Metabolomics combined with network pharmacology reveals the anti-hepatoma effects of terpenoids from Polygonatum kingianum var. grandifolium and Polygonatum sibiricum Redouté as well as differences in their terpenoid metabolites

Abstract

Liver cancer remains a smajor cause of mortality worldwide, underscoring the urgent need for novel natural therapeutics. Polygonatum kingianum var. grandifolium (PK) and Polygonatum sibiricum Redouté (PS) are rice in terpenoids, yet their anti-liver cancer mechanisms remain poorly understood. This study used metabolism, network analysis, molecular docking, and molecular dynamics simulations to investigate their therapeutic potential. Metabolomic analysis identified nine differential terpenoid metabolites, with Maslinic acid and Alphitolic acid being species-specific. Network analysis revealed 23 liver cancer-related targets, including five key proteins: HMGCR, PTGS2, ESR1, PPARG, and PGR. Functional enrichment analysis identified 126 GO terms and 11 KEGG pathways (P < 0.05). Molecular docking suggested strong binding affinities between core compounds and targets, while molecular dynamics simulations confirmed the stability of maslinic acid and alphitolic acid with their respective targets. This study enhances the pharmacological understanding of Polygonatum species and offers promising insights for the development of novel liver cancer treatments.

Keywords: Polygonatum Genus; Liver Cancer; Molecular Docking; Molecular Dynamics; Network Analysis; Terpenoid Metabolites.

Fig. 1

Multivariate statistical analysis of PK and PS. A: Clustering heatmap; B: PCA plot; C: OPLS-DA score plot; D: OPLS-DA validation plot

Fig. 2

Composition and Content Plots. A: Stacked chart of terpenoid content; B: Venn diagram of differential terpenoids; C: Peak area analysis chart of the nine differential terpenoids (the letters ‘a’ and ‘b’ above the bars indicate statistically significant differences between groups, p < 0.05)

Fig. 3

Target and Network Diagrams. A: Venn diagram of therapeutic targets and molecular targets; B-C: PPI core protein interaction network; D: Core targets

Fig. 4

GO Functional Annotation and KEGG Signaling Pathways. A: BP (Biological Process) results for terpenoids in the treatment of liver cancer; B: CC (Cellular Component) results for terpenoids in the treatment of liver cancer; C: MF (Molecular Function) results for terpenoids in the treatment of liver cancer; D: KEGG pathways for the therapeutic effects of terpenoids on liver cancer

Fig. 5

Network diagram of metabolite-target-metabolic pathway relationships

Fig. 6

Metabolite-Target Molecular Docking Figures. Represented sequentially as: HMGCR − Maslinic acid, ESR1 − Maslinic acid, PPARG − Maslinic acid, PGR − Maslinic acid, PTGS2 − Maslinic acid, PTGS2 − Alphitolic acid

Fig. 7

RMSD values of the five complexes, protein, and small molecule ligand. A ESR1 − Maslinic acid. B HMGCR − Maslinic acid. C PGR − Maslinic acid. D PPARG − Maslinic acid. E PTGS2 − Alphitolic acid

Fig. 8

Radius of gyration (Rg) values of the five complexes. A ESR1 − Maslinic acid. B HMGCR − Maslinic acid. C PGR − Maslinic acid. D PPARG − Maslinic acid. E PTGS2 − Alphitolic acid

Fig. 9

Solvent-accessible surface area (SASA) values of the five complexes. A ESR1 − Maslinic acid. B HMGCR − Maslinic acid. C PGR − Maslinic acid. D PPARG − Maslinic acid. E PTGS2 − Alphitolic acid

Fig. 10

Number of hydrogen bonds in the five complexes. A ESR1 − Maslinic acid. B HMGCR − Maslinic acid. C PGR − Maslinic acid. D PPARG − Maslinic acid. E PTGS2 − Alphitolic acid