Objectives Vascular endothelial growth factor receptor 2 (VEGFR2) is a critical therapeutic target in hepatocellular carcinoma (HCC) due to its role in angiogenesis and tumor progression. While several inhibitors are currently used, clinical utility is often limited by resistance and adverse effects, necessitating the discovery of novel therapeutic agents. The aim of this study was to identify and characterize novel, highly effective VEGFR2 inhibitors using an integrated computational pipeline to advance the development of new HCC treatments. Methods A comprehensive dataset from the ChEMBL database was curated and standardized for Quantitative Structure-Activity Relationship (QSAR) modeling. A binary classification framework was employed, where a Light Gradient Boosting Machine (LGBM) model demonstrated superior predictive performance. Two lead compounds and a reference were selected for in-depth molecular modeling. Their binding poses were predicted via molecular docking and subsequently subjected to 200 ns Molecular Dynamics (MD) simulations to assess stability and conformational dynamics. Thermodynamic binding affinities were calculated using the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method. Results The LGBM model achieved high accuracy and a robust Matthews Correlation Coefficient (MCC) on an independent test set. MD analysis, including Root Mean Square Deviation (RMSD) and Radius of Gyration (Rg), confirmed stable binding throughout the 200 ns trajectory. MMPBSA calculations validated the binding affinities, identifying van der Waals and electrostatic interactions as the primary driving forces for complex stability. Conclusion This study successfully bridges machine learning with advanced molecular simulations, offering a validated workflow for the rational design and optimization of novel small-molecule VEGFR2 inhibitors.
Background Breast cancer is the leading cause of cancer-related deaths in women, primarily due to distant metastasis. Metabolic reprogramming plays a critical role in tumor growth and spread, but the metabolic mechanisms underlying metastasis in breast cancer remain unclear. The primary objective of this study is to identify molecular targets mediating breast cancer progression and to evaluate whether targeting the metabolic reprogramming represents a potential therapeutic strategy. Methods To uncover key metabolic regulators involved in breast cancer progression, we analyzed high-throughput RNA sequencing data and identified Paired Like Homeodomain 1 (PITX1) as a frequently upregulated oncogene. Its expression was further validated by immunohistochemistry, quantitative PCR, and western blotting across various metastatic breast cancer tissues. The correlation between PITX1 expression and patient survival was also evaluated. Functional assays were conducted to explore the role of PITX1 in promoting breast cancer proliferation and metastasis. As this study is primarily based on mechanistic cellular and bioinformatic analyses rather than clinical intervention trials, traditional clinical effect size metrics are not directly applicable. However, we have now ensured that all major findings include quantitative effect measurements (e.g., fold changes, hazard ratios where applicable, correlation coefficients) together with corresponding statistical significance values to improve clarity and transparency. Results Elevated PITX1 expression was significantly associated with poorer overall survival, distant metastasis-free survival, relapse-free survival, and post-progression survival in breast cancer patients. Silencing PITX1 significantly reduced breast cancer cell proliferation and suppressed glycolysis. Mechanistically, we found that PITX1 transcriptionally activates Phosphofructokinase platelet (PFKP), a key glycolytic enzyme, thereby enhancing glycolytic flux to promote tumor growth and metastatic capacity. Notably, isoliquiritigenin was identified as a small-molecule inhibitor that targets the PITX1u2013PFKP axis, downregulating glycolysis and consequently suppressing breast cancer progression. Conclusion Our findings uncover a novel oncogenic mechanism by which PITX1 promotes breast cancer progression and metastasis through glycolytic reprogramming. Targeting the PITX1u2013PFKP axis with isoliquiritigenin offers a promising therapeutic strategy for breast cancer treatment.
Cytoskeletal reorganization is fundamental to essential cellular processes, including shape maintenance, migration, adhesion cytokinesis, and phagocytosis, and its dysregulation is a hallmark of tumor progression. In cancer cells, altered cytoskeletal dynamics promote invasion and genomic instability resulting from mitotic defects. The actin and microtubule cytoskeletons are highly dynamic polymer networks that organize intracellular architecture, establish polarity, and generate the mechanical forces required for cell division and motility. Their dysregulation disrupts normal cell behavior and facilitates tumor invasion and metastasis. The cytoskeleton therefore represents a key source of potential therapeutic targets for inhibiting metastatic dissemination. This review focuses on recent anticancer strategies targeting cytoskeletal and motility-associated pathways, including microtubule-directed agents, actin-modulating compounds, inhibitors of focal adhesion signaling, modulators of intermediate filament dynamics, and regulators of cytoskeletal crosstalk. Collectively, these strategies underscore the central role of cytoskeleton in cancer progression and highlight its potential as a promising therapeutic target.
