Multilevel synergistic anticancer mechanisms of Punica granatum L. peel polyphenols targeting the pan-cancer proliferation target PCNA: a combined analysis based on network pharmacology, molecular dynamics simulation, and UPLC-Q-TOF-HRMS

Abstract Punica granatum L. peel is rich in tannins and flavonoids. Modern pharmacological studies have indicated its multi-target anticancer potential; however, the molecular recognition mechanisms between its active components and key targets remain to be systematically clarified. In this study, network pharmacology, computational toxicology, molecular dynamics simulation, and UPLC-Q-TOF-HRMS high-resolution mass spectrometry were integrated to systematically explore the multi-level anticancer mechanisms of Punica granatum L. peel active components targeting proliferating cell nuclear antigen (PCNA). Through multi-level stepwise screening, eight candidate active molecules with favorable oral applicability and low toxicity risk were identified from Punica granatum L. peel. Network topology analysis predicted PCNA as a core hub in the component-disease common target network, and public transcriptome data further suggested that PCNA is significantly highly expressed in multiple common cancers and is associated with tumor stage. Molecular docking and all-atom molecular dynamics simulations predicted that Granatin B-type ellagitannin and corilagin could stably bind to the interdomain connecting loop functional interface of PCNA through hydrogen bonding networks and hydrophobic interactions, respectively, while MM/GBSA free energy calculations further located key hot-spot residues, suggesting potential competitive interference with the protein interactions between PCNA and DNA polymeraseδand p21. At the experimental level, first-order full-scan high-resolution mass spectrometry enabled the simultaneous identification of characteristic ions of the candidate components, providing direct chemical evidence for the network pharmacology predictions. Meanwhile, the study strictly distinguished the evidence hierarchy between computational prediction and experimental verification, clearly positioning taxane compounds lacking direct phytochemical evidence as database-predicted candidate molecules. This study established a complete research loop of “component screening-target prediction-dynamic simulation-chemical validation”, exploring the pan-cancer synergistic intervention mechanisms of Punica granatum L. peel polyphenols centered on PCNA from both molecular and chemical dimensions, thereby providing a theoretical basis and methodological reference for PCNA-targeted anticancer strategies based on natural products.