Abstract
Background Aloe vera has long been considered an anticancer herb in different parts of the world. Objective To explore the potential mechanism of aloe vera in the treatment of cancer using network pharmacology and molecule docking approaches. Methods The active ingredients and corresponding protein targets of aloe vera were identified from the TCMSP database. Targets related to cancer were obtained from GeneCards and OMIM databases. The anticancer targets of aloe vera were obtained by intersecting the drug targets with the disease targets, and the process was presented in the form of a Venn plot. These targets were uploaded to the String database for protein-protein interaction (PPI) analysis, and the result was visualized by Cytoscape software. Go and KEGG enrichment were used to analyze the biological process of the target proteins. Molecular docking was used to verify the relationship between the active ingredients of aloe vera and predicted targets. Results By screening and analyzing, 8 active ingredients and 174 anticancer targets of aloe vera were obtained. The active ingredient-anticancer target network constructed by Cytoscape software indicated that quercetin, arachidonic acid, aloe-emodin, and beta-carotene, which have more than 4 gene targets, may play crucial roles. In the PPI network, AKT1, TP53, and VEGFA have the top 3 highest values. The anticancer targets of aloe vera were mainly involved in pathways in cancer, prostate cancer, bladder cancer, pancreatic cancer, and non-small-cell lung cancer and the TNF signaling pathway. The results of molecular docking suggested that the binding ability between TP53 and quercetin was the strongest. Conclusion This study revealed the active ingredients of aloe vera and the potential mechanism underlying its anticancer effect based on network pharmacology and provided ideas for further research.
Highlights
Aloe vera (L.) Burm.f, a traditional herb belonging to the Liliaceae family, has been used around the world for a long time [1]
“Aloe” was used as the keyword to get the ingredients of aloe vera. e active ingredients should meet pharmacokinetic ADME criteria [19]. e related protein targets of aloe vera were obtained by searching and merging the related targets of each effective compound in Traditional Chinese Medicine Database and Analysis Platform (TCMSP). e gene names of targets were standardized in the UniProt database, and only the genes that have been “Reviewed” and belong to “Human” were left [20]
Verification by Molecular Docking Simulation. e network pharmacology revealed potential active ingredients of aloe vera and the key targets and pathways of aloe vera against cancer. e active compounds of aloe vera which have more than 4 gene targets were selected as the ligands and their 3D structures were searched in the PubChem database and prepared using the Chem3D software. en, the ligands were saved in the format of PDBQT after adding hydrogens, computing Gasteiger charges, detecting the root, and choosing rotatable bonds by using AutoDock Tools
Summary
Aloe vera (L.) Burm.f (synonym A. barbadensis Mill.), a traditional herb belonging to the Liliaceae family, has been used around the world for a long time [1] It contains various active compounds, including anthraquinones, naphthalenones, and polysaccharides [2]. To explore the potential mechanism of aloe vera in the treatment of cancer using network pharmacology and molecule docking approaches. E active ingredients and corresponding protein targets of aloe vera were identified from the TCMSP database. Molecular docking was used to verify the relationship between the active ingredients of aloe vera and predicted targets. 8 active ingredients and 174 anticancer targets of aloe vera were obtained. Is study revealed the active ingredients of aloe vera and the potential mechanism underlying its anticancer effect based on network pharmacology and provided ideas for further research Conclusion. is study revealed the active ingredients of aloe vera and the potential mechanism underlying its anticancer effect based on network pharmacology and provided ideas for further research
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