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Effects and possible mechanism of Ruyiping formula application to breast cancer based on network prediction.

ABSTRACT: Ruyiping (RYP), a Chinese herbal formula, can remove toxin and clear nodular, showing ability of preventing postoperative recurrence of breast cancer. In this study, network was performed to predict possible targets, genes and pathways associated with RYP and breast cancer. Thin Layer Chromatography (TLC) and High Performance Liquid Chromatography (HPLC) were used to quantitatively study RYP formula and its single herbs. MTT methods, Luciferase reporter systems, zebrafish model and western blotting were respectively adopted to verify network prediction. Results showed that the quality of RYP could be controlled and icariin could be selected as mark ingredient; RYP expressed anti-breast tumor effects, which could be associated with inhibiting expression of Transforming Growth Factor ? (TGF?), promoting cells apoptosis and anti-angiogenesis. Parts of these results were consistent with network predictions in some degree, but not all. Network can help us narrow areas, focus on crucial factors, save money as well as time, but the results predicted by network should be confirmed by further experiments.

SUBMITTER: Xie RF

PROVIDER: S-EPMC6437177 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Effects and possible mechanism of Ruyiping formula application to breast cancer based on network prediction.

Xie Rui-Fang RF Liu Sheng S Yang Ming M Xu Jia-Qi JQ Li Zhi-Cheng ZC Zhou Xin X

Scientific reports 20190327 1

Ruyiping (RYP), a Chinese herbal formula, can remove toxin and clear nodular, showing ability of preventing postoperative recurrence of breast cancer. In this study, network was performed to predict possible targets, genes and pathways associated with RYP and breast cancer. Thin Layer Chromatography (TLC) and High Performance Liquid Chromatography (HPLC) were used to quantitatively study RYP formula and its single herbs. MTT methods, Luciferase reporter systems, zebrafish model and western blott ...[more]

PMID: 30918282

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Project description:ObjectiveTo elucidate the pharmacological mechanisms of Qubi Formula (QBF), a traditional Chinese medicine (TCM) formula which has been demonstrated as an effective therapy for psoriasis in China.MethodsThe Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, BATMAN-TCM database, and literature search were used to excavate the pharmacologically active ingredients of QBF and to predict the potential targets. Psoriasis-related targets were obtained from Therapeutic Target Database (TTD), DrugBank database (DBD), MalaCards database, and DisGeNET database. Then, we established the network concerning the interactions of potential targets of QBF with well-known psoriasis-related targets by using protein-protein interaction (PPI) data in String database. Afterwards, topological parameters (including DNMC, Degree, Closeness, and Betweenness) were calculated to excavate the core targets of Qubi Formula in treating psoriasis (main targets in the PPI network). Cytoscape was used to construct the ingredients-targets core network for Qubi Formula in treating psoriasis, and ClueGO was used to perform GO-BP and KEGG pathway enrichment analysis on these core targets.ResultsThe ingredient-target-disease core network of QBF in treating psoriasis was screened to contain 175 active ingredients, which corresponded to 27 core targets. Additionally, enrichment analysis suggested that targets of QBF in treating psoriasis were mainly clustered into multiple biological processes (associated with nuclear translocation of proteins, cellular response to multiple stimuli (immunoinflammatory factors, oxidative stress, and nutrient substance), lymphocyte activation, regulation of cyclase activity, cell-cell adhesion, and cell death) and related pathways (VEGF, JAK-STAT, TLRs, NF-κB, and lymphocyte differentiation-related pathways), indicating the underlying mechanisms of QBF on psoriasis.ConclusionIn this work, we have successfully illuminated that Qubi Formula could relieve a wide variety of pathological factors (such as inflammatory infiltration and abnormal angiogenesis) of psoriasis in a "multicompound, multitarget, and multipathway" manner by using network pharmacology. Moreover, our present outcomes might shed light on the further clinical application of QBF on psoriasis treatment.

Project description:BackgroundCoronavirus disease 2019 (COVID-19) is a new global public health emergency. The therapeutic benefits of Cold‒Damp Plague Formula (CDPF) against COVID-19, which was used to treat "cold‒dampness stagnation in the lung" in Trial Versions 6 and 7 of the "Diagnosis and Treatment Protocol for COVID-19", have been demonstrated, but the effective components and their mechanism of action remain unclear.MethodsIn this study, a network pharmacology approach was employed, including drug-likeness evaluation, oral bioavailability prediction, protein‒protein interaction (PPI) network construction and analysis, Gene Ontology (GO) terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation, and virtual docking, to predict the bioactive components, potential targets, and molecular mechanism of CDPF for COVID-19 treatment.ResultsThe active compound of herbs in CDPF and their candidate targets were obtained through database mining, and an herbs-ingredients-targets network was constructed. Subsequently, the candidate targets of the active compounds were compared to those relevant to COVID-19, to identify the potential targets of CDPF for COVID-19 treatment. Subsequently, the PPI network was constructed, which provided a basis for cluster analysis and hub gene screening. The seed targets in the most significant module were selected for further functional annotation. GO enrichment analysis identified four main areas: (1) cellular responses to external stimuli, (2) regulation of blood production and circulation, (3) free radical regulation, (4) immune regulation and anti-inflammatory effects. KEGG pathway analysis also revealed that CDPF could play pharmacological roles against COVID-19 through "multi components‒multi targets‒multi pathways" at the molecular level, mainly involving anti-viral, immune-regulatory, and anti-inflammatory pathways; consequently, a "CDPF-herbs-ingredients-targets-pathways-COVID-19" network was constructed. In hub target analysis, the top hub target IL6, and ACE2, the receptor via which SARS-CoV-2 typically enters host cells, were selected for molecular docking analyses, and revealed good binding activities.ConclusionsThis study revealed the active ingredients and potential molecular mechanism by which CDPF treatment is effective against COVID-19, and provides a reference basis for the wider application and further mechanistic investigations of CDPF in the fight against COVID-19.

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Effects and possible mechanism of Ruyiping formula application to breast cancer based on network prediction.

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Effects and possible mechanism of Ruyiping formula application to breast cancer based on network prediction.

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