Objective: To predict the mechanism of radix paeoniae rubra in the prevention
and treatment of lung cancer through network pharmacology. Methods: The chemical components
of radix paeoniae rubra were retrieved from the TCMSP database, and its action targets were
predicted using the SwissTarget Prediction database. The targets related to lung cancer were
collected from the OMIM database and GeneCards database. The intersection of the targets of
radix paeoniae rubra components and the targets related to lung cancer was screened.The related
target proteins were collected using the String platform, and Gene Ontology annotation analysis
was conducted with the DAVID database. Visualization analysis was performed using Cytoscape
3.9.1 software. The protein-protein interaction network was constructed using the Centiscape 2.2
plugin software to obtain the core targets. Through PPI, GO and KEGG analysis, 8 targets with
high correlation between radix paeoniae rubra and lung cancer were obtained. The 3D structures
of paeoniflorin, ellagic acid and baicalein were obtained from the PubChem database and saved as
Sdf files. Molecular docking was performed between some of the targets and paeoniflorin, ellagic
acid and baicalein. Target information was found in the Uniport database and PDB database.
Molecular docking was conducted in AutodockVina and Openbabel to obtain the docking energy.
Molecular docking visualization was performed using PyMol software. Finally, the mechanism of
radix paeoniae rubra in the prevention and treatment of lung cancer was further analyzed. Based
on the initial screening of 8 key genes, the significantly associated extended pathways were
selected using KEGG, and molecular docking was conducted on all targets in these pathways.
The docking effect was judged by the standard of binding energy ≤ -5.0 kcal·mol-1. Results:
Thirteen compounds and 563 action targets were screened from the traditional Chinese medicine
radix paeoniae rubra. The intersection with 2 376 lung cancer-related targets yielded 223 common
targets. The protein-protein interaction network had 222 nodes and 4 678 edges. After screening,
39 nodes and 625 edges were obtained, and there were 39 core targets. GO annotation yielded
887 biological processes, 110 cellular components, and 197 molecular functions. The eight
targets with high correlation between radix paeoniae rubra and lung cancer were ESR1, EGFR,
KRAS2, KRAS, ERBB1, HRAS, MEK1, and MAP2K1, all of which showed good docking
activity with the core active components of radix paeoniae rubra. Based on the initial screening
of 8 target sites, the RAS-ERBB- MAPK signaling axis-related pathways were identified as
the main signaling pathways through KEGG screening. After screening and determining the
RAS-ERK, ERBB, MAPK, and Ras signaling pathways, molecular docking confirmed that
the core active components of radix paeoniae rubra had good binding. Conclusion: Network
pharmacology intuitively demonstrates that radix paeoniae rubra may exert therapeutic effects
on the proliferation of lung cancer cells through the multi-target and multi-pathway synergistic
inhibition of the RAS-ERBB-MAPK signaling axis, including the RAS-ERK, ERBB, MAPK, and
Ras signaling pathways, providing a theoretical basis for the subsequent development of drugs for
the treatment of lung cancer.
