Background <p><i>S. glabra</i> has been widely used to treat tumors in traditional Chinese medicine (TCM). However, the specific mechanism of action of <i>S. glabra</i> in pancreatic cancer remains unclear. In this study, network pharmacological analysis was used to identify the active components of <i>S. glabra</i> and their corresponding targets for the treatment of pancreatic cancer. Furthermore, molecular docking, molecular dynamic simulations, and in vitro experiments were performed to validate the findings.</p> Methods <p>The active components of <i>S. glabra</i> and their corresponding targets for the treatment of pancreatic cancer were identified using the TCMSP database and a literature search. Differentially expressed genes were identified using data from the Gene Expression Omnibus (GEO) database, and their protein–protein interaction (PPI) network was constructed using the STRING platform. The target genes of <i>S. glabra</i> were further assessed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses in the R software. Subsequently, a protein-protein interaction (PPI) network and a composite target–pathway network were established. The target genes were subjected to survival and mutation analyses. Molecular docking and molecular dynamic simulations were used to validate the interaction between the hub target genes and <i>S. glabra</i> in vitro. In addition, cell viability and qRT-PCR verification of <i>S. glabra</i> against pancreatic cancer in vitro.</p> Results <p>A total of 20 active components and 70 targets were identified. Based on the PPI network, <i>CASP3</i>, <i>MMP9</i>, <i>CCND1</i>, <i>EGF</i>, <i>MMP2</i>, <i>CASP8</i>, <i>ERBB2</i>, <i>STAT1</i>, and <i>PPARG</i> were identified as hub target genes. Enrichment analysis showed that <i>S. glabra</i> may primarily affect pathways such as p53 signaling, transcriptional dysregulation in cancer, proteoglycans in cancer, pancreatic cancer, and cell cycle. Molecular docking and molecular dynamic simulations indicated stable binding between anhydroicaritin-GSK3B and quercetin-PPARG. In vitro experiments demonstrated that treatment with <i>S. glabra</i> significantly inhibited the growth of PANC-1 cells and downregulated expression of <i>GSK3B</i> and <i>PPARG</i> (<i>P</i> &lt; 0.05).</p> Conclusion <p>This study demonstrates the potential of <i>S. glabra</i>, a herb in traditional Chinese medicine, for treating pancreatic cancer. The findings provide insights into the mechanism of action of the active ingredients of <i>S. glabra</i>, offering a strong theoretical foundation for its various clinical applications.</p> Clinical trial number <p>Not applicable.</p>

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Revealing the multi-target compounds of Sarcandra glabra identification and inhibition of novel target genes for the treatment of pancreatic cancer

  • Xing Liu,
  • Jianghong Ou

摘要

Background

S. glabra has been widely used to treat tumors in traditional Chinese medicine (TCM). However, the specific mechanism of action of S. glabra in pancreatic cancer remains unclear. In this study, network pharmacological analysis was used to identify the active components of S. glabra and their corresponding targets for the treatment of pancreatic cancer. Furthermore, molecular docking, molecular dynamic simulations, and in vitro experiments were performed to validate the findings.

Methods

The active components of S. glabra and their corresponding targets for the treatment of pancreatic cancer were identified using the TCMSP database and a literature search. Differentially expressed genes were identified using data from the Gene Expression Omnibus (GEO) database, and their protein–protein interaction (PPI) network was constructed using the STRING platform. The target genes of S. glabra were further assessed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses in the R software. Subsequently, a protein-protein interaction (PPI) network and a composite target–pathway network were established. The target genes were subjected to survival and mutation analyses. Molecular docking and molecular dynamic simulations were used to validate the interaction between the hub target genes and S. glabra in vitro. In addition, cell viability and qRT-PCR verification of S. glabra against pancreatic cancer in vitro.

Results

A total of 20 active components and 70 targets were identified. Based on the PPI network, CASP3, MMP9, CCND1, EGF, MMP2, CASP8, ERBB2, STAT1, and PPARG were identified as hub target genes. Enrichment analysis showed that S. glabra may primarily affect pathways such as p53 signaling, transcriptional dysregulation in cancer, proteoglycans in cancer, pancreatic cancer, and cell cycle. Molecular docking and molecular dynamic simulations indicated stable binding between anhydroicaritin-GSK3B and quercetin-PPARG. In vitro experiments demonstrated that treatment with S. glabra significantly inhibited the growth of PANC-1 cells and downregulated expression of GSK3B and PPARG (P < 0.05).

Conclusion

This study demonstrates the potential of S. glabra, a herb in traditional Chinese medicine, for treating pancreatic cancer. The findings provide insights into the mechanism of action of the active ingredients of S. glabra, offering a strong theoretical foundation for its various clinical applications.

Clinical trial number

Not applicable.