BACKGROUND: Artemisia annua exerts powerful effects in non-small cell lung carcinoma (NSCLC). Some studies have shown that Artemisia annua possesses the characteristics of new therapeutic drugs for NSCLC patients. However, the underlying molecular mechanism of Artemisia annua anti-NSCLC is not yet fully elucidated because Artemisia annua contains hundreds of ingredients. This study aimed to conduct network pharmacological analysis on the mechanism of action of Artemisia annua against NSCLC.

MATERIAL AND METHODS: The active ingredients and corresponding potential targets of Artemisia annua were searched and screened in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Then through The Cancer Genome Atlas (TCGA) and the National Center for Biotechnology Information (NCBI) databases to establish NSCLC related targets. Based on the matching results of Artemisia annua potential targets and NSCLC targets, a protein–protein interaction (PPI) network was constructed to analyze the interactions between these targets and topologically screen the central targets. Furthermore, Gene Ontology (GO) biological functions analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathways enrichment were carried out.

RESULTS: There were 19 main active ingredients of Artemisia annua screened for target prediction; 40 NSCLC-related common targets were identified via multiple NSCLC databases. The node area and corresponding degree value of AKT1, MYC, CCND1, VEGFA, JUN, MAPK1, EGFR, and ESR1 were large and could be easily found in the PPI network. The aforementioned results were further verified by the analysis of GO biological function and KEGG enrichment analysis.

CONCLUSIONS: The network pharmacology analysis reveals the molecular biological mechanism of Artemisia annua anti-NSCLC via multiple active components, multi-channels, and multi-targets. This suggests that Artemisia annua might be developed as a promising anti-NSCLC drug.

Keywords: Protein Interaction Maps, Artemisia annua, Databases, Factual, Databases, Genetic, Drugs, Chinese Herbal, Molecular Docking Simulation