CircRNA CircABCB1 Diminishes the Sensitivity of Breast Cancer Cells to Docetaxel by Sponging MiR-153-3p.

Resistance to docetaxel is a major problem to the success of docetaxel-based therapies for breast cancer. The present study was to identify the role of circABCB1 in altering the docetaxel resistance properties. Reverse transcription-quantitative PCR (qRT-PCR) was performed to quantify circABCB1 and miR-153-3p. The effects of circABCB1 on the viability, apoptosis and migration/invasion of docetaxel-resistant and -sensitive cells were investigated by cell function experiments, including Cell Counting Kit-8 and Transwell assays. Correlation between circABCB1 and the docetaxel-treated outcome was analyzed by multivariate Cox regression analysis, in addition to Kaplan-Meier analysis of time to treatment failure (TTF). The targeting relationship between circABCB1 and miR-153-3p was predicted and verified by dual-luciferase reporter assay and RNA immunoprecipitation. CircABCB1 was highly expressed in cancerous tissues, as well as the docetaxel-sensitive group and cells. The overexpression of circABCB1 contributed to cell viability, docetaxel-resistance and migration/invasion, but inhibited apoptosis. CircABCB1 can sponge miR-153-3p. CircABCB1 contributed to the docetaxel resistance of breast cancer, maybe via the miR-153-3p.

circRNA_0001006 predicts prognosis and regulates cellular processes of triple-negative breast cancer via miR-424-5p.

BACKGROUND: circular RNAs (circRNAs) have been considered novel biomarker candidates for human cancers, such as triple-negative breast cancer (TNBC). circ_0001006 was identified as a differentially expressed circRNA in metastatic breast cancer, but its significance and function in TNBC were unclear. The significance of circ_0001006 in TNBC was assessed and exploring its potential molecular mechanism to provide a therapeutic target for TNBC. RESULTS: circ_0001006 showed significant upregulation in TNBC and close association with patients' histological grade, Ki67 level, and TNM stage. Upregulated circ_0001006 could predict a worse prognosis and high risk of TNBC patients. In TNBC cells, silencing circ_0001006 suppressed cell proliferation, migration, and invasion. In mechanism, circ_0001006 could negatively regulate miR-424-5p, which mediated the inhibition of cellular processes by circ_0001006 knockdown. CONCLUSIONS: Upregulated circ_0001006 in TNBC served as a poor prognosis predictor and tumor promoter via negatively regulating miR-424-5p.

Value of genomics- and radiomics-based machine learning models in the identification of breast cancer molecular subtypes: a systematic review and meta-analysis.

Background: In the era of precision therapy, early classification of breast cancer (BRCA) molecular subtypes has clinical significance for disease management and prognosis. We explored the accuracy of machine learning (ML) models for early classification of BRCA molecular subtypes through a systematic review of the literature currently available. Methods: We retrieved relevant studies published in PubMed, EMBASE, Cochrane, and Web of Science until 15 April 2022. A prediction model risk of bias assessment tool (PROBAST) was applied for the assessment of risk of bias of a genomics-based ML model, and the Radiomics Quality Score (RQS) was simultaneously used to evaluate the quality of this radiomics-based ML model. A random effects model was adopted to analyze the predictive accuracy of genomics-based ML and radiomics-based ML for Luminal A, Luminal B, Basal-like or triple-negative breast cancer (TNBC), and human epidermal growth factor receptor 2 (HER2). The PROSPERO of our study was prospectively registered (CRD42022333611). Results: Of the 38 studies were selected for analysis, 14 ML models were based on gene-transcriptomic, with only 4 external validations; and 43 ML models were based on radiomics, with only 14 external validations. Meta-analysis results showed that c-statistic values of the ML based on radiomics for the identification of BRCA molecular subtypes Luminal A, Luminal B, Basal-like or TNBC, and HER2 were 0.76 [95% confidence interval (CI): 0.60-0.96], 0.78 (95% CI: 0.69-0.87), 0.89 (95% CI: 0.83-0.91), and 0.83 (95% CI: 0.81-0.86), respectively. The c-statistic values of ML based on the gene-transcriptomic analysis cohort for the identification of the previously described BRCA molecular subtypes were 0.96 (95% CI: 0.93-0.99), 0.96 (95% CI: 0.93-0.99), 0.98 (95% CI: 0.95-1.00), and 0.97 (95% CI: 0.96-0.98) respectively. Additionally, the sensitivity of the ML model based on radiomics for each molecular subtype ranged from 0.79 to 0.85, while the sensitivity of the ML model based on gene-transcriptomic was between 0.92 and 0.99. Conclusions: Both radiomics and gene transcriptomics produced ideal effects on BRCA molecular subtype prediction. Compared with radiomics, gene transcriptomics yielded better prediction results, but radiomics was simpler and more convenient from a clinical point of view.