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. 2022 Aug 25:9:985991.
doi: 10.3389/fnut.2022.985991. eCollection 2022.

Network pharmacology and molecular docking analysis reveal insights into the molecular mechanism of shiliao decoction in the treatment of cancer-associated malnutrition

Sidan Long  1   2 , Shuangshuang Ji  1 , Peng Xue  2 , Hongting Xie  1 , Yinjie Ma  2 , Shijie Zhu  2
Affiliations

Network pharmacology and molecular docking analysis reveal insights into the molecular mechanism of shiliao decoction in the treatment of cancer-associated malnutrition

Sidan Long et al. Front Nutr. .

Abstract

Purpose: Shiliao Decoction (SLD) was developed for treatment and prevention of cancer-associated malnutrition (CAM) in China. In this study, we aim to discover SLD's active compounds and demonstrate the mechanisms of SLD that combat CAM through network pharmacology and molecular docking techniques.

Methods: All components of SLD were retrieved from the pharmacology database of Traditional Chinese Medicine Systems Pharmacology (TCMSP). The GeneCards database and the Online Mendelian Inheritance in Man database (OMIM) were used to identify gene encoding _target compounds, and Cytoscape was used to construct the drug compound-_target network. The network of _target protein-protein interactions (PPI) was constructed using the STRING database, while gene ontology (GO) functional terms and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways associated with potential _targets were analyzed using a program in R language (version 4.2.0). Core genes linked with survival and the tumor microenvironment were analyzed using the Kaplan-Meier plotter and TIMER 2.0 databases, respectively. Protein expression and transcriptome expression levels of core gene were viewed using the Human Protein Atlas (HPA) and the Cancer Genome Atlas (TCGA). A component-_target-pathway (C-T-P) network was created using Cytoscape, and Autodock Vina software was used to verify the molecular docking of SLD components and key _targets.

Results: The assembled compound-_target network primarily contained 134 compounds and 147 _targets of the SLD associated with JUN, TP53, MAPK3, MAPK1, MAPK14, STAT3, AKT1, HSP90AA1, FOS, and MYC, which were identified as core _targets by the PPI network. KEGG pathway analysis revealed pathways involved in lipid and atherosclerosis, the PI3K/Akt signaling pathway, and immune-related pathways among others. JUN is expressed at different levels in normal and cancerous tissues, it is closely associated with the recruitment of different immune cells and has been shown to have a significant impact on prognosis. The C-T-P network suggests that the active component of SLD is capable of regulating _target genes affecting these related pathways. Finally, the reliability of the core _targets was evaluated using molecular docking technology.

Conclusion: This study revealed insights into SLD's active components, potential _targets, and possible molecular mechanisms, thereby demonstrating a potential method for examining the scientific basis and therapeutic mechanisms of TCM formulae.

Keywords: cancer-associated malnutrition; immunity; molecular docking; network pharmacology; prognosis; shiliao decoction.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Workflow diagram of network pharmacology analysis.
FIGURE 2
FIGURE 2
Construction of the _target genes set (A) and compound-_target network (B). Blue quadrilateral nodes indicate _targets, while differentially colored V nodes denote compounds. The dark yellow, light purple, light red, light blue, dark green, gray, dark red, dark purple, light yellow, and light green V nodes represent active compounds of Glycyrrhiza uralensis Fisch., Amomum villosum Lour., Citrus reticulata Blanco., Dioscorea opposite Thunb., Coix lacryma-jobi L.var.ma-yuen (Roman.) Stapf., Codonopsis pilosula (Franch.) Nannf., Ziziphus jujuba Mill., Typha angustifolia L., Zingiber officinale Rosc., Zanthoxylum bungeanum Maxim., respectively.
FIGURE 3
FIGURE 3
PPI network (A) and its key _target network (B).
FIGURE 4
FIGURE 4
Enrichment analysis. (A) GO enrichment analysis. (B) KEGG enrichment analysis. (C) The most enriched KEGG pathway. Red genes in the network is the SLD _target genes.
FIGURE 5
FIGURE 5
Immune infiltration and prognosis. (A–D) Association of JUN with the prognosis of different cancers. (E) Correlation of JUN expression with immune cells.
FIGURE 6
FIGURE 6
Differential expression of JUN in cancers. (A) Validation of the expression of JUN at the protein level using the HPA database. (B) Validation of the expression of JUN at the transcriptome level using the TCGA database.
FIGURE 7
FIGURE 7
Component-_target-pathway network (A) and molecular docking (B,C). (A) The blue diamonds represent genes. The red circle nodes denote components of SLD, and the yellow V nodes represent CAM-related signaling pathways. (B) Molecular docking results of core _targets and active compounds. The color indicates an affinity score. Dark blue represents the lowest affinity score and the highest affinity between receptor and ligand. White represents the highest affinity score and the lowest affinity between receptor and ligand. (C) Optimal complex structure of components and _targets.
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