Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 May 9;104(19):e42366.
doi: 10.1097/MD.0000000000042366.

Network pharmacology study on the mechanism of Curcumae Rhizoma in the treatment of non-small cell lung cancer

Affiliations

Network pharmacology study on the mechanism of Curcumae Rhizoma in the treatment of non-small cell lung cancer

Zhirui Yang et al. Medicine (Baltimore). .

Abstract

Non-small cell lung cancer (NSCLC) poses a significant threat to public health worldwide. Curcumae Rhizoma (CR) has potent therapeutic potential in different cancers. However, the mechanism of CR treating NSCLC remains unclear. In this study, a network pharmacology-based strategy is followed to address the issue. The targets related to CR or NSCLC were obtained from multiple online public databases. Compound-target network was constructed using Cytoscape. Protein-protein interaction (PPI) was analyzed by STRING. Key transcription factors were explored in TRRUST. Gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were accomplished in Metascape. The druglikeness of compounds was tested in Molinspiration Cheminformatics Software. Autodock Vina was used for molecular docking. Molecular dynamic (MD) simulation was performed using Gromacs. There were 104 overlapped targets considered as key targets of CR treating NSCLC. The key components of CR, including reynosin, (4S,5S)-13-hydroxygermacrone 4,5-epoxide, and (E)-1,7-bis(4-hydroxyphenyl)-6-hepten-3-one, were screened by topological parameters and bioactivity scores. Central clustered targets in PPI network (epidermal growth factor receptor [EGFR], SRC, JAK2, and mitogen-activated protein kinase 3 [MAPK3]) were identified as critical therapeutic targets of CR. GO and KEGG enrichment analysis suggested that therapeutic effect of CR on NSCLC involved various biological processes, cellular components, and molecular functions, and pathways in cancer, JAK-STAT signaling pathway, and p53 signaling pathway were strongly related. Molecular docking and MD simulation suggested that key compounds in CR had high binding affinity to critical NSCLC targets, like EGFR, JAK2, SRC, and MAPK3, with stable complexes formed. This study revealed key components and mechanism of CR treating NSCLC based on a network pharmacology-driven strategy, providing a reference for in-depth study on treating NSCLC.

Keywords: bioactive compounds; network pharmacology; non-small cell lung cancer; underlying mechanism.

PubMed Disclaimer

Conflict of interest statement

The authors have no funding and conflicts of interest to disclose.

Figures

Figure 1.
Figure 1.
Venn diagram depicting common targets between Curcumae Rhizoma and (NSCLC). NSCLC = non-small cell lung cancer.
Figure 2.
Figure 2.
Compound-target network diagram. Diamonds represent monoterpenoids. Hexagons represent sesquiterpenes. Circles represent diarylheptanoids. Triangle represent 2-undecanone. Round rectangles represent common targets of Curcumae Rhizoma and non-small cell lung cancer. Size and color of nodes were proportional to the value of Degree.
Figure 3.
Figure 3.
Protein-protein interaction network of key targets in the treatment of non-small cell lung cancer by Curcumae Rhizoma. (A) PPI network of 104 targets of Curcumae Rhizoma treating non-small cell lung cancer. (B) The top 3 central gene cluster identified by MCODE analysis. Node size was proportional to the value of BC. Node color was proportional to the value of CC. Border color of node was proportional to the value of degree. Edge width was proportional to the combined score calculated by STRING 11.5. BC = betweenness centrality, CC = closeness centrality, MCODE = Molecular Complex Detection, PPI = protein–protein interaction.
Figure 4.
Figure 4.
Network of the top 10 key TF and their related targets. Orange nodes represent TF and the node size was proportional to the number of overlapped genes. Sky blue nodes represent corresponding targets. TF = transcription factors.
Figure 5.
Figure 5.
(A) Gene ontology enrichment analysis and (B) Kyoto encyclopedia of genes and genomes pathway enrichment analysis. BP = biological processes, CC = cellular components, MF = molecular functions.
Figure 6.
Figure 6.
Representative diagrams of molecular docking between compounds in Curcumae Rhizoma and the targets. (A) JAK2 and (3R)-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)heptan-3-ol. (B) SRC and (E)-1,7-bis(4-hydroxyphenyl)-6-hepten-3-one. (C) MAPK3 and (3R)-1-(3,4-dihydroxyphenyl)-7-phenyl-(6E)-6-hepten-3-ol. (D) EGFR and reynosin. EGFR = epidermal growth factor receptor, JAK2 = tyrosine-protein kinase JAK2, MAPK3 = mitogen-activated protein kinase 3, SRC = proto-oncogene tyrosine-protein kinase Src.
Figure 7.
Figure 7.
Heatmap of binding affinity. CR41, (4S,5S)-13-hydroxygermacrone 4,5-epoxide. CR45, (3R)-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)heptan-3-ol. CR46, (3R)-1-(3,4-dihydroxyphenyl)-7-phenyl-(6E)-6-hepten-3-ol. CR52, (E)-1,7-bis(4-hydroxyphenyl)-6-hepten-3-one.
Figure 8.
Figure 8.
Molecular dynamic simulation. (A–D) The RMSD. (E–H) Rg. (I–L) The number of hydrogen bonds. (M–P) The RMSF. (Q–T) The energy decomposition diagram of amino acids. Rg = radius of gyration, RMSD = root mean square deviation, RMSF = root mean square function.

Similar articles

References

    1. AACR Cancer Progress Report 2022 Steering Committee. Cancer in 2022. Cancer Discov. 2022;12:2733–8. - PubMed
    1. Chen P, Liu Y, Wen Y, Zhou C. Non-small cell lung cancer in China. Cancer Commun (Lond). 2022;42:937–70. - PMC - PubMed
    1. Sung H, Ferlay J, Siegel RL, et al. . Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. - PubMed
    1. Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature. 2018;553:446–54. - PubMed
    1. Ettinger DS, Wood DE, Aisner DL, et al. . Non-small cell lung cancer, version 5.2017, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2017;15:504–35. - PubMed

MeSH terms

Substances