An article, published in BioData Mining, aimed to identify the bioactive compounds in ginger as a treatment for colon cancer by developing an integrated network pharmacology and molecular docking approach.
Colorectal cancer is a broad term that includes patients that suffer from rectal cancer and colon cancer. In 2018 there were 1,849,518 new colorectal cancer cases worldwide and almost 900,000 deaths. There are increasing therapeutic approaches for colon cancers available, including conventional treatments of surgical resection and radiotherapy, but also new interventional drug therapies. However, the high costs of molecular targeted therapies and the side effects induced by chemotherapy, such as nausea and digestive tract irritation reaction, have led to incidences of treatment dropout.
Historically, medicinal botanicals, as part of complementary medicine in the US, have provided an important resource for the discovery of anticancer drug agents, with more than half of currently available drugs being related to them. Ginger rhizome (Zingiber officinale) is the major medicinal part of the ginger plant. This contains the pungent phenolic compounds, volatile oil, diarylheptanoids and phenylalkanoids. These compounds have been found to cause a wide range of pharmacological effects. In addition to that, the components derived from ginger, 6-gingerol and 6-shogaol have been reported to prevent cell proliferation against colon cancer, such as colon-26 tumours. However, it is not yet known whether other components possess anticancer activity.
Identification of candidate bioactive compounds in ginger
Network pharmacology has been successfully introduced to reveal the therapeutic mechanisms of traditional Chinese medicine (TCM) compounds. In this study, the researchers used network pharmacology to establish the component-targets-pathways-disease network, to investigate the potential mechanisms of ginger in colon cancer prevention.
Firstly, the researchers collected the candidate compounds and intersection genes for colon cancer. The traditional Chinese medicine systems pharmacology (TCMSP) database was used to collate the potential active components of ginger, by filtering the metric of oral bioactivity (OB) and drug-likeness (DL). The TCMSP database is a platform that contains many compounds from herbal medicine, related protein targets and their pharmacokinetic properties. DL refers to the qualitative property of chemicals, whereas OB refers to the relative amount and rate at which an oral drug is absorbed. Calculations of OB and DL are obtained using machine learning methods or the Tanimoto coefficient, and are commonly used for filtering out compounds that are unlikely to have therapeutic effects.
Once the candidate compounds were identified, protein targets found to be interacting with those potential active compounds were predicted through TCMSP and Swiss Target Prediction databases. The researchers then constructed the protein-protein interaction (PPI) and components-target-pathways-disease network. The targets interacting with colon cancer were collected using Genecards, OMIM and Drugbank databases.
Gene Ontology was used to functionally annotate key genes into the three main categories, cellular components (CCs), molecular functions (MFs) and biological processes (BPs). Meanwhile, KEGG enrichment analysis was used to unveil the possible biological process of key genes.
Validating the identified bioactive compounds in Ginger
Finally, the potential bioactive components and core targets were further validated by the molecular docking simulation. A total of 8 key genes including SRC, PIK3R1, TP53, HSP90AA1, MAPK8, JAK2, CASP3 and ERBB2, were included in the molecular docking simulation. Discovery studio software was used, and the screened active components were prepared using the ‘prepare ligands’ module to obtain a 3D conformation. After removing crystallographic water molecules, the ‘prepare protein’ module was used to remove the polyconformation of the target protein and to supplement the incomplete amino acid residues. The ‘LibDock’ module and LibDockScores were then used to evaluate affinity of the target protein and the active components. The LibDockScore of the target protein and its corresponding prototype ligand was viewed as the threshold, and the components with higher scores were regarded as active for interacting with this protein.
Identification of mechanisms of ginger for the treatment of colon cancer
The researchers collected six potential active compounds, 285 interacting targets and 1356 disease-related targets. This study identified a total of 34 key targets, including PIK3CA, SRC and TP53, through PPI analysis. The identified targets were mainly focused on the processes of phosphatidylinositol 3-kinase signalling, cellular response to oxidative stress and cellular response to peptide hormone stimulus. The KEGG enrichment revealed that three signalling pathways were closely related to the colon cancer prevention properties of ginger, as well as endocrine resistance and hepatitis B pathways. The molecular docking simulation validated that TP53, HSP90AA1 and JAK2 were the most targeted genes.
This study used integrated network pharmacology and molecular docking to demonstrate that ginger produces preventative effects against colon cancer. They were able to validate multiple components and multiple targets using their integrated approach. These results provide a promising start in search for leading compounds and the development of new therapeutics to treat colon cancer.
Image credit: FreePik