A network pharmacology approach for investigating the multi-target mechanisms of Huangqi in the treatment of colorectal cancer
Xiaodong ChuYiran ZhangZheng-bin LinZhanzheng ZhaoShuchen HuangfuShenghui QiuYanguan GuoHui DingTing HuangXiaoli ChuJinghua PanYun-long Pan
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Colorectal cancer (CRC) is the third most prevalent cancer globally. In the treatment of CRC, surgical resection is commonly adopted, and neoadjuvant chemotherapy or immunotherapy is mainly administered for patients with advanced disease. However, despite the developments in the field of cancer treatment, the mortality rate of CRC has remained high. Therefore, novel treatments for CRC need to be explored. Astragalus membranaceus, commonly known in China as Huangqi (HQ), a traditional Chinese medicine, has been reported to be a potential antitumorigenic agent. This study aimed to investigate the mechanisms of action of HQ.Active ingredients and putative targets of HQ were obtained through a comprehensive search of the Traditional Chinese Medicine Systems Pharmacology database. CRC-related targets were retrieved from the GeneCards database and then overlapping targets were acquired. After visualization of the compound-disease network and protein-protein interaction (PPI) network, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the overlapping genes were performed. Additionally, HCT116 cells were treated with the active components of HQ at a 20-µM concentration. Cell Counting Kit-8 was used to detect cell activity, and real-time quantitative polymerase chain reaction was carried out to detect the expression of genes downstream of the interleukin (IL)-17 signaling pathway.A PPI network comprising 177 nodes and 318 edges was obtained. The GO analysis of the overlapping genes showed enrichment in response to lipopolysaccharide and oxidative process. For the KEGG analysis, the AGE-RAGE signaling pathway and inflammation-related pathways, such as the IL-17 and tumor necrosis factor (TNF) signaling pathways, were enriched. The in vitro experiments showed that HQ promoted the apoptosis of CRC cells by inhibiting the expression of the CCL2, CXCL8, CXCL10, and PTGS2 genes.This study systematically revealed the multitarget mechanism of HQ in CRC through a network pharmacology approach. We verified that HQ promotes CRC cell death via the IL-17 signaling pathway. This finding provides indications for further mechanistic studies and the development of HQ as a potential treatment for CRC patients.Keywords:
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KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.
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The KEGG database and analysis tools (https://www.kegg.jp) have been developed mostly for understanding genes and genomes of cellular organisms. The KO (KEGG Orthology) dataset, which is a collection of functional orthologs, plays the role of linking genes in the genome to pathways and other molecular networks, enabling KEGG mapping to uncover hidden features in the genome. Although viruses were part of KEGG for some time, they were not fully integrated in the KEGG analysis tools, because the KO assignment rate is very low for virus genes. To supplement KOs a new dataset named virus ortholog clusters (VOCs) is computationally generated, covering 90% of viral proteins in KEGG. VOCs can be used, in place of KOs, for taxonomy mapping to uncover relationships of sequence similarity groups and taxonomic groups and for identifying conserved gene orders in virus genomes. Furthermore, selected VOCs are used to define tentative KOs for characterizing protein functions. Here an overview of KEGG tools is presented focusing on these extensions for viral protein analysis.
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KEGG ( http://www.genome.jp/kegg/ ) is a database of biological systems that integrates genomic, chemical and systemic functional information. KEGG provides a reference knowledge base for linking genomes to life through the process of PATHWAY mapping, which is to map, for example, a genomic or transcriptomic content of genes to KEGG reference pathways to infer systemic behaviors of the cell or the organism. In addition, KEGG provides a reference knowledge base for linking genomes to the environment, such as for the analysis of drug-target relationships, through the process of BRITE mapping. KEGG BRITE is an ontology database representing functional hierarchies of various biological objects, including molecules, cells, organisms, diseases and drugs, as well as relationships among them. KEGG PATHWAY is now supplemented with a new global map of metabolic pathways, which is essentially a combined map of about 120 existing pathway maps. In addition, smaller pathway modules are defined and stored in KEGG MODULE that also contains other functional units and complexes. The KEGG resource is being expanded to suit the needs for practical applications. KEGG DRUG contains all approved drugs in the US and Japan, and KEGG DISEASE is a new database linking disease genes, pathways, drugs and diagnostic markers.
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Abstract Background The MetaCyc and KEGG projects have developed large metabolic pathway databases that are used for a variety of applications including genome analysis and metabolic engineering. We present a comparison of the compound, reaction, and pathway content of MetaCyc version 16.0 and a KEGG version downloaded on Feb-27-2012 to increase understanding of their relative sizes, their degree of overlap, and their scope. To assess their overlap, we must know the correspondences between compounds, reactions, and pathways in MetaCyc, and those in KEGG. We devoted significant effort to computational and manual matching of these entities, and we evaluated the accuracy of the correspondences. Results KEGG contains 179 module pathways versus 1,846 base pathways in MetaCyc; KEGG contains 237 map pathways versus 296 super pathways in MetaCyc. KEGG pathways contain 3.3 times as many reactions on average as do MetaCyc pathways, and the databases employ different conceptualizations of metabolic pathways. KEGG contains 8,692 reactions versus 10,262 for MetaCyc. 6,174 KEGG reactions are components of KEGG pathways versus 6,348 for MetaCyc. KEGG contains 16,586 compounds versus 11,991 for MetaCyc. 6,912 KEGG compounds act as substrates in KEGG reactions versus 8,891 for MetaCyc. MetaCyc contains a broader set of database attributes than does KEGG, such as relationships from a compound to enzymes that it regulates, identification of spontaneous reactions, and the expected taxonomic range of metabolic pathways. MetaCyc contains many pathways not found in KEGG, from plants, fungi, metazoa, and actinobacteria; KEGG contains pathways not found in MetaCyc, for xenobiotic degradation, glycan metabolism, and metabolism of terpenoids and polyketides. MetaCyc contains fewer unbalanced reactions, which facilitates metabolic modeling such as using flux-balance analysis. MetaCyc includes generic reactions that may be instantiated computationally. Conclusions KEGG contains significantly more compounds than does MetaCyc, whereas MetaCyc contains significantly more reactions and pathways than does KEGG, in particular KEGG modules are quite incomplete. The number of reactions occurring in pathways in the two DBs are quite similar.
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The KEGG-PATH approach, a kind of data mining through functional enrichment analysis of time-course experiments or those involving multiple treatments, can uncover the complex regulation mechanisms of KEGG pathways through the subdivision of total effect.
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Abstract KEGG (Kyoto Encyclopedia of Genes and Genomes) is a bioinformatics resource for understanding the functions and utilities of cells and organisms from both high‐level and genomic perspectives. It is a self‐sufficient, integrated resource consisting of genomic, chemical, and network information, with cross‐references to numerous outside databases, containing a complete set of building blocks (genes and molecules) and wiring diagrams (interaction networks) to represent cellular functions. This unit describes protocols for using KEGG, KEGG PATHWAY, KEGG GENES, KEGG SSDB, KEGG EXPRESSION, and KEGG LIGAND. Protocols are also described for how to color maps, compare chemical compounds and glycan chains, analyze Ortholog Clusters, and visualize and analyze microarray data, among other procedures. All of these many protocols enable the user to take advantage of the full breadth of the functionality provided by KEGG.
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KEGG (Kyoto Encyclopedia of Genes and Genomes) is a bioinformatics resource for understanding the functions and utilities of cells and organisms from both high-level and genomic perspectives. It is a self-sufficient, integrated resource consisting of genomic, chemical, and network information, with cross-references to numerous outside databases, containing a complete set of building blocks (genes and molecules) and wiring diagrams (interaction networks) to represent cellular functions. This unit describes protocols for using KEGG, KEGG PATHWAY, KEGG GENES, KEGG SSDB, KEGG EXPRESSION, and KEGG LIGAND. Protocols are also described for how to color maps, compare chemical compounds and glycan chains, analyze Ortholog Clusters, and visualize and analyze microarray data, among other procedures. All of these many protocols enable the user to take advantage of the full breadth of the functionality provided by KEGG.
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