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 (

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.

CircRNA circABCB1 diminishes the sensitivity of breast cancer cells to docetaxel by sponging miR-153-3p