Systematic Elucidation of the Mechanism of Action of Curcumin Against Colorectal Cancer via Network Pharmacology Approach

Background: Curcumin is a potential drug for the treatment of colorectal cancer (CRC). Its mechanism of action has not been elucidated. Aim: To investigate the mechanism of action of curcumin in the treatment of CRC via network pharmacology, molecular docking and experimental verication. Methods: The targets of curcumin and CRC were obtained from the public databases. The component-targets network of curcumin in the treatment of CRC was constructed by Cytoscape v3.7.2. Through protein-protein interaction (PPI), the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), important targets and signaling pathways related to CRC treatment were identied. Finally, the results were veried by molecular docking and in vitro experiments. Results: A total of 30 potential targets of curcumin for CRC treatment were collectedThe core targets included AKT1, EGFR and STAT3 were identied. GO function enrichment analysis showed 140 items, and KEGG pathway enrichment analysis showed 61 signaling pathways, that were related to the regulation of protein kinase activity, negative regulation of apoptosis process, cancer signaling pathway and PI3K-Akt signali-ng pathway. In vitro experimental verication showed that curcumin could promote the apoptosis of CRC cells, and the key proteins of these signaling pathways were differentially expressed. Conclusion: This study explored the targets and pathways of curcumin in the treatment of colorectal cancer. In vitro experiments showed that curcumin has a therapeutic effect against CRC by inhibiting PI3K-Akt signaling pathway. Our results will lay a foundation for subsequent clinical research and drug development.

These research results showed that curcumin has many potential effects and has de nite therapeutic effects on CRC. However, most of the previous studies focused on some signaling pathways and related targets, and they did not comprehensively and systematically explain the mechanism of action of curcumin in the prevention and treatment of CRC, that limit the promotion and secondary development of curcumin.
With the development of bioinformatics, network pharmacology can systematically and comprehensively reveal the relationship between the active components of traditional Chinese medicine and its potential mechanism of action. Network pharmacology has become an e cientmethod for the study of traditional Chinese medicine(Luo et al. 2020). This study aims to explore the potential targets and molecular mechanisms of curcumin in the treatment of CRC by network pharmacology and molecular docking analysis. Firstly, we screened the molecular targetsof curcumin and pathological targets of CRC by databases. Then, the enrichment analysis was carried out according to the Gene Ontology (GO) and the Kyoto Encyclopediaof Genes and Genomes (KEGG). The multidimensional network of ' drug-targetpathway-disease ' was constructed by Cytoscape v3.7.2. Finally, the interaction between curcumin and targets was veri ed by molecular docking analysis and in vitro experiments, and the biological mechanism of curcumin in the treatment of CRC was explained.The summary of this study is shown in the ow chart of Figure 2.

Drug-Likeness Prediction
Lipinski's rule of ve (RO5) is an empirical rule for screening potential oral drugs byevaluating the properties of drugs, including molecular weight (MW), octanol-water partition coe cient (XLogP3), polar surface area, number of rotatable bonds, hydrogen bond acceptor count, and hydrogen bond donor count (Yang et al. 2020). To explore the drug-likeness properties of curcumin, we searched the Pubchem database ( https://pubchem.ncbi.nlm.nih.gov/ ) with ' curcumin ' as the keyword, obtained the SMILES format of curcumin, and then uploaded it to the SwissADME website ( http://www.swissadme.ch/ ) to nd relevant parameters.

Collection of Curcumin-related Targets
PubChem database was used to obtain the SMILES format of curcumin, which was imported into Swiss target prediction database http://www.swisstargetprediction.ch/ . 'Homo sapiens ' was selected and ' probability > 0 ' was used as the screening condition for target prediction. Finally, the standard gene names were collected by the UniProt platform.

Common Target Screening and Network Construction of Curcumin and CRC
The curcumin-related targets and CRC-related targets were analyzed by Jvenn online platform (http://jvenn.toulouse.inra.fr/app/index.html) to obtain intersection targets and draw the Wayne diagram. The composition-targets network gure was constructed by Cytoscape v3.7.2.

Construction of Protein-Protein Interaction Network (PPI)
The intersection targets were imported into the String platform(https://www.string-db.org/). Then, the interaction relationship between the targets was obtained and saved as the TSV format le. The le was imported into Cytoscape v3.7.2 to get the network diagram. To identify the central nodes and key proteins in the PPI network, the topology parameters were calculated by NetworkAnalyzer, and the degree of centrality (betweenness,closeness, and subgraph) was determined by the CytoNCA.

GO Function and KEGG Pathway Enrichment Analysis
The common targets of curcumin and CRC obtained by the above screening was imported into the DAVID database (https://david.ncifcrf.gov/). The species was set to be "Homo Sapiens". With P < 0.05 as the statistical difference screening condition, the potential targets of curcumin on CRC was evaluated. The biological function and pathways of the targets were analyzed. Histograms and bubble charts are produced through the Bioinformatics cloud platform (http://www.bioinformatics.com.cn/, an online platform for data analysis and visualization). Then, the targets-pathways network was constructed by Cytoscape v3.7.2.

Molecular Docking Analysis
Molecular docking is a validation method, which simulates the binding of receptorsand ligands by computer and predict their a nity. The mol2 le of curcumin was downloaded from TCMSP database (https://tcmspw.com/tcmsp.php). The AutoDock Tools 1.5.6 software was imported and saved in pdbqt format. The 3D structures of key target proteins were downloaded from the PDB database (https://www.rcsb.org), and the water molecules and inactive ligands were removed by PyMOL software. The protein was imported into AutoDock Tools 1.5.6 softwarefor hydrogenation and charge treatment, and the output was pdbqt format. Finally, AutoDock VINA software was used to simulate the molecular docking of the receptor and its ligand, and the optimal binding conformation was obtained. Flow Cytometry Analysis for Cell Apoptosis HCT116 cells were seeded in 6-well plates with 4×10 5 cells/well for 24 hours, and treated with different concentrations of curcumin (0, 12.5, 25, and 50 μmol/L) for 24 hours. The curcumin (CAS number: 458-37-7, Purity ≥ 95%) was purchased from Univ Co., Ltd. (Shanghai,China). The cells were resuspended with 500 μL 1 × binding buffer and incubated with5 μL annexin V-FITC in the dark for 15 min at room temperature. Then, 5 μL PI was added. Finally, cell apoptosis was detected by ow cytometry.

Western Blotting Assay
The total protein was extracted using the radioimmunoprecipitation buffer. Proteins were separated via sodium dodecyl-sulfate polyacrylamide gel electrophoresis and transferred onto polyvinylidene uoride membranes. After sealing for 3 hours in BSA blockingsolution at room temperature, the membrane was washed three times with TBST and incubated with the primary antibody at 4 ℃ overnight. After washing the membrane with TBST three times, the membrane was incubated with the horseradish peroxidase-conjugated secondary antibody for 2 hours at room temperature. The Enhanced Chemiluminescence Detection Kit was used to detect and visualize protein bands. We used Image J software to quantify the protein bands and GAPDH was used as an internal parameter to calculate the relative protein expression.

Data Analysis
Spss20.0 software was used for data analysis. All data are expressed as the mean ± standard deviation (SD). Paired t-test was used for comparison between groups, and P < 0.05 showed that the difference was statistically signi cant. of less than or equal to 140 Â, a computed octanol/water partition coe cient (XLogP3-AA) of less than 5, less than 10 rotatable bonds (RB), no more than 10 hydrogen bond acceptor (HBA), and no more than 5 hydrogen bond donors (HBD) (Chen et al. 2019). It can be seen from the obtained parameters that the properties of curcumin comply with the RO5, indicating that it has good drug-like properties (Table 1).

Composition-Targets Network
The related targets of curcumin were searched, 104 targets were obtained after removing the duplication, and 1911 CRC-related targets were obtained after removing the duplication. Next, 30 common targets were screened out, that were considered potential targets of curcumin in the treatment of CRC ( Figure 3A).

Construction of Protein-Protein Interaction Network (PPI)
We uploaded 30 common targets to the STRING database to determine their functional relationships and interactions. Then the protein interactions with the default con dence level of 0.4 were imported into Cytoscape v3.7.2 to generate a protein-protein interaction (PPI) network, which consisted of 26 nodes and 90 edges, as shown in Figure 4.
To identify the pivot nodes and essential proteins in the PPI network, the topology parameters of the node degree were calculated by the network analyzer, and the three centralities (betweenness, closeness and subgraph) were determined through the CytoNC as shown in Table 2.

GO and KEGG Pathway Enrichment Analysis
The GO and KEGG enrichment analysis were performed via David platform. The GO enrichment analysis is composed of biological process (BP), cellular component (CC) and molecular function (MF). A total of effects of curcumin were related to protein kinase activity, ATP binding, negative regulation of apoptotic process and protein serine/threonine kinase activity,et al., as shown in Figure 5A.
In the enrichment analysis of KEGG pathway, 61 enrichment results were obtained. A total of 20 typical pathways were selected to make the visualizedbubble diagram after excluding irrelevant pathways ( Figure 5B). The results showed that these pathways were mainly related to pathways in cancer, PI3K and Akt signaling pathway, FOXO signaling pathway, et al. Six targets (AKT1, RAF1, BRAF, EGFR, IKBKB, and STAT3) in the rst 20 pathways participated in a high frequency (≥9 times), indicating that they played important roles in CRC. Ten representative signaling pathways are selected to construct a "pathwaystargets" network, as shown in Figure 5C.

Molecular Docking
Curcumin is docked with three important targets AKT1, STAT3 and EGFR. These targets are selected not only because they are the key nodes of PPI network, but also they play important roles in KEGG enrichment pathways. The binding energies of AKT1, STAT3 and EGFR with curcumin were -9.9 kcal/mol, -8.7 kcal/mol, -8.5 kcal/mol, respectively. The binding energies of matrine to AKT1,STAT3 and EGFR were -7.8 kcal/mol, -8.7 kcal/mol, -7.6 kcal/mol, respectively (Table 3). It can be seen that curcumin has a strong binding force with key targets. The binding of curcumin with AKT1 is mainly through the hydrogen bonding with amino acid residues ASN53 and GLN79, hydrophobic interaction with TRP80, and π bonding with LEU210, LEU264, LYS268, VAL270, ILE84. The binding of curcumin with EGFR is mainly through the hydrophobic interaction with amino acid residues VAL762, PHE856, ALA743,MET790, CYS775, and π-bond interaction with LEU844. The binding of curcumin with STAT3 is mainly through the hydrogen bonding of amino acid residues ASP1021, ASN1008, ARG1007, GLU957, GLY962, hydrophobic interaction with LEU881, VAL889, ALA906 and π bonding with LEU1010 ( Figure 6).

Experimental Veri cation Curcumin Promoted Apoptosis of CRC Cells
The effect of curcumin on the apoptosis of HCT116 cells was evaluated through ow cytometric analysis. After treatment with 0, 12.5, 25 and 50μmol/L of curcumin for 24 h, CRC cells were stained with Annexin V-FITC and PI to determine the degree of apoptosis. The results showed that the percentage of apoptotic cells increased signi cantly in a dose-dependent manner after curcumin treatment of HCT116 cells, indicating that curcumin induced apoptosis of HCT116 cells (Figure 7).

Validation of Targets
We further veri ed these targets were involved in curcumin-induced apoptosis in CRC cells by Western blotting. As shown in Figure 8, AKT1 protein levels decreased signi cantly in a dose-dependent manner. The difference was statistically signi cant (p<0.05). The results showed that AKT1 was an important target of curcumin in the treatment of CRC. . However, the regulatory mechanism of curcumin in CRC treatment has not been systematically elucidated. Based on the "drug-target-pathway-disease" network (Lu et al. 2020), in this study, we explored the mechanism of action of curcumin in the treatment of CRC.
By analyzing the PPI network and KEGG enrichment results, we predicted that AKT1, EGFR and STAT3 were the core targets of curcumin in the treatment of CRC. Molecular docking analysis showed that curcumin had good a nity for these three targets, and AKT1 had the highest binding degree. GO enrichment results showed that the therapeutic effect of curcumin was closely related to the regulation of protein kinase activity. Meanwhile, KEGG enrichment results suggested that PI3K-Akt signaling pathway played an important role, which indicated that this signaling pathway was the key link of curcumin in the treatment of CRC. AKT1, a member of the Akt family, is a serine/threonine protein kinase. and symbiont, so as to achieve the homeostasis of internal environment, maintain the body balance, and play the role of prevention and treatment of CRC.
In conclusion, in this study, through the combination of network pharmacology, molecular docking, and in vitro experiments, we veri ed that curcumin had a therapeutic effect against CRC by inhibiting PI3K-Akt signaling pathway. The mechanism of action of curcumin is binding to AKT1, STAT3 and EGFR by hydrogen bond, hydrophobic effect and π-cation bond. This study provides a rational for further clinical research and new drug development using curcumin against CRC.

Declarations Ethical Approval
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Consent to Participate
All authors contributed to the article.

Consent to Publish
All authors approved the submitted version.

Authors Contributions
XYH and YMX contributed to study design, data interpretation, experimental results and manuscript preparation. XLF and CG contributed to manuscript editing, revised the manuscript for important intellectual content, and approved the nal version. XQL and YL contributed to data acquisition and analysis. The authors declare that all data were generated in-house and that no paper mill was used.

Funding
This work was supported by the National Natural Science Foundation of China (81403360). Hospital fund of Yueyang Hospital (2019YYZ08). 2020 Shanghai Sports Science and technology "preparing for war and tackling key problems" project (20J019). The funders play no role in data collection and analysis, design, decision to publish, or preparation of the manuscript.

Competing Interests
The authors declare that there are no competing interests.

Availability of data and materials
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.