| Literature DB >> 30854000 |
Liangjun Yang1, Wei Liu1, Zhipeng Hu2, Maoyi Yang2, Jiali Li1, Xiangzhen Fan3, Huafeng Pan1.
Abstract
The Wei Pi Xiao (WPX) decoction, based on the theory of traditional Chinese medicine, has been widely used for the treatment of gastric precancerous lesions (GPL). Although WPX is known to be effective for the treatment of GPL, its active ingredients, cellular targets, and the precise molecular mechanism of action are not known. This study aimed to identify the multiple mechanisms of action of the WPX decoction in the treatment of GPL. The active compounds, drug targets, and the key pathways involved in the therapeutic effect of WPX in the treatment of GPL were analyzed by an integrative analysis pipeline. The information pertaining to the compounds present in WPX and their disease targets was obtained from TCMSP and GeneCards, respectively. The mechanisms underlying the therapeutic effect of WPX were investigated with gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A total of 82 bioactive compounds and 146 related targets were identified in this study. Following target analyses, the targets were further mapped to 26 key biological processes and 21 related pathways to construct a target-pathway network and an integrated GPL pathway. The study demonstrated that the WPX formula primarily treats the dysfunctions of GPL arising from cell proliferation, apoptosis, and mucosal inflammation, which offered a novel insight into the pathogenesis of GPL and revealed the molecular mechanism underlying the therapeutic effects of the WPX formula in GPL. This study offers a novel approach for the systematic investigation of the mechanisms of action of herbal medicines, which will provide an impetus to the GPL drug development pipeline.Entities:
Year: 2019 PMID: 30854000 PMCID: PMC6378068 DOI: 10.1155/2019/1562707
Source DB: PubMed Journal: Evid Based Complement Alternat Med ISSN: 1741-427X Impact factor: 2.629
Figure 1The protocol of the systems pharmacology approach used in this study.
24 representative components from WPX and their corresponding predicted OB, DL, Caco-2 scores and structure.
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| 1 | MOL000006 | Luteolin | 36.16 | 0.25 | 0.19 |
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| 2 | MOL000043 | Atractylenolide I | 37.37 | 0.15 | 1.30 |
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| 3 | MOL000098 | Quercetin | 46.43 | 0.28 | 0.05 |
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| 4 | MOL000211 | Mairin | 55.38 | 0.78 | 0.73 |
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| 5 | MOL000239 | Jaranol | 50.83 | 0.29 | 0.61 |
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| 6 | MOL000263 | Oleanolic acid | 29.02 | 0.76 | 0.59 |
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| 7 | MOL000296 | Hederagenin | 36.91 | 0.75 | 1.32 |
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| 8 | MOL000354 | Isorhamnetin | 49.60 | 0.31 | 0.31 |
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| 9 | MOL000358 | Beta-sitosterol | 36.91 | 0.75 | 1.32 |
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| 10 | MOL000409 | Astragaloside IV | 17.74 | 0.15 | (2.22) |
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| 11 | MOL000417 | Calycosin | 47.75 | 0.24 | 0.52 |
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| 12 | MOL000422 | kaempferol | 41.88 | 0.24 | 0.26 |
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| 13 | MOL000449 | Stigmasterol | 43.83 | 0.76 | 1.44 |
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| 14 | MOL000902 | Curcumol | 103.55 | 0.13 | 1.12 |
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| 15 | MOL000906 | Wenjine | 47.93 | 0.27 | 0.30 |
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| 16 | MOL001659 | Poriferasterol | 43.83 | 0.76 | 1.44 |
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| 17 | MOL001689 | Acacetin | 34.97 | 0.24 | 0.67 |
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| 18 | MOL006554 | Taraxerol | 38.40 | 0.77 | 1.37 |
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| 19 | MOL006756 | Schottenol | 37.42 | 0.75 | 1.33 |
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| 20 | MOL007111 | Isotanshinone II | 49.92 | 0.40 | 1.03 |
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| 21 | MOL007134 | Danshensu | 36.91 | 0.06 | -0.27 |
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| 22 | MOL007151 | Tanshindiol B | 42.67 | 0.45 | 0.05 |
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| 23 | MOL007154 | Tanshinone IIA | 49.89 | 0.40 | 1.05 |
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| 24 | MOL007156 | Tanshinone VI | 45.64 | 0.30 | 0.48 |
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Figure 2Gene ontology (GO) analysis of the target genes associated with GPL. The X-axis represents the significant enrichment counts of these terms, while the Y-axis represents the categories of ‘biological process' in the GO of the target genes (FDR ≤ 0.01).
Figure 3The C-T network generated in this study. The red nodes represent the potential targets, and the green nodes represent the herbal compounds, while the lines represent the interactions between them.
Figure 4The T-P network generated in this study. The blue nodes represent potential targets and the red nodes represent the related pathways. The sizes of the nodes are in proportion to their degree.
Figure 5The GPL pathway constructed in this study. The orange nodes represent the potential disease protein targets, while the green nodes represent the relevant targets in the pathway.