Cancer stem cells (CSCs) are subpopulations of tumor masses with unique abilities in self-renewal, stemness maintenance, drug resistance, and the promotion of cancer recurrence. Recent studies have suggested that breast CSCs play essential roles in chemoresistance. Therefore, new agents that selectively target such cells are urgently required. Reactive oxygen species (ROS)-producing enzymes are the reason for an elevated tumor oxidant status. The nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcriptional factor, which upon detecting cellular oxidative stress, binds to the promoter region of antioxidant genes. By triggering a cytoprotective response, Nrf2 maintains cellular redox status. Cripto-1 participates in the self-renewal of CSCs. Herein, luteolin, a flavonoid found in Taraxacum officinale extract, was determined to inhibit the expressions of stemness-related transcriptional factors, the ATP-binding cassette transporter G2 (ABCG2), CD44, aldehyde dehydrogenase 1 activity as well as the sphere formation properties of breast CSCs. Furthermore, luteolin suppressed the protein expressions of Nrf2, heme oxygenase 1 (HO-1), and Cripto-1 which have been determined to contribute critically to CSC features. The combination of luteolin and the chemotherapeutic drug, Taxol, resulted in enhanced cytotoxicity to breast cancer cells. These findings suggest that luteolin treatment significantly attenuated the hallmarks of breast cancer stemness by downregulating Nrf2-mediated expressions. Luteolin constitutes a potential agent for use in cancer stemness-targeted breast cancer treatments.

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Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.