Investigating the Mechanism of Edible Medicinal Plants Against Squamous Cell Carcinomas Based on Network Pharmacology, Bioinformatics, and Molecular Dynamics Simulation

This study utilized network pharmacology, bioinformatics, along with machine learning to investigate the multi-target synergistic anti-cancer mechanisms of three edible medicinal plants (EMPs)—mulberry leaf, lotus leaf, and sea buckthorn—against oral and esophageal squamous cell carcinomas (OSCC and ESCC). We identified potential active constituents and their targets through mining Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Swiss Target Prediction databases. Concurrently, integration with differential expression profiles and co-expression modules identified crucial intersection targets between the EMPs and these two cancers. Subsequent machine learning algorithms and cross-cancer analysis consistently identified Matrix Metalloproteinase-1 (MMP1) as a critical hub gene. Its overexpression is closely associated with tumor invasion and metastasis. Molecular simulations revealed stable binding interactions between active constituents from three EMPs and hub proteins. Furthermore, research on immune cell infiltration suggested that the active components of three EMPs may impact the tumor immune microenvironment in both OSCC and ESCC through the regulation of pivotal gene expression. Collectively, this work systematically elucidates the molecular basis underlying the multi-target, multi-pathway synergistic anti-cancer effects of these EMPs, providing a theoretical foundation for developing natural drugs against these squamous cell carcinomas.