Triple-negative breast cancer (TNBC), a highly aggressive molecular subtype characterized by a lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression, remains a major clinical challenge, with a median overall survival of approximately 12-15, months in advanced-stage patients. Andrographolide (AG), a diterpenoid phytochemical derived from Andrographis paniculata, has demonstrated promising anti-inflammatory, antioxidant, and antitumor activities. However, its precise molecular and therapeutic mechanisms in TNBC remain poorly understood. AG inhibits the proliferation and metastasis of TNBC. Through an integrative approach combining network pharmacology and machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) and random forest, cyclin-dependent kinase 1 (CDK1) has been identified as a critical molecular gene of AG. Experimental validation via western blot and immunofluorescence analyses reveal that AG treatment significantly downregulates CDK1 expression while concurrently upregulating the expression of the tumor suppressor p53, suggesting a functional interplay between these pathways. These mechanistic insights indicate that AG potentially exerts antiproliferative effects on TNBC cells through modulation of the p53/CDK1 signaling axis, thereby establishing a robust preclinical basis for its potential clinical translation in this challenging malignancy.
Keywords: andrographolide; dynamic molecular simulation; machine learning; network pharmacology; triple-negative breast cancer.