Exploring the mechanism of curcumin in the treatment of colon cancer based on network pharmacology and molecular docking

Abstract

Objective: Curcumin is a plant polyphenol extracted from the Chinese herb turmeric. It was found that curcumin has good anti-cancer properties in a variety of cancers, but the exact mechanism is not clear. Based on the network pharmacology and molecular docking to deeply investigate the molecular mechanism of curcumin for the treatment of colon cancer, it provides a new research direction for the treatment of colon cancer. Methods: Curcumin-related targets were collected using PharmMapper, SwissTargetPrediction, Targetnet and SuperPred. Colon cancer related targets were obtained using OMIM, DisGeNET, GeneCards and GEO databases. Drug-disease intersection targets were obtained via Venny 2.1.0. GO and KEGG enrichment analysis of drug-disease common targets were performed using DAVID. Construct PPI network graphs of intersecting targets using STRING database as well as Cytoscape 3.9.0 and filter core targets. Molecular docking via AutoDockTools 1.5.7. The core targets were further analyzed by GEPIA, HPA, cBioPortal and TIMER databases. Results: A total of 73 potential targets of curcumin for the treatment of colon cancer were obtained. GO function enrichment analysis yielded 256 entries, including BP(Biological Progress):166, CC(celluar component):36 and MF(Molecular Function):54. The KEGG pathway enrichment analysis yielded 34 signaling pathways, mainly involved in Metabolic pathways, Nucleotide metabolism, Nitrogen metabolism, Drug metabolism - other enzymes, Pathways in cancer,PI3K-Akt signaling pathway, etc. CDK2, HSP90AA1, AURKB, CCNA2, TYMS, CHEK1, AURKA, DNMT1, TOP2A, and TK1 were identified as core targets by Cytoscape 3.9.0. Molecular docking results showed that the binding energies of curcumin to the core targets were all less than 0 kJ-mol-1, suggesting that curcumin binds spontaneously to the core targets. These results were further validated in terms of mRNA expression levels, protein expression levels and immune infiltration. Conclusion: Based on network pharmacology and molecular docking initially revealed that curcumin exerts its therapeutic effects on colon cancer with multi-target, multi-pathway. Curcumin may exert anticancer effects by binding to core targets. Curcumin may interfere with colon cancer cell proliferation and apoptosis by regulating signal transduction pathways such as PI3K-Akt signaling pathway,IL-17 signaling pathway, Cell cycle. This will deepen and enrich our understanding of the potential mechanism of curcumin against colon cancer and provide a theoretical basis for subsequent studies.

Keywords: colon cancer; curcumin; immune infiltration; molecular docking; molecular mechanism; network pharmacology.

FIGURE 1

This study is a detailed flow chart of a network-based pharmacology study.

FIGURE 2

Targets relevant to the treatment of colon cancer. ((A). Volcano plot of DEGs associated with colon cancer. (B). Venn diagram showing the common part of curcumin and colon cancer).

FIGURE 3

PPI network diagram ((A). PPI network of potential targets for curcumin therapy of colon cancer. (B). Drug-target-pathway network diagram. The blue circles represent targets, the orange diamonds are pathways, and the green triangles are curcumin).

FIGURE 4

Bubble plot of enrichment analysis ((A). GO functional enrichment analysis of curcumin in colon cancer. (B). KEGG pathway enrichment analysis of curcumin in colon cancer).

FIGURE 5

PI3K-Akt signaling pathway (red marks represent potential targets for curcumin intervention).

FIGURE 6

Molecular docking pattern of curcumin and core target protein. ((A). Curcumin-CDK2, (B). Curcumin-HSP90AA1, (C). Curcumin-AURKB, (D). Curcumin-CCNA2, (E). Curcumin-TYMS, (F). Curcumin-CHEK1, (G). Curcumin-AURKA, (H). Curcumin-DNMT1, (I). Curcumin-TOP2A, (J). Curcumin-TK1).

FIGURE 7

Molecular docking pattern of original ligand and core target protein. ((A). HJK-CDK2, (B). 6DL-HSP90AA1, (C). VX6-AURKB, (D). SO4-CCNA2, (E). SO4-TYMS, (F). C70-CHEK1, (G). SKE-AURKA, (H). ZN-DNMT1, (I). EVP-TOP2A, (J). 4TA-TK1).

FIGURE 8

Hub gene expression in the GEPIA database. ((A). Box plot of hub gene mRNA expression levels in the GEPIA database. Red represents tumor tissues and gray represents normal tissues. (B). Stage diagram of hub gene mRNA expression levels and pathological stages in the GEPIA database).

FIGURE 9

Immunohistochemical images of hub gene protein expression levels in the HPA database.

FIGURE 10

Genetic information of hub targets. ((A). Data showed that 57 of 110 patients (52%) had genetic mutations in these targets. (B). The diagram shows the correlation between the mRNA and protein levels of (a) CDK2, (b) HSP90AA1, (c) AURKB, (d) TYMS, (e) AURKA, (f) TK1, (g) DNMT1, (h) TOP2A).

FIGURE 11

Relationship between differentially expressed core targets and immune cell infiltration. ((A). CDK2, (B). HSP90AA1, (C). AURKB, (D). CCNA2, (E). TYMS, (F). CHEK1, (G). AURKA, (H). DNMT1, (I). TOP2A, (J). TK1).