Identification of the active substances and mechanisms of ginger for the treatment of colon cancer based on network pharmacology and molecular docking

doi:10.21203/rs.3.rs-38111/v1

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Identification of the active substances and mechanisms of ginger for the treatment of colon cancer based on network pharmacology and molecular docking

https://doi.org/10.21203/rs.3.rs-38111/v1

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published 11 Jan, 2021

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Background: Colon cancer is increasing recently but the high cost and adverse side effects experienced always leads to treatment drop out. Zingiber officinale, commonly known as ginger, is a popular herbal medicine and this study was aimed to identify the active compounds from ginger and to investigate its anti-cancer mechanisms through network pharmacology construction.

Results: Ginger compounds were discerned through the TCMSP, which were filtered by the metrics of oral bioavailability and drug likeness, and its related targets were searched. After that, the targets interacting with colon cancer were collected using Genecards, OMIM, and Drugbank databases. Six potential active compounds, 288 interacting targets in addition to 1356 disease-related targets were collected, of which 114 intersection targets were obtained. The PPI network showed that 32 targets including SRC, PIK3R1, and TP53 were identified as key targets. These targets were mainly associated with the biological processes like transmembrane receptor protein tyrosine kinase signaling pathway, regulation of cellular protein localization, cellular response to oxidative stress. KEGG enrichment manifested that ginger probably produced preventive effects against colon cancer by regulating significant signaling pathway like pathway in cancer, hepatitis B, and estrogen signaling pathway. TP53, HSP90AA1, MAPK8, JAK2, CASP3, and ERBB2 could be viewed as the most potential target proteins, which were validated by molecular docking simulation.

Conclusion: This study demonstrated the multi-component, multi-target, and multi-pathway characteristics of ginger, providing novel insight for ginger compounds developed as new drug for anti-colon cancer.

Bioinformatics

ginger

colon cancer

network pharmacology

pathway analysis

molecular docking

1-monolinolein

With rapid development of population growth and life style, cancer as a growing threat in the world, is the second leading noncommunicable disease of death globally next only to cardiovascular disease [1[. Colorectal cancer, including colon cancer and rectal, is the second most common cause of cancer-related deaths worldwide, and 880,792 colon cancer deaths were projected to occur in 2018 (https://gco.iarc.fr/). Its pathological manifestations contain diarrhea or constipation, changes in stool consistency (thinning, bloody, or mucus), abdominal pain or masses, weakness, and fatigue [2]. Currently, the comprehensive therapeutic approaches are mainly focused on the surgery, radiotherapy, and chemotherapy. However, the high costs of molecular targeted therapy and a variety of adverse effects including nausea, vomiting, and digestive tract irritation reaction caused by chemotherapy make a portion of patients eventually interrupt chemotherapy.

Historically medicinal botanicals, as part of complementary medicine in the USA, have provided a source of inspiration for discovering anticancer drug agents, and more than half of currently available drugs are natural products or are related to them [3]. Ginger (Zingiber officinale), belong to Zingiberaceae family, has long been used as a popular traditional medicine with aromatic and pungent rhizome, which mainly distributed in India, China, and Nigeria [4]. The rhizome as the major medicinal part of ginger contains many pungent phenolic compounds, such as volatile oil, gingerol analogues, diarylheptanoids, and phenylalkanoids, leading to a mass of pharmacological effects with wide range [5, 6]. Furthermore, ginger has been reported to preventing cell proliferation against colon cancer cells, such as Colon-26 tumors [7, 8]. However, the other bioactive compounds, except for pungent compound of 6-gingerol and 6-shogaol, and its molecular actions are still not systematically reported.

While moving ahead with computer technology, network pharmacology incorporating a series of disciplines and techniques had emerged. Network-based drug discovery is considered to be the next pattern for the pharmaceutical research, which using “network-targeted, multi-component” strategy instead of “one target, one drug” [9, 10]. Due to the multi-component and multi-target characteristics of herbal medicine, network pharmacology has been successfully introduced to analyze the biological network between herbal medicine and diseases [11]. In this study, the active components, therapeutic target prediction, and drug target network analysis were systematically conducted. Moreover, the potential compounds and targets were further validated by the molecular docking simulation, which would be beneficial for development of new drug and clinical application of ginger in the treatment of colon cancer. The detailed workflow of the network pharmacology based study of ginger is shown in Fig. 1.

Active compounds and corresponding target collection.

The traditional Chinese medicine systems pharmacology database (TCMSP, http://tcmspw.com/tcmsp.php) is a platform including ingredients from herbal medicine, related targets as well as its pharmacokinetic properties, which was used to collect the active components of ginger [12]. The active constituents were filtered by the metrics absorption, distribution, metabolism, and excretion (ADME), including oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 [13].

The targets interacting with these active compounds were predicted based on TCMSP (http://tcmspw.com/tcmsp.php [12] and Swiss Target Prediction (http://www.swisstargetprediction.ch/) databases [14]. Subsequently, the obtained targets were imported into Uniprot database (https://www. uniprot.org/) to acquire its related gene name, gene ID and functions.

Acquisition of disease-associated targets.

Keywords such as “colon cancer” or “colon tumor” were used to search the known targets related to the pathogenesis of colon cancer from four major databases, including GeneCards (https://www.genecards.org/) [15], OMIM (https://omim.org/) [16], and Drugbank Database (https://www.drugbank.ca/). To reduce the false positives, only the curated targets that directly associate with the disease were considered. After removing the duplicate genes, the genes related to disease and active compounds were inputted to Draw Venn Diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/), and the intersection genes were collected as candidate targets.

Protein-Protein Interaction (PPI) network.

Based on the candidate targets, a PPI network was constructed by importing the gene name of the targets to the Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/) databases, with confidence scores > 0.7 [17]. Cytoscape 3.7.1 software, version 3.7.1 [18] was used to visualize the PPI network. Then, three topological features, “degree” “betweenness”, and “closeness” were calculated to identify the key targets. “Degree” parameter represents the number of edges associated with a node. “Betweenness” indicates the number of shortest paths between pairs of nodes, and “Closeness” describes the inverse of the number of the distances.

GO and KEGG pathway enrichment analysis.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed using Metascape (https://metascape.org). GO functionally annotates genes and proteins into three main terms including cellular components (CCs), molecular functions (MFs), and biological processes (BPs). KEGG enrichment analysis unveils the possible biological process with key genes. In addition, the bubble chart of GO and KEGG enrichment analysis were performed on the bioinformatics platform (http://www.bioinformatics.com.cn/).

Construction of drug-compounds-disease-targets-pathway network.

To characterize the therapeutic mechanisms of ginger in the treatment of colon cancer, a network of drug-compounds-disease-targets-pathway was constructed and visualized using Cytoscape software, version 3.7.1 [18]. In the network, the nodes of different colors and shapes represent the drug, compounds, disease, gene/protein, or disease related pathway, respectively, and an “edge” is an association between the nodes.

Molecular docking simulation.

A total of 8 targets having good correlation with other proteins, compounds, and pathways including SRC, PIK3R1, TP53, HSP90AA1, MAPK8, JAK2, CASP3, and ERBB2 were included in this molecular docking simulation. The Discovery studio software (version 4.5.0, Biovea Inc., Omaha, NE, USA) was used and the PDB-ID of these targets were accessed. To ensure the reliability of molecular docking, a protein crystal structure with a resolution of less than 2.8 Å was required. In briefly, the active compounds were prepared using “Prepare Ligands” module to obtain an effective three-dismensional conformation. After crystallographic water molecules removing, the “Prepare Protein” module was used to hydrogenate remove the polyconformation of target protein, and supplement the incomplete amino acid residues. Subsequently, the molecular docking were performed in “LibDock” module, and the LibDockScore was required to evaluate the affinity between the target proteins and active compounds. The LibDockScore between target protein and its corresponding prototype ligand were viewed as the threshold, and the compound with higher scores were considered as the active component of ginger for this protein.

Ginger compounds and their interacting targets

After the searching, filtering, and removal of the duplicates, 6 compounds (MOL002464, MOL002501, MOL002514, MOL000358, MOL000359, and MOL002467) were obtained from TCMSP database (listed in Table 1). It was noted that the representative component 6-gingerol was recruited in this study, since the DL value is relatively close to 0.18. Meanwhile, 288 targets in total interacting with these active compounds were collected, among which 254 were obtained from the Swiss TargetPrediction and 34 from TCMSP database.

Disease-associated targets.

After removal of the duplicates, there remained 1356 colon cancer-associated genes, in which 1165 genes were from the GeneCards database, 156 from the OMIM, and 35 from the Drugbank. After merging the disease and active compounds related targets, 114 candidate targets were collected for further mechanisms study of ginger on treatment of colon cancer (Fig. 2A).

PPI network analysis.

The 114 candidate targets were connected to establish an initial PPI network that included 103 nodes and 510 edges, and 4 isolated targets genes were removed (Fig. 2B). In addition, the top two protein-protein interacting clusters were constructed (Fig. 2C and 2D). The PIK3R1, MAPK3, and TP53 were as the core targets for one cluster (19 nodes and 47 edges), while another cluster (17 nodes and 42 edges) as the STAT 3 and HSP90AA1. As shown in Fig. 2, the color and size of the nodes reflected the degree value, and larger and redder nodes meant a greater “degree” value, such as PIK3CA, SRC, PIK3R1, and TP53. Subsequently, the median values of three topological indexes (degree, betweenness, and closeness) were calculated, and a total of 32 key targets, with SRC, PIK3R1, TP53, STAT3, and HSP90AA1 as the top ones, were collected for pathway enrichment analysis (Table 2).

Table 2

The 32 key targets and the counts of their interacting targets.
No.	Uniprot ID	Gene symbol	Target name	Edge
1	P12931	SRC	Proto-oncogene tyrosine-protein kinase	37
2	P27986	PIK3R1	Phosphatidylinositol 3-kinase regulatory subunit alpha	37
3	P04637	TP53	Cellular tumor antigen p53	35
4	P40763	STAT3	Signal transducer and activator of transcription 3	31
5	P07900	HSP90AA1	Heat shock protein HSP 90-alpha	27
6	P45983	MAPK8	Mitogen-activated protein kinase 8	23
7	O60674	JAK2	Tyrosine-protein kinase JAK2	20
8	P00533	EGFR	Epidermal growth factor receptor	19
9	P42338	PIK3CB	Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit beta isoform	19
10	P23458	JAK1	Tyrosine-protein kinase	18
11	P03372	ESR1	Estrogen receptor	18
12	Q05513	PRKCZ	Protein kinase C zeta type	18
13	Q05655	PRKCD	Protein kinase C delta type	18
14	Q04206	RELA	Transcription factor p65	17
15	Q05397	PTK2	Focal adhesion kinase 1	15
16	P42574	CASP3	Caspase-3	15
17	P04626	ERBB2	Receptor tyrosine-protein kinase erbB-2	15
18	P08254	JUN	Mitogen-activated protein kinase 8	14
19	P52333	JAK3	Tyrosine-protein kinase JAK3	14
20	Q02750	MAP2K1	Dual specificity mitogen-activated protein kinase kinase 1	14
21	P06493	CDK1	Cyclin-dependent kinase 1	14
22	P10275	AR	Androgen receptor	13
23	P18031	PTPN1	Tyrosine-protein phosphatase non-receptor type 1	12
24	P20248	CCNA2	Cyclin-A2	12
25	P42345	MTOR	Serine/threonine-protein kinase mTOR	10
26	P06401	PGR	Progesterone receptor	9
27	P14780	MMP9	Matrix metalloproteinase-9	9
28	P07949	RET	Proto-oncogene tyrosine-protein kinase receptor Ret	8
29	P53350	PLK1	Serine/threonine-protein kinase PLK1	8
30	P08254	MMP3	Stromelysin-1	8
31	P49841	GSK3B	Glycogen synthase kinase-3 beta	8
32	O14757	CHEK1	Serine/threonine-protein kinase Chk1	8

GO analysis.

As shown in Fig. 3, eventually, 20 markedly enriched BPs terms were identified, with transmembrane receptor protein tyrosine kinase GO: 0007169, regulation of cellular protein localization GO: 1903827, and positive regulation of kinase activity GO: 0045860 as the top ones. In addition, the size of the dot in bubble chart indicates the number of target genes in the corresponding function pathway, and the enrichment expresses the ratio of the number of target genes belonging to the number of all the annotated genes located in the pathway. Compared to other pathway, these three BPs terms mentioned above also enriched more counts of targets, while growth hormone receptor signaling pathway displayed a higher enrichment. For CCs, the items with significant enrichment were in glutamatergic synapse GO: 0098978, membrane raft GO: 0045121, and microtubule organizing center GO: 0005815; and for MFs in phosphotransferase activity, alcohol group receptor GO: 0016773, kinase binding GO: 0019900, and protein tyrosine kinase activity GO: 0004713.

KEGG pathway enrichment analysis.

The KEGG enrichment will be able to display how ginger acts on the signaling pathway, thereby playing a therapeutic role in colon cancer. Here, based on the 32 core targets, 10 significant signaling pathways were obtained (Fig. 4A), with pathways in cancer, hepatitis B, and estrogen signaling pathway as the top ones. As displayed in Fig. 4B, pathways in cancer was identified as a set of significantly key pathway with most targets enrichment, and bladder cancer showed a higher level of enrichment.

Drug-compound-disease-target-pathway network.

The drug-compound-disease-target-pathway network was shown in Fig. 5, which included 50 nodes (6 compounds, 32 targets, and 10 pathways) and 161 edges. The red rectangle node is the Chinese herbal medicine ginger; darker blue nodes is colon cancer, V nodes is 32 core target genes, purple ellipse nodes are 10 significant signaling pathway, blue ellipse node represents 6 effective active ingredients, while lines represent the interactions between them. According to the network analysis, each active compound acts on at least one target genes, and 6-gingerol was regarded as the most effective compound that interacts with 20 target genes. Most of genes were regulated by at least 2 active components, and the most significant pathway in cancer associated with 19 core targets. This network analysis indicated the characteristics of multiple components and multiple targets of ginger in the treatment of colon cancer.

Molecular docking result and analysis.

To validate the 8 core targets and their interacting compounds, the molecular docking were performed. The binding affinities of the testing compounds were compared to the corresponding ligands. As the LibDockScores displayed in Table 3 and Fig. 6, all the compounds had strong interactions than the prototype ligands, or similar effects to the ligands, except for targets SRC and PIK3R1. Among these compounds, 1-monolinolein (MOL002464), belong to fatty acid compounds, always displayed the highest binding affinities with the most testing targets protein, especially for the target HSP90AA1. The molecular docking results suggested that all these compounds can be considered as the active components of ginger against colon cancer, and meanwhile, TP53, HSP90AA1, MAPK8, JAK2, CASP3, and ERBB2 were speculated to be the potential targets for reaching this effect. The representative molecular docking result were exhibited in Fig. 7.

Table 3

The LibDock Scores of 8 core targets and their interacting compounds.
Target	PDB ID	Compounds	LibDock Score
SRC	2BDF	Ligand 1	141.019
		MOL002464	126.807
		MOL002501	108.312
		MOL002467	107.09
		MOL000358	113.863
		MOL002514	94.4539
		MOL000359	113.863
PIK3R1	4ZOP	Ligand 2	124.549
		MOL002464	122.846
		MOL002501	116.663
		MOL002467	118.603
		MOL000358	122.444
		MOL002514	96.8916
		MOL000359	120.734
TP53	5O1F	Ligand 3	105.7
		MOL002464	135.803
		MOL002501	135.516
		MOL002467	122.954
		MOL000358	115.344
		MOL002514	112.737
		MOL000359	115.344
HSP90AA1	4BQG	Ligand 4	89.7644
		MOL002464	130.891
		MOL002501	142.366
		MOL002467	119.565
		MOL000358	116.638
		MOL002514	110.012
		MOL000359	116.638
MAPK8	4E73	Ligand 5	91.9872
		MOL002464	98.8619
		MOL002501	99.3929
		MOL002467	99.8414
		MOL000358	99.7914
		MOL002514	92.4867
		MOL000359	99.7914
JAK2	3KCK	Ligand 6	106.149
		MOL002464	116.428
		MOL002501	112.673
		MOL002467	103.286
		MOL000358	101.945
		MOL002514	93.3826
		MOL000359	102.536
CASP3	1RE1	Ligand 7	85.4841
		MOL002464	111.344
		MOL002501	106.137
		MOL002467	104.814
		MOL000358	87.2257
		MOL002514	85.8912
		MOL000359	85.7436
ERBB2	3PPO	Ligand 8	103.488
		MOL002464	139.158
		MOL002501	149.566
		MOL002467	128.035
		MOL000358	136.813
		MOL002514	103.498
		MOL000359	136.762

Colon cancer is a common malignant tumor of the in the gastrointestinal system that occurs in the colon, with the complications of intestinal obstruction and lower gastrointestinal bleeding [19]. As the characteristics of safety, minimal side effects and improved the quality of life, Chinese herbs are increasingly used as cancer-preventive drug for patients who prefer a conservative treatment.

Recently, along with the development of systems biology, network pharmacology has become an advanced method to analyze the mechanism of complex components of Chinese herbs. In the current article, it have highlighted that these 6 chemical components of ginger are closely associated with the anti-colon cancer effects and related signaling pathway. 6-Gingerol, a naturally occurring plant phenol, is the most abundant components in fresh ginger, which has been shown to possess anti-tumorigenic and pro-apoptotic activities [20]. Although 6-gingerol has a lower DL value of 0.16, it was also selected for explore its underlying mechanisms on the prevention of colon cancer. In fact, this compound exhibited compact relationships with 20 disease-related targets, indicating the potential of 6-gingerol as a leading compound in colon cancer prevention. Furthermore, structural variation could be applied for improving the pharmacokinetics characteristics of 6-gingerol to reach a reasonable drug-like property. Other active components including sexangularetin, sitosterol, and beta-sitosterol also have been regarded as the potential active compounds for treating colon cancer. Previous studies have exhibited that 6-gingerol effectively play an anti-colorectal cancer effect by inhibiting leukotriene A(4) hydrolase expression and induction of G2/M arrest [21, 22]. Similarly, beta-sitosterol, a main dietary phytosterol in plants, was found to be preventive on the growth of HT116 human colon cancer cells and this function was associated with induction of Bax and activation of Caspases [23].

Through the topological indexes analysis, a total of 32 nodes that were screened as the most important targets genes contribute to the colon cancer treatment of ginger. Among these genes, proto-oncogene tyrosine-protein kinase (SRC), with the maximum interacting edges, is a family of non-receptor tyrosine kinases that plays an essential role in the selection and maturation of developing T-cells in the thymus and in the function of mature T-cells [24]. A previous study reported that SRC is a potential oncogene, which contributes to colon cancer cell proliferation through FRα/c-SRC/ERK1/2/NFκB/TP53 pathway [25]. Besides, according to the KEGG pathway enrichment analysis, SRC were highly involved in hepatitis B, adherens junction, bladder cancer, and estrogen signaling pathway. In addition, hepatitis B is a well-described complication in numerous cancer patients who receive chemotherapy and has been associated with malignancy, most notably hepatocellular carcinoma [26]. The artificial immunization through hepatitis B vaccination can reduce the risk of liver cancer, which might provide a practicable mean for primary prevention [27]. Moreover, a significant association between hepatitis B infection and the presence of colorectal adenomas was observed by Patel et al. 2015 [28]. These reports support the important role of hepatitis B infection in the treatment of colon cancer, however, larger prospective investigations are demanded to strengthen these findings.

In addition, GO enrichment analysis has offered a more systematic perspective to illustrate the essential signaling pathway of ginger on preventing colon cancer. The 20 biological processes terms with notably enriched targets have been displayed to be associated with colon cancer. For instance, transmembrane receptor protein tyrosine kinase signaling pathway, including the targets of AR, EGFR, CASP3, PIK3CB, and TAST, is predicted as the most contributing biological process for the anti-colon cancer effect of ginger. Recent interpretation of cancer pathophysiology have highlighted that tyrosine kinases are usually activated in upstream or downstream of relevant oncogenes, especially the receptor tyrosine kinases [29]. They are critical regulators of many key processes including cell survival and proliferation, which may be used as a novel therapeutic target for colon cancer. Besides, other biological process, such as regulation of reactive oxygen species (ROS) metabolic process has been found to take part in prevention of human colon cancer. It is becoming increasingly clear that ROS including superoxide and hydrogen peroxide, positioned at a crossroads potentially linking the cancer and immune microenvironment [30]. A further study revealed that metformin can reduce the risk of cancer in type II diabetic patients, and targeting mitochondrial complex I with metformin led to reduction of complex I generated ROS and reduced tumorigenesis in xenograft mouse models [31].

Collectively, 6 active compounds were identified from ginger and 288 targets in addition to 1356 disease-related targets in the treatment of colon cancer were collected. In the String analysis, a total of 32 key targets were yielded, and 10 signaling pathways including pathways in cancer, hepatitis B, and estrogen signaling pathway were observed to play an important role in the mechanism of ginger for colon cancer prevention. In addition, TP53, HSP90AA1, MAPK8, JAK2, CASP3, and ERBB2 were speculated to be the six most important target proteins. These results might be promising to searching for leading compounds and the developments of new drug for colon cancer, however, continued experiments are demanded to available the findings.

Authors

All research has been done by the authors.

Declaration of Competing Interest

Authors declare no conflict of interest.

Acknowledgements

The authors wish to thank the editor and the anonymous reviewers whose constructive comments are very helpful in strengthening the presentation of this paper. This work was financially supported by the "Xinglin scholars" talent research promotion program (NO. QNXZ2019025) and “Xinglin Scholar” Research Premotion Project of Chengdu University of TCM (NO. CXTD2018007).

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Identification of the active substances and mechanisms of ginger for the treatment of colon cancer based on network pharmacology and molecular docking

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