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pmid:34471638
Uncovering the Mechanisms and Molecular Targets of Weibing Formula 1 against Gastritis: Coupling Network Pharmacology with GEO Database.
Ke Chen · Luojian Zhang · Zhen Qu · Feng Wan · Jia Li · Ye Yang · Hui Yan · Shile Huang
BioMed research international · 2021 doi:10.1155/2021/5533946
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Complete file
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However, the potential pharmacological mechanism of Weibing Formula 1 is still unclear to date. A network pharmacology-based strategy was performed to uncover the underlying mechanisms of Weibing Formula 1 against gastritis. Furthermore, we structured the drug-active ingredients-genes-disease network and PPI network of shared targets, and function enrichment analysis of these targets was carried out. Ultimately, Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR were used to verify the related genes. We found 251 potential targets corresponding to 135 bioactive components of Weibing Formula 1. Then, 327 gastritis-related targets were known gastritis-related targets. Among which, 60 common targets were shared between potential targets of Weibing Formula 1 and known gastritis-related targets. The results of pathway enrichment analysis displayed that 60 common targets mostly participated in various pathways related to Toll-like receptor signaling pathway, MAPK signaling pathway, cytokine-cytokine receptor interaction pathway, chemokine signaling pathway, and apoptosis. Based on the GSE60427 dataset, 15 common genes were shared between differentially expressed genes and 60 candidate targets. The verification results of the GSE5081 dataset showed that except for DUOX2 and VCAM1, the other 13 genes were significantly upregulated in gastritis, which was consistent with the results in the GSE60427 dataset. More importantly, real-time quantitative PCR results showed that the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly upregulated and NOS2, EGFR, and IL-10 were downregulated in gastritis patients, while the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly downregulated and NOS2, EGFR, and IL-10 were upregulated after the treatment of Weibing Formula 1. PTGS2, NOS2, EGFR, MMP9, CXCL2, CXCL8, and IL-10 may be the important direct targets of Weibing Formula 1 in gastritis treatment. Our study revealed the mechanism of Weibing Formula 1 in gastritis from an overall and systematic perspective, providing a theoretical basis for further knowing and application of this formula in the future.", "claims": [{"id": "claim-method", "statement": "Then, the Benjamini-Hochberg method was used to improve the false discovery rate (FDR) in multiple comparisons.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34471638", "locator": "sections[10] \"2.8. 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GEO Data Verified Differentially Expressed Genes", "row_count": 21, "column_count": 7}, "table": {"label": "Table 3", "caption": "The top 20 significantly enriched pathway between gastritis and normal control.", "rows": [["Name", "Size", "ES", "NES", "NOM p val", "FDR q val", "FWER p val"], ["Pathways in cancer", "322", "-0.444", "-1.662", "p ≤ 0.001", "0.051", "0.402"], ["MAPK signaling pathway", "264", "-0.403", "-1.47", "0.004", "0.104", "0.841"], ["Cytokine-cytokine receptor interaction", "252", "-0.706", "-1.703", "p ≤ 0.001", "0.043", "0.313"], ["Regulation of actin cytoskeleton", "211", "-0.362", "-1.396", "0.03", "0.142", "0.913"], ["Focal adhesion", "197", "-0.463", "-1.491", "0.045", "0.09", "0.804"], ["Chemokine signaling pathway", "183", "-0.747", "-1.793", "p ≤ 0.001", "0.039", "0.146"], ["Endocytosis", "176", "-0.407", "-1.86", "0.004", "0.028", "0.079"], ["Jak Stat signaling pathway", "147", "-0.572", "-1.731", "p ≤ 0.001", "0.044", "0.247"], ["Alzheimer's disease", "143", "-0.384", "-1.463", "0.032", "0.107", "0.848"], ["Insulin signaling pathway", "137", "-0.356", "-1.45", "0.046", "0.115", "0.864"], ["Cell adhesion molecules (CAMs)", "130", "-0.701", "-1.652", "p ≤ 0.001", "0.053", "0.425"], ["Natural killer cell-mediated cytotoxicity", "124", "-0.634", "-1.707", "p ≤ 0.001", "0.044", "0.304"], ["Neurotrophin signaling pathway", "124", "-0.407", "-1.624", "0.011", "0.052", "0.502"], ["Leukocyte transendothelial migration", "115", "-0.645", "-1.686", "p ≤ 0.001", "0.044", "0.348"], ["Vascular smooth muscle contraction", "112", "-0.468", "-1.431", "0.016", "0.127", "0.884"], ["T cell receptor signaling pathway", "106", "-0.641", "-1.758", "p ≤ 0.001", "0.043", "0.2"], ["Melanogenesis", "101", "-0.414", "-1.409", "0.032", "0.135", "0.899"], ["GnRH signaling pathway", "100", "-0.439", "-1.528", "0.01", "0.071", "0.734"], ["Parkinson's disease", "98", "-0.41", "-1.584", "0.025", "0.065", "0.615"], ["Toll-like receptor signaling pathway", "96", "-0.681", "-1.77", "p ≤ 0.001", "0.042", "0.175"]], "row_count": 21, "column_count": 7, "numeric_cells": 120, "header_rows": 1, "locator": "section[18]/table-wrap[2]#tab3"}}], "gaps": ["asset:data:geo:GSE5081 not downloaded", "asset:data:geo:GSE5081 licence not read", "asset:data:geo:GSE60427 not downloaded", "asset:data:geo:GSE60427 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "PPI"}}, {"id": "method-2", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}, {"id": "method-3", "family": null, "specific": null, "label": "Benjamini-Hochberg method", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Then, the Benjamini-Hochberg method was used to improve the false discovery rate (FDR) in multiple comparisons."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: ppi_network."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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