RESUMO
Aim To investigate the pharmacological mechanism of Weimaining in the treatment of lung ade¬nocarcinoma ( LUAD) , using a network pharmacology and molecular docking approach. Methods The active components and potential targets of Weimanin ( Rhizo- ma Fagopyri Cymosi) were screened out through TCM- SP, TCMID, BATMAN-TCM and ETCM data plat-form, and supplemented with literature. The gene ex¬pression data of LUAD were obtained from the Gen Ex¬pression Omnibus database(GEO) , and the differenti¬ally expressed genes were determined using R software. A protein-protein interaction( PPI) network of intersec¬tion targets was constructed by STRING and visualized by Cytoscape software, and GO functional annotation and KEGG pathway enrichment analysis were per¬formed by Metascape platform. Finally, molecular doc¬king studies were carried out to verify the binding of core components and targets. Results Selecting the OB and DL as filter condition, 16 active ingredients and 353 potential targets were involved. MMP-9, MMP-1, CAT and other targets were closely related to LUAD. The KEGG analysis showed that target genes were enriched in several key cancer-related signaling pathways, including the Fluid shear stress and athero¬sclerosis, Pathways in cancer, AGE-RAGE signaling pathway, p53 signaling pathway, etc. Conclusions The present study investigates the main active compo¬nents, targets and related pathways of Weimaining in the treatment of LUAD, which provides the theoretical basis and ideas for further research.
RESUMO
OBJECTIVE： To investigate the potential effective components and mechanism of Achyranthes bidentata in the treatment of osteoporosis （OP）. METHODS： The effective components of A. bidentata were retrieved from the TCMSP database， and corresponding targets of them were collected. The targets related to OP were retrieved from DisGeNET database. TBtools 1.0 mapping software was used to draw the Wayne diagram， and screen the intersecting targets of effective components of A. bidentata and disease OP. Cytoscape 3.6.1 software and STRING database were used to construct and analyze the “drug-component- disease-target” network and protein-protein interaction （PPI） network； KEGG pathway enrichment analysis was conducted by using DAVID bioinformatics resource database. RESULTS： A total of 19 effective components were screened from A. bidentata， and there were 32 intersecting targets between effective components and disease OP. In “drug-component-disease-target” network， there were 45 nodes [1 for A. bidentata， 1 for OP， 11 for effective components （8 of the 19 effective components had no corresponding OP target）， 32 for intersecting targets] and 119 edges between nodes； quercetin， kaempferol， wogonin， baicalein and palmatine were important effective components. In PPI network， there were 31 nodes （1 of 32 intersecting targets was not associated with other proteins） and 212 edges， among which IL6， ESR1， MAPK1， IL8 and MAPK14 were the core targets of the network. There were 67 KEGG enrichment pathways， including rheumatoid arthritis， hepatitis B， Toll-like receptor signaling pathway， PI3K/Akt signaling pathway， JAK/STAT signaling pathway， NF-κB signaling pathway and so on. CONCLUSIONS： The main potential effective components of A. bidentata in the treatment of OP are quercetin， kaempferol， wogonin， baicalein and palmatine， the mechanism of which may be associated with cell differentiation and apoptosis， metabolism， inflammation reaction， etc. It has multi-component， multi-target and multi-system chara- cteristics.
