Abstract
Background
Chinese 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.
Methods
A 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.
Results
The 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.
Conclusions
This study provided a workflow for understanding mechanism of CHM for against drug resistance.
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Background
Lung cancer is a leading cause of cancer mortality worldwide, more than 85% of which is non-small cell lung cancer (NSCLC). Lung adenocarcinoma is the major form of NSCLC, which represents about 50% of lung cancer [1]. Epidermal growth factor receptor (EGFR) mutation is a main contributing factor of lung adenocarcinoma (LUAD) in east Asian countries (about 60% of lung adenocarcinoma) [2]. In China, according to cancer statistics for 2015, lung cancer shows the highest morbidity and mortality [3].
The epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) such as gefitinib, erlotinib and afatinib, which targeted the EGFR pathway, showed potential in the treatment of patients with EGFR mutated NSCLC [4]. And the drugs have effects on LUAD patients with EGFR mutations including the deletion of exon 19 and L858R missense mutation of exon 21 [5]. Although they are effective for early treatment of LUAD, patients will soon have drug resistance in 4 to 12 months during therapy process [2]. Researchers have made great efforts to explore resistance mechanisms and they have discovered many mechanisms of EGFR-TKI resistance. The most frequently studied mechanism of acquired resistance is the T790 M point mutation in exon 2 of EGFR [6, 7]. Secondly, in histologic transformation, the small cell of LUAD histologic transformation and epithelial-mesenchymal transition (MET) activation were closely associated with the acquired EGFR-TKI resistance in patients with never smoked [8,9,1 and 2). The expression of core genes and subnetwork of compound-target network were showed in Fig. 4.
The results showed that 35 compounds acted on 20 pathways, which showed average degree of 6.32. In the compound-pathway network, the compounds mainly involved in pathways such as Salivary secretion (degree = 33), Renin secretion (degree = 32) and Calcium signaling pathway (degree = 31) (Additional file 3).
As shown in Fig. 4d, we clustered the pathways into four modules which were secretion pathways, drug resistance pathways, cancer pathways and signaling pathways. The compounds of FZKA have multiple target effects and involved in multiple pathways that may be related to drug resistance. Of these compounds, MOL000497 (licochalcone a), MOL002773 (beta-carotene), MOL000358 (beta-sitosterol) and MOL000546 (diosgenin) directly involved in drug resistance pathways (EGFR-TKI resistance and platinum drug resistance).
Core genes validation in independent dataset
The core genes were searched from EGFR-mutation LUAD cohort in TCGA. The clinical factors and baseline information of these patients were listed in Table 3. And RS of overall survival is calculated from linear combination of gene expression and coefficient. The median value of RS is considered as threshold to classify patients into two groups and RS was significantly associated with LUAD patient survival (HR = 6.604, 95% CI: 2.314–18.850). Generally, sensitive group would have longer survival time than resistance. The median value of RS showed that survival of sensitive group was significantly better than resistance group (p = 0.0012) (Fig. 5a). The RS distribution in patients was showed in Fig. 5b. And AUC of RS showed that the core genes model performed (AUC = 0.853) well prediction capacity (Fig. 5c). Additionally, RS of RFS was also validated in TCGA database (Fig. 5d). The result of RS in RFS was similar to overall survival (HR = 5.132, 95% CI: 1.531–17.220). And the log-rank test showed that these genes could significantly classify patients into two groups (p = 0.0036). AUC of RS of RFS also showed well prediction capacity in 3 years (AUC = 0.746).
KEGG pathway enrichment
The results of pathway enrichment will be able to show how these drugs act on the pathway, thereby alleviating cell resistance to drugs (Fig. 6). Through the result of KEGG pathway enrichment showed that two pathways (hsa05223: Non-small cell lung cancer and hsa01521: EGFR tyrosine kinase inhibitor resistance) were directly associated with EGFR-TKI resistance.
The EGFR tyrosine kinase inhibitor resistance pathway showed that there were many pathways can lead to drug resistance. In this study, the pathway enrichment indicated that FZKA acted on PRKCA and BCL2 pathway to affect drug resistance. In addition, in Non-small cell lung cancer, FZKA also act on PRKCA and CDKN1A (p21).
Molecular docking assay
The mechanism of FZKA was reflected by interaction of compound and target. Thus, molecular docking simulation is used to analyze interaction between them. Three targets and four compounds which involve in the EGFR-TKI resistance were listed in Table 4.
The versatile functions of CDKN1A (p21) are not fully understood and the associated pathways and mechanism need to be further elucidated (a. Less understood issues: p21Cip1 in mitosis and its therapeutic potential; b. Ironing out the role of the cyclin-dependent kinase inhibitor, p21 in cancer: Novel iron chelating agents to target p21 expression and activity). The structure and the interacting information of active pocket of CDKN1A (p21) are still lacking. Thus, we mainly concentrated our docking analysis on PRKCA and BCL2. The 3D structure of PRKCA and BCL2 are derived from the PDB database and used for docking analysis (Fig. 7).
In simulation processing, CDKN1A (p21) does not have full-length crystal structure and active pocket information. Thus, we searched 3D structure of PRKCA and BCL2 for docking analysis.
PRKCA protein has a common characteristic of kinases and a small-molecule ligand is bound to ATP’s competitive pockets [37]. During the molecular docking simulation, we docked beta-sitosterol into the binding pocket of PRKCA protein, however, it returned no binding poses of beta-sitosterol, which indicates that beta-sitosterol doesn’t have the ability to bind to the active site of PRKCA protein. The binding pocket of BCL2 shows that it has the characteristics of protein-protein interaction [38]. The results of docking simulation for BCL2 suggested that all the three small-molecule ligands could be docked to the binding site of BCL2 as shown in Fig. 8a, b and c. LCA tends to have the best affinity among three compounds according to the docking scores (Table 5) and the detailed binding mode of LCA was analyzed. As shown in Fig. 8d, e, LCA was buried in a hydrophobic pocket formed by Phe101, Asp108, Phe109, Met112, Glu133, Leu134, Asn140, Arg143, Ala146, Phe150, Val153. Among these residues, the hydroxy of LCA had a hydrogen bonding with Arg143, while the benzene rings of LCA formed π-π stacking and π-cation interaction with Phe101 and Arg143. The results of the molecular docking simulation above showed that BCL2 tended to be the potential target involved in EGFR-TKI resistance and non-small cell lung cancer pathways and LCA could be an active compound to decrease the EGFR-TKI resistance.
Discussion
Recently, with the growing research on CHM by network, a new “multi-target, multi-drug” model was considered as more effective strategy for understanding drug action and treatment complex disease [39]. Although a CHM formulation of FZKA was reported for treatment NSCLC patients with EGFR-TKI resistance, the mechanism o formulations have not been illustrated. In this work, we employed complex network analysis, bioinformatics and computer simulation methods for investigating the mechanism of drug action. The results suggested that one of the main mechanisms may be by inhibiting BCL2 and PRKCA pathway which were EGFR-TKI resistance pathways for overcoming EGFR-TKI resistance.
Generally, many studies considered that hub targets or hub pathways interacted with compounds in the network as an important point in drug action. In this study, ADRB2 is a hub node in the subnetwork. The subnetwork showed that there were many molecules interacting with ADRB2 (Fig. 4c). Beta-2 adrenergic receptor (ADRB2), coded by an intronless gene on chromosome 5q31–32, mediate the catecholamine-induced activation of adenylate cyclase through the action of G proteins [40]. Generally, ADRB2 was reported that it significantly associated development of cancer and it is considered that sympathetic neurotransmitters can act as ligands and activate ADRB2 expressed on the surface of tumor cells to promote tumor growth [41]. In addition, ADRB2 was also found that it associated with risk of asthma and LUAD [42,43,44]. At present, the relationship between ADRB2 and lung cancer is mainly related to the activation of mitotic pathways [44]. And some studies figure out activity ADRB2 can active EGFR signaling pathway for tumor growth [45, 46]. Although ADRB2 was not directly involved in EGFR-TKI resistance pathway in this study, the role of ADRB2 was very important in EGFR-TKI resistance due to involve in cell proliferation and EGFR signaling pathway.
The proteins involved in important biological pathways was considered as core proteins. And the compounds interacted with core proteins may be key component in herbs. There eight genes were searched from overlap of targeted proteins and DEG from sensitive and resistance PC9 cell lines. And eight genes can predict prognosis of LUAD patients with EGFR mutation. And RS of these genes can significantly classify the patients into sensitive and resistance groups (Fig. 6).
Of these genes, BCL2, PRKCA (PKC) and CDKN1A (p21) were directly associated with EGFR-TKI resistance pathway (hsa01521) and NSCLC pathway (hsa05223). And five compounds act on these proteins (Table 5). BCL2 is a noted protein in regulation apoptosis of cancer. Previous study reported that overexpression BCL2 can inhibit apoptosis in cancer cells [47]. And BCL2 was also reported that it involved in the mediation of chemotherapy resistance in NSCLC [48]. In this study, EGFR-TKI resistance cell lines have high expression of BCL2 (Fig. 4b). In addition, the results showed that three compounds acted with BCL2. And licochalcone a (LCA) showed the best score in three compounds. According to previous study, LCA can inhibit BCL2 for inducing autophagy and promoting apoptosis in cancer cells [49]. Another study reported that LCA induced autophagy effect in NSCLC cells [50]. Although these two studies reported that LCA can induce apoptosis and autophagy by experiment, the mechanism of molecular level has not been revealed. In our study, the simulation results showed that LCA act on active package of BCL2 protein. The binding of small molecules to BCL2 can influence the binding of BCL to downstream ligands. Therefore, the binding of three small molecules and BCL2 may play vital role in regulation apoptosis of LUAD with EGFR-TKI resistance.
Other two proteins (PRKCA and CDKN1A) were also analyzed in study. But 3D structure of CDKN1A(p21) has not been resolved yet. So, the mechanism of CDKN1A fail to analyze. Additionally, in analysis of PRKCA, the binding of beta-sitosterol and PRKCA was very different from common PRKCA inhibitor. So, the molecular simulation software doesn’t get the result from PRKCA. Although PRKCA and CDKN1A have not been validation by molecular simulation, the results also indicated that FZKA could overcome EGFR-TKI resistance through affecting eight core targets. Of these targets, ADRB2, BCL2, PRKCA and CDKN1A were reported by previous publications. Other genes have not been reported to associated with EGFR-TKI in LUAD.
Above all, from our analysis, the compounds from Hedyotis Diffusae Herba, licorice, Hedysarum multijugum Maxim, Solanum nigrum Linn, Curcumae Rhizoma and Atractylodes Macrocephala Koidz play major role in overcoming EGFR-TKI resistance in LUAD. And BCL2 and PKC pathways may be main targets of FZKA. And these two targets as drug targets for overcoming EGFR-TKI resistance were also reported by previous publication. Other targets such as LYZ, HTR2C, KCNMA1 and PLA2G4A were not enriched in pathway that related with EGFR-TKI resistance. However, these targets may be potential targets of drug resistance. Further experiments are still needed confirm this conclusion.
Conclusion
In clinical practice, it has been found that FZKA has the effect of overcoming the drug resistance of EGFR mutations positive, but the molecular mechanism is unclear. This study revealed that compounds from FZKA directly acted on targets which involved in EGFR-TKI resistance. That interaction indicated that FZKA can overcome drug resistance through inhibiting BLC2 and PRKCA pathways.
Abbreviations
- ADME:
-
Absorption, distribution, metabolism and excretion
- AUC:
-
Area Under roc Curve
- CHM:
-
Chinese herbs medicine
- DEGs:
-
Differential gene expressions
- DL:
-
Drug-likeness
- EGFR:
-
Epidermal growth factor receptor
- EGFR-TKI:
-
Epidermal growth factor receptor tyrosine kinase inhibitors
- FZKA:
-
Fuzhengkangai
- KEGG:
-
Kyoto Encyclopedia of Genes and Genomes
- LCA:
-
Licochalcone a
- LUAD:
-
Lung adenocarcinoma
- NSCLC:
-
Non-small cell lung cancer
- OB:
-
Oral bioavailability
- ROC:
-
Receiver Operating Characteristic
- RS:
-
Risk score
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Acknowledgements
We gratefully acknowledge the support of Institute of modern physics, Chinese Academic of science, with the donation of the P100 GPU (NVIDIA Corporation) used for our simulations.
Funding
This work was supported by Gansu province funding (No. 1606RJZA016). This funding has supported the data analysis and design of the study in this work.
Availability of data and materials
We have presented all our main data in the form of tables in additional file. The datasets supporting the conclusions of this article are available in public database from TCMSP, PDB database, TCGA and GEO (GSE34228) dataset.
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KY and YW formulated the idea of the paper and supervised the research. ZB and ZC performed the research, analyzed the data and wrote the manuscript. DS and XL designed molecular docking by computer. JX and JZ computed the clinical variable in Table 3 and analyzed the prognostic core genes in EGFR mutation LUAD cohort. XY participated in revising the data and improving manuscript writing. All authors reviewed the manuscript. And all authors read and approved the final version of the manuscript.
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Additional files
Additional file 1:
The details information of 76 compounds that were filtered by ADME from the eleven herbs of FZKA. (XLSX 16 kb)
Additional file 2:
The compound-target network was consisted by 76 compounds and 130 targets. (XLSX 14 kb)
Additional file 3:
The information of compound-pathway network obtained with network analysis. (XLSX 16 kb)
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Bing, Z., Cheng, Z., Shi, D. et al. Investigate the mechanisms of Chinese medicine Fuzhengkangai towards EGFR mutation-positive lung adenocarcinomas by network pharmacology. BMC Complement Altern Med 18, 293 (2018). https://doi.org/10.1186/s12906-018-2347-x
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DOI: https://doi.org/10.1186/s12906-018-2347-x