Targeting Breast Cancer with N-Acetyl-D-Glucosamine: Integrating Machine Learning and Cellular Assays for Promising Results

Background: Breast cancer is a common cancer with high mortality rates. Early diagnosis is crucial for reducing the prognosis and mortality rates. Therefore, the development of alternative treatment options is necessary. Objective: This study aimed to investigate the inhibitory effect of N-acetyl-D-glucosamine (D-GlcNAc) on breast cancer using a machine learning method. The findings were further confirmed through assays on breast cancer cell lines. Methods: MCF-7 and 4T1 cell lines (ATCC) were cultured in the presence and absence of varying concentrations of D-GlcNAc (0.5 mM, 1 mM, 2 mM, and 4 mM) for 72 hours. A xenograft mouse model for breast cancer was established by injecting 4T1 cells into mammary glands. D-GlcNAc (2 mM) was administered intraperitoneally to mice daily for 28 days, and histopathological effects were evaluated at pre-tumoral and post-tumoral stages. Results: Treatment with 2 mM and 4 mM D-GlcNAc significantly decreased cell proliferation rates in MCF-7 and 4T1 cell lines and increased Fas expression. The number of apoptotic cells was significantly higher than untreated cell cultures (p < 0.01 - p < 0.0001). D-GlcNAc administration also considerably reduced tumour size, mitosis, and angiogenesis in the post-treatment group compared to the control breast cancer group (p < 0.01 - p < 0.0001). Additionally, molecular docking/dynamic analysis revealed a high binding affinity of D-GlcNAc to the marker protein HER2, which is involved in tumour progression and cell signalling. Conclusion: Our study demonstrated the positive effect of D-GlcNAc administration on breast cancer cells, leading to increased apoptosis and Fas expression in the malignant phenotype. The binding affinity of D-GlcNAc to HER2 suggests a potential mechanism of action. These findings contribute to understanding D-GlcNAc as a potential anti-tumour agent for breast cancer treatment.