BACKGROUND: This study aimed to use a network pharmacology approach to establish the effects of plumbagin on pancreatic cancer (PC) and to predict core targets and biological functions, pathways, and mechanisms of action.

MATERIAL AND METHODS: Genes associated with the pathogenesis of PC were obtained from a database of gene-disease associations (DisGeNET). Putative genes associated with plumbagin were identified from the databases of drug target identification (PharmMapper), target prediction of bioactive components (SwissTargetPrediction), and comprehensive drug target information (DrugBank). PC targets of plumbagin were harvested by using a functional enrichment analysis tool (FunRich). The data of function-related protein-protein interactions (PPIs) with a confidence score >0.9 were obtained by using functional protein association networks (STRING). The network interactions of plumbagin and PC targets and function-related proteins were constructed through complex network analysis and visualization (Cytoscape). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were used to identify the effects of plumbagin.

RESULTS: The most important biotargets for plumbagin in PC were identified as TP53, MAPK1, BCL2, and IL6. A total of 1,731 annotations and 121 enriched pathways for plumbagin and PC were identified by KEGG and GO analysis. The top 10 signaling pathways of plumbagin and PC were screened, followed by identification of biological components and functions.

CONCLUSIONS: Network pharmacology established the effects of plumbagin on PC, predicted core targets, biological functions, pathways, and mechanisms of action. Further studies are needed to validate these predictive biotargets in PC.

Keywords: Health Care Evaluation Mechanisms, Medical Oncology, Pharmacology, Biomarkers, Tumor, Computational Biology, gene ontology, Gene Regulatory Networks, Naphthoquinones, Pancreatic Neoplasms, Protein Interaction Maps