Investigate the mechanisms of Chinese medicine Fuzhengkangai towards EGFR mutation-positive lung adenocarcinomas by network pharmacology

Abstract

BackgroundChinese traditional herbal medicine Fuzhengkangai (FZKA) formulation combination with gefitinib can overcome drug resistance and improve the prognosis of lung adenocarcinoma patients. However, the pharmacological and molecular mechanisms underlying the active ingredients, potential targets, and overcome drug resistance of the drug are still unclear. Therefore, it is necessary to explore the molecular mechanism of FZKA.MethodsA systems pharmacology and bioinformatics-based approach was employed to investigate the molecular pathogenesis of EGFR-TKI resistance with clinically effective herb formula. The differential gene expressions between EGFR-TKI sensitive and resistance cell lines were calculated and used to find overlap from targets as core targets. The prognosis of core targets was validated from the cancer genome atlas (TCGA) database by Cox regression. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment is applied to analysis core targets for revealing mechanism in biology.ResultsThe results showed that 35 active compounds of FZKA can interact with eight core targets proteins (ADRB2, BCL2, CDKN1A, HTR2C, KCNMA1, PLA2G4A, PRKCA and LYZ). The risk score of them were associated with overall survival and relapse free time (HR = 6.604, 95% CI: 2.314–18.850; HR = 5.132, 95% CI: 1.531–17.220). The pathway enrichment suggested that they involved in EGFR-TKI resistance and non-small cell lung cancer pathways, which directly affect EGFR-TKI resistance. The molecular docking showed that licochalcone a and beta-sitosterol can closely bind two targets (BCL2 and PRKCA) that involved in EGFR-TKI resistance pathway.ConclusionsThis study provided a workflow for understanding mechanism of CHM for against drug resistance.Electronic supplementary materialThe online version of this article (10.1186/s12906-018-2347-x) contains supplementary material, which is available to authorized users.

Network pharmacology based virtual screening of active constituents of Prunella vulgaris L. and the molecular mechanism against breast cancer

“…GO functional analysis predicted that the key targets of Prunella vulgaris L. are mainly involved (supplementary table 5) in estrogen receptor binding, steroid hormone receptor binding, steroid hormone receptor activity, and so forth. KEGG pathway analysis was used to determine relevant signaling pathways associated with the anti-breast cancer effect of Prunella vulgaris L. The following processes had the highest number of genes: Proteoglycans in cancer (13), Endocrine resistance (11), Human cytomegalovirus infection (11), MicroRNAs in cancer (11), Breast cancer (10), and Kaposi sarcomaassociated herpesvirus infection (10). From the 20 (25), and VEGFA (25) showed a higher degree (Fig.…”

“…Network pharmacology is a new drug discovery approach created by Hopkins in 2007 and integrates systematic medicine with information science 9 . It emphasizes on the concept of “network target, multicomponent therapeutics” 10 , 11 , shifting the paradigm from the concept of one gene, one target, and one disease. Network pharmacology is a powerful method used to study the synergistic actions and underlying mechanisms of traditional medicine 12 – 14 .…”

A bioinformatics investigation into the pharmacological mechanisms of javanica oil emulsion injection in non-small cell lung cancer based on network pharmacology methodologies

“…Target integration of the DEGs discriminated from four datasets (GSE19804, GSE18842, GSE43458, and GSE62113) was performed using Robus-tRankAggreg [38]. Genes with a log2-fold change |log 2 FC| ≥1 and an FDR-adjusted P value < 0.05 were considered DEGs [39]. 4The Cancer Genome Atlas (TCGA) provides over 2.5 petabytes of genomic, epigenomic, transcriptomic, and proteomic data, and our ability to diagnose, treat, and prevent cancer has benefited from these data [40].…”

“…The risk score (RS) was based on the linear combination of the candidate mRNAs for each patient with NSCLC and calculated by multiplying the sum of the mRNA expression values by the single variable Cox regression coefficient [39]. The prediction performance of the model was measured based on the area under the curve (AUC) obtained from the time-dependent receiver operating characteristic (ROC) analysis, and the accuracy of the RS to predict OS at 1 to 5 years was assessed.…”

Top-100 Most Cited Publications Concerning Network Pharmacology: A Bibliometric Analysis

“…NP can help to understand the polypharmacology of a drug and its effect on biological networks to improve drug efficacy [4]. In traditional Chinese medicine (TCM), herbs used for disease treatment are also considered to have multiple targets and pathways [5, 6]. The advent of NP has afforded new methods to identify particular targets of the active complex ingredients of herbs used in TCM and their interactions in the context of molecular networks [1, 3, 5].…”