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

Abstract

Background and objective Colon cancer is occurring at an increasing rate and ginger (Zingiber officinale), as a commonly used herbal medicine, has been suggested as a potential agent for colon cancer. This study was aimed to identify the bioactive components and potential mechanisms of ginger for colon cancer prevention by an integrated network pharmacology approach. Methods The putative ingredients of ginger and its related targets were discerned from the TCMSP and Swiss target prediction database. After that, the targets interacting with colon cancer were collected using Genecards, OMIM, and Drugbank databases. KEGG pathway and GO enrichment analysis were performed to explore the signaling pathways related to ginger for colon cancer treatments. The PPI and compound-target-disease networks were constructed using Cytoscape 3.8.1. Finally, Discovery studio software was employed to confirm the key genes and active components from ginger. Results Six potential active compounds, 285 interacting targets in addition to 1356 disease-related targets were collected, of which 118 intersection targets were obtained. A total of 34 key targets including PIK3CA, SRC, and TP53 were identified through PPI network analysis. These targets were mainly focused on the biological processes of phosphatidylinositol 3-kinase signaling, cellular response to oxidative stress, and cellular response to peptide hormone stimulus. The KEGG enrichment manifested that three signaling pathways were closely related to colon cancer prevention of ginger, cancer, endocrine resistance, and hepatitis B pathways. TP53, HSP90AA1, and JAK2 were viewed as the most important genes, which were validated by molecular docking simulation. Conclusion This study demonstrated that ginger produced preventive effects against colon cancer by regulating multi-targets and multi-pathways with multi-components. And, the combined data provide novel insight for ginger compounds developed as new drug for anti-colon cancer.

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

“…Faced with this situation, we consider the outcome of the coefficient of determination more reliable and trustworthy: similarly to the Matthews correlation coefficient (MCC) ( Matthews, 1975 ) in binary classification ( Chicco & Jurman, 2020 ; Chicco, Tötsch & Jurman, 2021 ; Tötsch & Hoffmann, 2021 ; Chicco, Starovoitov & Jurman, 2021 ; Chicco, Warrens & Jurman, 2021 ), R -squared generates a high score only if the regression is able to correctly classify most of the elements of each class. In this example, the regression fails to classify all the elements of the class and of the class, so we believe a good metric would communicate this key-message.…”

Micropropagation of Ginger (Zingiber officinale Roscoe) ‘Bentong’ and Evaluation of Its Secondary Metabolites and Antioxidant Activities Compared with the Conventionally Propagated Plant

“…Ginger (Zingiber officinale Roscoe), a herbal plant from the Zingiberaceae family, is one of those plants. It has been used as spice and medicine for treating cancer [1]; cardiovascular disease [2]; diabetes [3]; and several other illnesses such as cold, nausea, asthma, and cough [4]. Owing to the global pandemic of COVID-19, ginger consumption gained more interest.…”

Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis

“…Among ensemble ML algorithms, RF represents a flexible non-parametric approach with several properties such as invariant to monotonic transformation, robustness to outliers, stability in the presence of correlated variables, or interaction among features [ 7 , 10 ]. Despite these advantages, in a high-dimensional setting “large P-variables small N-sample size”, RF may provide poor accuracy, especially if complex variable interactions (e.g., gene-gene) exist.…”

“…The potential of ML and RF to select important genes related to particular conditions has been recognized in life science [ 7 , 10 ], but they are considered as black-box models, hindering decisions making based on their results. For this reason, the involvement of prior knowledge encapsulated through networks that describe gene-gene interaction has been explored, improving the performance of the models [ 11 – 14 ].…”