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Cancer therapy is a strategic measure in inhibiting breast cancer stem cell (BCSC) pathways. Naringenin, a citrus flavonoid, was found to increase breast cancer cells’ sensitivity to chemotherapeutic agents. Bioinformatics study and 3D tumorsphere in vitro modeling in breast cancer (mammosphere) were used in this study, which aims to explore the potential therapeutic targets of naringenin (PTTNs) in inhibiting BCSCs. Bioinformatic analyses identified direct target proteins (DTPs), indirect target proteins (ITPs), naringenin-mediated proteins (NMPs), BCSC regulatory genes, and PTTNs. The PTTNs were further analyzed for gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein–protein interaction (PPI) networks, and hub protein selection. Mammospheres were cultured in serum-free media. The effects of naringenin were measured by MTT-based cytotoxicity, mammosphere forming potential (MFP), colony formation, scratch wound-healing assay, and flow cytometry-based cell cycle analyses and apoptosis assays. Gene expression analysis was performed using real-time quantitative polymerase chain reaction (q-RT PCR). Bioinformatics analysis revealed p53 and estrogen receptor alpha (ERα) as PTTNs, and KEGG pathway enrichment analysis revealed that TGF-ß and Wnt/ß-catenin pathways are regulated by PTTNs. Naringenin demonstrated cytotoxicity and inhibited mammosphere and colony formation, migration, and epithelial to mesenchymal transition in the mammosphere. The mRNA of tumor suppressors P53 and ERα were downregulated in the mammosphere, but were significantly upregulated upon naringenin treatment. By modulating the P53 and ERα mRNA, naringenin has the potential of inhibiting BCSCs. Further studies on the molecular mechanism and formulation of naringenin in BCSCs would be beneficial for its development as a BCSC-targeting drug.

Highlights • Euc decreased expression of p-STAT3 (Tyr705), nuclear translocation and DNA-binding. • Euc inhibited cell viability, metastasis and BCSC-like traits by inhibiting STAT3. • Euc covalently interacted with STAT3 through a Michael addition reaction. • Euc exhibits potent anti-tumor growth and lung metastasis without acute toxicity., Signal transducer and activator of transcription 3 (STAT3) is an important therapeutic target to triple negative breast cancer (TNBC) treatment. In the present study, we aim to investigate the potential activity of Eucannabinolide (Euc), a novel sesquiterpene lactone separated from Eupatorium cannabinum Linn. against TNBC by targeting STAT3 and expect that Euc will be developed as an inhibitor of STAT3 in the treatment of TNBC. We found that Euc effectively suppressed STAT3 activation at tyrosine 705, inhibited its translocation to nucleus, and decreased its DNA binding capacity. Moreover, introduction of STAT3-short hairpin RNAs or STAT3 inhibitor S3I-201 attenuates the Euc-induced inhibition of cell viability. And, Euc inhibited cell viability, proliferation, metastasis and breast cancer stem cell-like traits but did not induce cytotoxicity in human mammary epithelial cells. The in vivo study similarly demonstrated that administration of Euc inhibited the growth of xenograft tumors and impaired tumor metastasis of a lung metastasis model. The above phenomena were associated with STAT3 dysfunction induced by Euc. In conclusion, Euc elicits the effects of anti-proliferation, anti-metastasis and anti-breast cancer stem cell-like traits in TNBC via targeting STAT3. These data highlight that development of Euc as a STAT3 inhibitor may offer a promising therapeutic strategy for TNBC.

Highlights • Biology-driven strategy can be used in development of prognostic model. • The BCSC-associated miRNA classifier can predict prognosis for HR + HER2 − breast cancer. • The BCSC-associated miRNA classifier outperforms IHC4 scoring and 21-gene RS. • Chemotherapy can improve DRFS in patients predicted as high-risk. Breast cancer patients with high proportion of cancer stem cells (BCSCs) have poor clinical outcomes. MiRNAs regulate key features of BCSCs as oncogenes or tumor suppressors. Although hormone receptor (HR)-positive, HER2-negative breast cancers are the most common subtype, current methods are inadequate to predict its clinical outcome. In this multicenter study, we identified and validated a 10 BCSC-associated-miRNA classifier that can predict survival for HR + HER2 − patients. Retrospective analysis showed that this classifier outperformed IHC4 scoring and 21-gene Recurrence Score (RS), and chemotherapy could improve survival in high-risk patients determined by this classifier. This model may facilitate personalized clinical decision for HR + HER2 − individuals., Purpose Breast cancer patients with high proportion of cancer stem cells (BCSCs) have unfavorable clinical outcomes. MicroRNAs (miRNAs) regulate key features of BCSCs. We hypothesized that a biology-driven model based on BCSC-associated miRNAs could predict prognosis for the most common subtype, hormone receptor (HR)-positive, HER2-negative breast cancer patients. Patients and Methods After screening candidate miRNAs based on literature review and a pilot study, we built a miRNA-based classifier using LASSO Cox regression method in the training group (n = 202) and validated its prognostic accuracy in an internal (n = 101) and two external validation groups (n = 308). Results In this multicenter study, a 10-miRNA classifier incorporating miR-21, miR-30c, miR-181a, miR-181c, miR-125b, miR-7, miR-200a, miR-135b, miR-22 and miR-200c was developed to predict distant relapse free survival (DRFS). With this classifier, HR + HER2 − patients were scored and classified into high-risk and low-risk disease recurrence, which was significantly associated with 5-year DRFS of the patients. Moreover, this classifier outperformed traditional clinicopathological risk factors, IHC4 scoring and 21-gene Recurrence Score (RS). The patients with high-risk recurrence determined by this classifier benefit more from chemotherapy. Conclusions Our 10-miRNA-based classifier provides a reliable prognostic model for disease recurrence in HR + HER2 − breast cancer patients. This model may facilitate personalized therapy-decision making for HR + HER2 − individuals.