Breast cancer is the leading cause of female mortality worldwide. Although there are several modern treatments for breast cancer, there is a high rate of recurrence for the majority of treatments; therefore, the search for effective anticancer agents continues. The present study aimed to investigate the anti-breast cancer potential of frullanolide, a compound which is isolated and purified from the Grangea maderaspatana plant, for selected human breast cancer cell lines (MCF-7, MDA-MB-468 and MDA-MB-231). The MTT assay was used to assess cytotoxic activity in breast cancer cell lines of treatment with frullanolide at 1.25, 2.5, 5.0, 10.0 and 20.0 µg/ml. Additionally, the apoptotic induction ability of frullanolide at various concentrations [0.5x, 1x and 2x half maximal inhibitory concentration (IC 50)] was investigated by flow cytometry and western blot analysis. Frullanolide exhibited strong anti-breast cancer activity against MDA-MB-468 (IC 50 , 8.04±2.69 µg/ml) and weak cytotoxicity against the MCF-7 (IC 50 , 10.74±0.86 µg/ml) and MDA-MB-231 (IC 50 , 12.36±0.31 µg/ml) cell lines. The IC 50 of frullanolide was high in the human normal epithelial breast cell line (MCF-12A) and mouse fibroblast cell line (L-929). Density plot diagrams revealed that frullanolide induced apoptosis in MCF-7, MDA-MB-468 and MDA-MB-231 cells. Notably, a plausible anticancer mechanism was elucidated via cellular apoptosis by p53-independence in the treated MCF-7 cell line and p53-dependence in the treated MDA-MB-468 and MDA-MB-231 cell lines. In conclusion, the present study demonstrated that frullanolide may exert anticancer activity on breast cancer cell lines by inducing apoptosis. Frullanolide offers a possible novel approach to breast cancer therapy.

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Triple negative breast cancer (TNBC) lacks well-defined molecular targets and is highly heterogenous, making treatment challenging. Using gene expression analysis, TNBC has been classified into four different subtypes: basal-like immune-activated (BLIA), basal-like immune-suppressed (BLIS), mesenchymal (MES), and luminal androgen receptor (LAR). However, there is currently no standardized method for classifying TNBC subtypes. We attempted to define a gene signature for each subtype, and to develop a classification method based on machine learning (ML) for TNBC subtyping. In these experiments, gene expression microarray data for TNBC patients were downloaded from the Gene Expression Omnibus database. Differentially expressed genes unique to 198 known TNBC cases were identified and selected as a training gene set to train in seven different classification models. We produced a training set consisting of 719 DEGs selected from uniquely expressed genes of all four subtypes. The highest average accuracy of classification of the BLIA, BLIS, MES, and LAR subtypes was achieved by the SVM algorithm (accuracy 95–98.8%; AUC 0.99–1.00). For model validation, we used 334 samples of unknown TNBC subtypes, of which 97 (29.04%), 73 (21.86%), 39 (11.68%) and 59 (17.66%) were predicted to be BLIA, BLIS, MES, and LAR, respectively. However, 66 TNBC samples (19.76%) could not be assigned to any subtype. These samples contained only three upregulated genes (EN1, PROM1, and CCL2). Each TNBC subtype had a unique gene expression pattern, which was confirmed by identification of DEGs and pathway analysis. These results indicated that our training gene set was suitable for development of classification models, and that the SVM algorithm could classify TNBC into four unique subtypes. Accurate and consistent classification of the TNBC subtypes is essential for personalized treatment and prognosis of TNBC.

Triple-negative breast cancer (TNBC) presents an important clinical challenge, as it does not respond to endocrine therapies or other available targeting agents. FOXM1, an oncogenic transcriptional factor, has reported to be upregulated and associated with poor clinical outcomes in TNBC patients. In this study, we investigated the anti-cancer effects of FDI-6, a FOXM1 inhibitor, as well as its molecular mechanisms, in TNBC cells. Two TNBC cell lines, MDA-MB-231 and HS578T, were used in this study. The anti-cancer activities of FDI-6 were evaluated using various 2D cell culture assays, including Sulforhodamine B (SRB), wound healing, and transwell invasion assays together with 3D spheroid assays, mimicking real tumour structural properties. After treatment with FDI-6, the TNBC cells displayed a significant inhibition in cell proliferation, migration, and invasion. Increased apoptosis was also observed in the treated cells. In addition, we found that FDI-6 lead to the downregulation of FOXM1 and its key oncogenic targets, including CyclinB1, Snail, and Slug. Interestingly, we also found that the FDI-6/Doxorubicin combination significantly enhanced the cytotoxicity and apoptotic properties, suggesting that FDI-6 might improve chemotherapy treatment efficacy and reduce unwanted side effects. Altogether, FDI-6 exhibited promising anti-tumour activities and could be developed as a newly effective treatment for TNBC.

Background: Triple-negative breast cancer (TNBC) is a heterogeneous disease associated with late-stage diagnosis and high metastatic rates. However, a gene signature for reliable TNBC biomarkers is not available yet. We aimed to identify potential key genes and their association with poor prognosis in TNBC through integrated bioinformatics.Methods: Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database.Differentially expressed genes (DEGs) in TNBC vs. non-TNBC and TNBC vs. normal tissues were analyzed. Overlapping upregulated and downregulated DEGs were selected as inputs for Gene Ontology and pathway enrichment analyses using Metascape. Then, UALCAN and Kaplan-Meier plotter were employed to analyze the prognostic values of all overlapping DEGs. Results:We identified 21 upregulated and 24 downregulated overlapping DEGs in TNBC vs. non-TNBC and TNBC vs. normal breast tissue. The upregulated overlapping DEGs were mainly enriched in various pathways including chromosome segregation, cell cycle phase transition, and cell division, whereas overlapping DEGs were significantly downregulated in pathways, such as multicellular organismal homeostasis, tissue homeostasis, and negative regulation of cell population proliferation. Key genes were identified by association with poor overall survival (OS). Our results showed that high expression of CENPW and HORMAD1 was associated with poor OS of TNBC patients. Conversely, the low expression of PIP, APOD, and ZNF703 indicated worse OS. Conclusions:We identified key genes (CENPW, HORMAD1, APOD, PIP, and ZNF703) associated with poor OS. Thus, these genes might serve as candidate prognostic markers for TNBC.