-negative patients also obtained statistical significance (hazard ratio 2.29, 95% confidence interval (CI) 1.04-5.06, Cox P=0.04), outperforming five other published breast cancer prognostic signatures. The 45-gene signature identified in this study revealed that TGF-ß signaling of immune/inflammatory regulation may play an important role in distant metastatic invasion of triple-negative breast cancer. CONCLUSIONS/SIGNIFICANCE: Gene expression data and recurrence information of triple-negative breast cancer were collected and analyzed in this study. A novel set of 45-gene signature was found to be statistically predictive in disease recurrence of triple-negative breast cancer. The 45-gene signature, if further validated, may be a clinically useful tool in risk assessment of distant recurrence for early-stage triple-negative patients.