Next Article in Journal
Is There an Effect of Methyl Donor Nutrient Supplementation on Metabolic Syndrome in Humans?
Previous Article in Journal
Bayesian Space–Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010–2013
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Medical Sciences
Volume 8
Issue 1
10.3390/medsci8010001
medsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Network Pharmacology Approach Reveals the Potential Immune Function Activation and Tumor Cell Apoptosis Promotion of Xia Qi Decoction in Lung Cancer

by
Song Zhang
and
Yun Wang
*
School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 10029, China
*
Author to whom correspondence should be addressed.
Med. Sci. 2020, 8(1), 1; https://doi.org/10.3390/medsci8010001
Submission received: 25 November 2019 / Revised: 19 December 2019 / Accepted: 24 December 2019 / Published: 29 December 2019
Browse Figures
Review Reports Versions Notes

Abstract

:
As the leading cause of cancer death worldwide, lung cancer (LC) has seriously affected human health and longevity. Chinese medicine is a complex system guided by traditional Chinese medicine theories (TCM). Nowadays, the clinical application of TCM for LC patients has become the focus for its effectiveness and security. In this paper, we will analyze and study the mechanism of Xia Qi Decoction (XQD) in the treatment of LC. The results collectively show that XQD could act on 41 therapeutic targets of LC. At the same time, 8 of 41 targets were significantly expressed in immune tissues and cells by activating CD8+T cells to promote apoptosis of cancer cells. It reveals the molecular mechanism of XQD in the treatment of LC from the perspective of network pharmacology. In addition, in the treatment of LC, XQD can activate (up-regulate) the function of immune cells, promote the apoptosis of tumor cells, and have an active anti-tumor immune effect. In conclusion, this study reveals the unique advantages of traditional Chinese medicine in the treatment of cancer, in reinforcing the healthy qi and eliminating the pathogenic factors. More research, however, is needed to verify the potential mechanisms.

1. Introduction

Lung cancer (LC), as the second most common cancer in the world, has the highest mortality rate. Specific biomarkers for its diagnostics, treatment, and prognosis are still under further investigation [1], and the incidence of LC continues to increase in China [2,3]. Concurrently, LC is the leading cause of cancer death in China [4]. Although much research effort has been made in improving treatment for LC in recent decades, a survival rate of five years is still less than 20% [5]. There are two main pathological types of LC: small cell lung cancer (SCLC) accounting for about 85% and non-small cell lung cancer (NSCLC) accounting for about 13%–15% [6]. The main symptoms of LC are coughing, hemoptysis, dyspnea, and fatigue. Traditional Chinese medicine (TCM), as an important part of the healthcare system in China, has gradually gained popularity both at home and abroad [7]. There is a general consensus that TCM produces therapeutic effects in a holistic manner [8]; besides, multi-target therapy is more effective than single therapy in combating polygenic diseases [9].
With the development of systems biology, network biology, and pharmacology, Andrew L. Hopkins proposed the concept of network pharmacology [10]. Network pharmacology studies the treatment or influence of drugs on diseases from the network level, and reveals the synergism law of multi-component drugs, so as to discover the high efficiency and low toxicity of multi-target drugs. Therefore, from the molecular level, the idea of TCM is consistent with those of network pharmacology [11].
In recent years, TCMs have been widely applied in the treatment of cancers in China and beyond. XQD, as a classic herbal formula, was created by Huang Yuanyu in the Qing Dynasty from “Si Sheng Xin Yuan”. XQD is mainly used to treat diseases of lung and stomach system. XQD is composed of eight medicinal herbs: Licorice (LO), Schisandra chinensis (SC), Poria cocos (PC), Pinellia ternata (Thunb) Breit (AT), Citrus reticulata (CR), Fritillaria cirrhosa D. Don (FR),Paeoniae radix alba (PR), and Amygdalus communis vas (AC). XQD is commonly used to treat lung diseases such as cough, asthma, and lung cancer [12,13]. Clinical experiments found that Xiaqi Decoction can also treat the acute exacerbation of chronic bronchitis [14]. Furthermore, it was found that the total effective rate of the experimental group was 86.7%, which was significantly higher than that of the control group (63.3%), after clinical comparison experiments [15]. However, the effective compounds, targets and pharmacological mechanisms of XQD in the treatment of lung cancer remained unclear. Therefore, in this paper, a network pharmacology approach, functional gene pathway analysis, network analysis, and other comprehensive methods were used to reveal XQD-related active compounds, key therapeutic targets, and the molecular mechanism of action for LC.

2. Materials and Methods

2.1. Collection of XQD Chemical Ingredients

TCMSP database (http://sm.nwsuaf.edu.cn/lsp/tcmsp.php.) is a traditional Chinese medicine systems pharmacology database and analysis platform [16]. To determine the chemical ingredients of the eight herbs contained in XQD, we performed a search by TCMSP. In order to maximize the chances of finding the fully active compounds, we set two conditions as the criteria for screening these active compounds: oral bioavailability (OB) and drug-likeness (DL), which are the two most important indicators of evaluating ADME characteristics via bioinformatics.

2.2. Target Selection and Herb-Ingredient-Target Network Construction of XQD

After discovering 112 active ingredients of eight traditional Chinese medicines of XQD, the next important step was to identify their molecular targets and trigger a series of biological effects. The potential targets of XQD were also identified from DrugBank database [17] and the database of our research group [18]. Herb-ingredient-target network with Cytoscape3.7 software was constructed. Nodes in the network were herbs, ingredients and targets. Edges were used to connect herbs with ingredients, active ingredients, and act targets to show the relationship between herbs, active ingredients, and targets in order to explore the multiple pharmacological mechanisms of XQD based on the constructed network.

2.3. Collection of Therapeutic Targets for LC

Therapeutic targets for LC were collected from the following four databases: NCBI database [19], TTD database [20], OMIM database [21], and DrugBank database [17]. Using “Lung cancer” to search in the four databases mentioned above, the duplicate targets were eliminated, and 228 targets related to human LC were finally obtained.

2.4. Protein-Protein Interaction Data

Protein–protein interaction (PPI) data were from STRING database [22] and an auxiliary elucidation system for the TCM mechanism of our team laboratory, which realized the automatic establishment of biological network model [18]. Entity grammar systems is a formal grammar system [23], which is extended to Chomsky generative grammar system and has been used for the analysis of multiple mechanisms of action of traditional Chinese medicine. It has the characteristics of high efficiency and flexibility, and is suitable for the study of complex biological systems. The ID type of protein is converted to UniProt ID [24].

2.5. Herb-Ingredient-LC Therapeutic Target Network Analysis

The ingredients-target network was mapped to LC-related gene network. Furthermore the interaction network between the target of XQD and LC-related genes was established. If the chemical components of XQD overlap with the therapeutic targets of LC, then these targets are the direct targets of XQD for LC treatment. If the chemical targets of XQD act on the therapeutic targets of LC through one or two protein interactions, then these targets are indirect targets of XQD for LC. The direct and indirect targets of XQD on LC were found, and the mechanism was determined. In this network, the nodes represent Chinese medicine, ingredients, targets or genes and LC, and the links between these nodes. “Degree value” is the number of edges associated with it. Targets with connectivity greater than twice the average number of node degrees are selected as key nodes in the network [25].

2.6. Pathway Enrichment Analysis

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a database of genome decipherment. The KEGG pathway was used to analyze the main pathways of XQD acting on 41 targets of LC. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks [26], which was used to integrate the KEGG pathways [27].

2.7. Relationship Analysis Between LC Targets and Immunological Targets Affected by XQD

41 genes of LC affected by XQD were searched in GeneCards database [28] to find the expression of genes (targets) in human immune tissues and cells. Chi-square test was used to screen the genes significantly expressed in immune tissues and cells. We constructed a network between the selected genes and immune tissues and cells, to explore the mechanism of the key genes regulating LC and immunity.

3. Results

3.1. OB Prediction and DL Calculation

According to TCMSP database of TCM systematic pharmacology and analysis platform, the chemical ingredients of the eight traditional Chinese medicines of XQD were identified. In order to obtain the potential active ingredients of XQD, the chemical were evaluated and screened using OB ≥ 30% [29], DL ≥ 0.18 [30], respectively. The chemical ingredients of eight traditional Chinese medicines in XQD were as follows: 92 in LO,13 in AT,15 in PC,19 in AC,13 in FR,8 in SC,13 in PR,5 in CR. In order to further screen the active ingredients of LO, the OB of which was increased to more than 50%. Eventually, 112 active ingredients were screened out (Table 1).

3.2. Target Selection and Herb-Ingredient-Target Network Construction of XQD

Ninety-six potential targets were obtained from TCMSP and our laboratory database. Cytoscape software was used to construct a network of herb-ingredient-target. The nodes in the network were Chinese herbs, components, and targets; in addition, the edge of the network represented the relationship between herb–component and component–target. By introducing 8 herbs, 112 ingredients, and 96 targets into Cytoscape, three kinds of nodes were connected to construct a network of traditional Chinese medicines, active ingredients, and targets, as shown in Figure 1.
The network consists of 8 herbs, 112 chemical ingredients, 96 targets, 234 nodes, and 1498 edges. The Centiscape2.2 plugin is used to screen the key nodes in the network. Centrality is used to screen the targets. Using Centiscape2.2, we can calculate Degree Centrality (DC), Closeness Centrality (CC), and Betweenness Centrality (BC). Degree value denotes the number of routes connected to the node in the network. In this network, Degree = 13.833, Betweenness Centrality = 0.008, Closeness Centrality = 0.365. Among 50 of 112 active ingredients are active ingredients with degree greater than 14 and degree of 9 active ingredients is greater than twice the average degree in Table 2. Among the 96 targets, degree of 29 targets is more than 14 and degree of 19 targets is more than twice the average in Table 3. Therefore, these 19 targets are very likely to be the key targets for XQD to play a therapeutic role.
In the herb-ingredient-target network, 45.5% of the chemical ingredients had more than 14 targets, and 12 of them had more than 28 targets in Table 3. This indicated that most of the chemical ingredients in XQD could simultaneously act on multiple targets to play a combined therapeutic role. For example, Beta-sitosterol, a chemical ingredients contained in Fritillaria chuanxiong, AT, and PR, has the most targets and can interact with 49 targets. Secondly, the number of targets of Kaempferol in PR and L-SPD in AC was 44. Beta-sitosterol is a plant derived nutrient with anticancer properties against LC, stomach cancer, ovarian cancer, and leukemia. Studies have shown that BS interfere with multiple cell signaling pathways, including cell cycle, apoptosis, survival, invasion, angiogenesis, and metastasis [31]. Kaempferol is the most common flavonoid compound, which has inhibitory effects on LC [32], ovarian cancer [33], breast cancer [34], and many other tumors.
In the network, the highest degree target was AR, corresponding to 87 chemical compounds. Positive expression of AR might be correlated with the progression and the lymph node metastasis of lung cancer [35]. AR could inhibit the proliferation and survival of cancer cells by up-regulating PTEN directly [36]. Secondly, ESR1 had 74 chemical ingredients that can act on this target. In non-small cell lung cancer, ESR1 can be combined with EGFR, showing enhanced antiproliferation effects [37]. Therefore, it was speculated that these key components and targets were closely related to the mechanism of XQD in the treatment of LC.

3.3. Construction of PPI Network in XQD

96 targets of XQD were imported into STRIING database, and the species were limited to human beings. Some proteins did not interact with each other, which were not reflected in the interaction network. The highest confidence level with score greater than 0.9 was selected to obtain the protein network. Average degree value = 5.15, and 13 non-interactive targets were excluded. In PPI networks with 82 targets, the size and color of the target were set to reflect the Degree size, the minimum value of Degree = 1, and the maximum value of Degree = 14. The thickness of the table was set to reflect the combine size in Figure 2.
The PPI network (Figure 2) of 82 targets included 82 nodes and 207 edges. Eight targets with a degree greater than twice the median were MAPK3 (Degree = 14), JUN (Degree = 12), TP53 (Degree = 12), HSP90AA1 (Degree = 11), TNF (Degree = 11), ESR1 (Degree = 11), F2 (Degree = 11), CHRM2 (Degree = 11). A total of 8 central targets were obtained. These eight targets play a key role in the protein network and become the hub connecting other nodes in the network. Among them, the degree of MAPK3 (mitogen-activated protein kinase 3) was the highest (Degree = 14). This indicates that these targets play a key role in the network and become the hub connecting other targets in the network.

3.4. Constructing the Network of Herb-Ingredient-Target-LC Therapeutic Target

To construct the network of XQD and lung cancer treatment, 91 of the 112 chemical ingredients of XQD act on 41 targets of LC through direct or indirect protein interaction. Degree = 8.09, Betweenness centrality = 0.012, Closeness centrality = 0.356. Ultimately, as shown in Figure 3, the degree value of 25 components was more than 8. There are 25 key components whose degree value is greater than the average degree value (Table 4). These components could be considered as the key compounds of XQD in the treatment of LC. In the network (Figure 3), we screened 14 direct targets (Table 5) and 27 indirect targets (Table 6) for LC.
They were mainly: Beta-sitosterol (degree = 17) in PR, AT and FR can act on 14 targets of LC. Kaempferol in PR (degree = 14) acts on 13 targets of LC. Beta-sitosterol is present in PR, having anti-tumor, anti-microbial, and immunomodulatory activities [38]. In vitro studies showed that Beta-sitosterol increased the number of viable peripheral blood mononuclear cell (PBMC) and activated swine dendritic cells (DCs) in culture [39]. Beta-sitosterol (β-sitosterol) induced G0/G1 cell cycle arrest and inhibited cell proliferation in A549 cells. These results indicate that beta-sitosterol may serve as novel targets for the treatment of NSCLC [40]. Bio-assay guided fractionation showed the presence of phytosteols (β-sitosterol) which significantly inhibited the growth of A549 cells and promoted apoptosis alone or in combination. This study ensures that these phytosterols, alone or in combination, can be considered as safe and potential drug candidates for LC treatment [41]. Kaempferol in PR can act on 13 targets of LC. MEK-MAPK is a requirement for kaempferol-induced cell death machinery in A549 cells [32]. Cavidine (degree = 13) in AT acts on 12 targets of LC. Cavidine exists in AT and has significant anti-inflammatory effect, which inhibits the production of proinflammatory cytokines TNF-alpha and IL-6 [42]. At the same time, Cavidine has anti-inflammatory activity to prevent inflammatory injury induced by lipopolysaccharide (LPS) [42,43].
Naringenin (degree = 12) in LO and formononetin (degree = 11) in AC can act on 10 targets of LC, respectively. The cumulative effect of these four ingredients on the number of therapeutic targets for LC is 22. Studies have shown that Naringenin, a natural product that is mainly present in LO, may contribute to cancer prevention. There are many advantages compared to traditional chemotherapeutic drugs, such as low toxicity, which can also inhibit the number of lung cancer cells metastasis by regulating immunity [44]. Thus, it has a potential to inhibit lung cancer [44,45]. Naringenin up-regulates the expression of death receptor 5 and enhances TRAIL-induced apoptosis in human lung cancer A549 cells, with no detectable inhibitory effects on cell proliferation of normal lung fibroblast cells [46]. Formononetin in AC was investigated the anti-proliferative effects on human non-small cell lung cancer (NSCLC). It inhibits proliferation of two NSCLC cell lines (A549 and nci-h23), induces G1 phase cell cycle arrest, and promotes NSCLC cell apoptosis. The results demonstrated that formononetin might be a potential chemopreventive drug for lung cancer therapy through induction of cell cycle arrest and apoptosis in NSCLC cells [47]. Baicalein (degree = 10) in AT is a widely used Chinese herbal medicine, traditionally used as anti-inflammatory and anti-cancer therapy. Baicalein significantly decreased lung cancer proliferation in H-460 cells in a dose-dependent induction in apoptosis. This was the first time that baicalin had been effective in vitro and in vivo in NSCLC [48]. Experimental studies had shown that Baicalein induced cell cycle arrest and apoptosis in human lung squamous carcinoma CH27 cells [49].

3.5. Pathway Analysis of XQD Acting on LC Therapeutic Targets

In order to investigate the biological effects of 41 targets of XQD for LC, 41 genes were inserted by ClueGO plug in Cytoscape. The target-pathway network graph (Figure 4) was obtained by setting a path that only showed p value ≤ 0.05 and in which the number of genes in the pathway was more than three. We analyzed the data and relevant biological processes for LC, choosing top ten remarkable significant pathways (Figure 5) according to the p value for further study. The first four pathways with the largest number of gene enrichment were cell senescence [50,51], FoxO signaling pathway [52,53], HIF-1 (hypoxia inducible factor) signaling pathway [54], and estrogen signaling pathway [55,56]. Thus pro-senescence therapies may represent a new treatment for lung cancer [57]. FoxO play a vital role in cell fate determination, and the subfamily is also considered to play a key role in cancer as a cancer inhibitor. In the process of apoptosis, FoxO participates in mitochondrial dependent and independent processes, triggering the expression of death receptor ligands such as Fas ligand, TNF apoptotic ligand and Bcl-XL [58]. HIF-1 signaling pathway inhibits cell viability and induces cell apoptosis [55]. The results indicate that HIF-1α signaling pathway plays an important role in the regulation of TNF-α-induced proliferation and metastasis of A549 cells in NSCLC [59]. In addition, there are five other pathways: p53 signaling pathway [60,61], cellcycle, ErbB signaling pathway [62], IL-17 signaling pathway [51,52,63,64], small cell lung cancer, vascular endothelial growth factor signaling pathway [65,66]. All of them are closely related to the occurrence of lung cancer, apoptosis of tumor cells, and immune function. Moreover, we drew a histogram of the 10 pathways screened, showing the number of genes enriched in each pathway.

3.6. Constructing Target-Immune Tissues and Cell Network

The 41 LC genes affected by XQD were placed in the GeneCards database to search for the expression of targets (genes) in human tissues and cells. With the chi-square test, finally eight therapeutic targets for LC are significantly expressed in immune tissues and cells. The data of eight LC treatment targets and 14 immune tissues and cells were introduced into Cytoscape 3.7.1 to construct a network between target-immune (Figure 5) for further analysis. The size and color of the target were set to reflect the Degree size, and the thickness of the table was set to reflect the combine size. In this network, HLA-B, CASP8, and MAPK14 three targets of XQD, had the most obvious relationship with immune tissues and cells. From Figure 6 and Table 7, it can be seen that the expression level of HLA-B in 14 immune tissues and cells is significantly higher than that of the other seven targets. The expression of CASP8 in NK cells, CD8+T cells and CD4+T cells is significantly higher than that in the other 11 tissues and cells. MAPK14 also is more significantly expressed in 14 kinds of immune tissues and cells. Therefore, we concluded that the three targets of HLA-B, CASP8, and MAPK14 are the immunological targets of XQD for the treatment of LC.

4. Discussion

HLA-B is an important target of XQD in the treatment of LC and also an immunological target. HLA-B is one of the MHC I. MHC is the major histocompatibility complex of human, namely human Leukocyte Antigen HLA, which participates in antigen presentation, specifically recognizes TCR, and plays a key role in the activation of T cells. HLA-B belongs to HLA-I molecule, which is widely distributed on the surface of all nucleated cells and closely related to human immunity [67]. While complexing with antigenic peptides, HLA-B molecules initiate CD8+T cell responses via interaction with the T cell receptor (TCR) and coreceptor CD8 [68]. Similarly, the expression of HLA-B in cancer cells was helpful in activating the activation and proliferation of CD8+T cells [69].
CASP8 is a member of the caspase-cysteine protease family and plays an important role in the development of cancer [70]. It has been reported that 79% of NSCLC cell lines lack Caspase-8, about 35% of SCLC and 18% of bronchogenic carcinoma have promoter methylation of CASP8 [71]. Caspase-8 can activate Caspase downstream of almost all apoptotic cascades and induce apoptosis [72,73], as a caspase-dependent apoptotic pathway promoter, which has been extensively studied under the trigger of death receptor of TNF-R1 [74]. CASP8 has immuno-regulatory functions [75], which regulates T cell activation and proliferation [76], positive regulation of macrophage differentiation [77], and activation of natural killer cells [78].
MAPK14 activation could be a common response of most cancer cells. The family of p38 has 4 different isoforms (MAPK11/p38β, MAPK12/p38γ, MAPK13/p38δ, and MAPK14/p38α) [79], among which MAPK14 is the most abundant and widely expressed [80]. MAPK14 is an important apoptotic inducer, TNF-α, TGF-β and oxidative stress activate MAPK14 signaling pathway to induce apoptosis [81,82,83], and exert its anti-tumor effect [84]. Reactivation of the p38α MAPK pathway might be a useful therapy for LC [85].
Studies have shown that Fas-mediated increase in the activity of P38MAPK requires the participation of Caspase family members. P38MAPK is the downstream target of Caspase, thus causing target cell apoptosis [86]. Here are the mechanisms of P38MAPK in promoting apoptosis: 1) participating in Fas/FasL-mediated apoptosis [87]; 2) upregulating the expression of TNF-alpha; TNF can induce apoptosis by activating MAPKK upstream of p38MAPK, and ultimately activating p38MAPK [88]. Malignant proliferation of cells or the production of cancer cells are due to the dysfunction of cell proliferation and cell death regulation. Fas/FasL is the most important signal pathway involved in cell apoptosis [89], which is closely related to anti-cancer therapy [90].
XQD involves the biological process of tumor cell apoptosis induced by immune regulation (Figure 7). In stimulating the process of tumor cell apoptosis, XQD plays a role in HLA-B on dendritic cells (MHC I) of mature DC and CD8+T cells at the same time, to activate CD8+T cells in the immune response, to identify tumor antigen and kill tumor cells. After activation, CTL expresses FasL and TNF-α, binds to the Fas and TNFR1 on the surface of tumor cells, and generates FADD, which will conduct the apoptosis signal into the cell and perform CASP8 proteolytic activation, thus initiating the apoptosis process of tumor cells. The apoptotic signal was transmitted to MAPK14 to induce accelerated apoptosis.
As demonstrated in this study, XQD can play a vital role in the treatment of LC by enhancing immunity and mediating apoptosis of cancer cells. One of the characteristics of TCM is to seek the root of the disease. It advocates the idea of strengthening the body and eliminating pathogens in the treatment of diseases. In the mechanism of XQD in the treatment of LC, it can not only activate the function of immune cells and improve the immunity of patients, but also promote the apoptosis of cancer cells. From the molecular mechanism, the advantages of XQD in the treatment of LC are that it reinforces the healthy qi and eliminates the pathogenic factors. In the study of XQD, the practical application of network analysis method was described, and the results show that this method was an effective strategy for the modern research of TCM. The data of herbal ingredients involved in each formula cover a wide range and involve many targets, which in turn provides more directions for the research on the molecular mechanism of the therapeutic effect of TCM formula. Different TCM formulations have different components and targets. The network pharmacology method can better explain the different molecular mechanisms of different formulas for the treatment of various diseases. Although a lot of network pharmacology research has been done in the field of TCM, it has not shown the characteristic advantages of TCM therapy—Fu Zheng Fu Ben and strengthening the immunity. At present, research on LC rarely involve the effects of drugs on immune tissues and cells. This study aims to make up for this deficiency. Using a similar research analysis method, more cancers and tumors can be studied.

5. Conclusions

The study reveals the molecular mechanism of XQD in the treatment of LC from the perspective of network pharmacology. The aim of this study was to analyze the molecular mechanism of the effective components and targets in TCM prescriptions acting on lung cancer and immunity, and to understand the synergistic mechanism and characteristics of TCM from a more comprehensive perspective. The results show that this method can better explain the unique advantages of XQD in the treatment of LC.
While treating LC, XQD can activate (up-regulate) the function of immune cells, promote the apoptosis of tumor cells, and has an active anti-tumor immune effect. Therefore, it is necessary to give full play to the characteristics of TCM in the treatment of cancer: reinforce the healthy qi and eliminate the pathogenic factors. It provides a new idea for the current research of LC treatment, with the aim of treating LC while enhancing immunity.

Author Contributions

Methodology, S.Z.; Software, S.Z.; Formal Analysis, S.Z..; Resources, S.Z.; Data Curation, S.Z.; Writing – Original Draft Preparation, S.Z.; Writing – Review & Editing, S.Z. and Y.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research work was funded by the National Natural Science Foundation of the Natural Science Foundation of China (No.81673697 and NO.81973495).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Stumbryte, A.; Gudleviciene, Z.; Kundrotas, G.; Dabkeviciene, D.; Kunickaite, A.; Cicenas, S. Individual and combined effect of TP53; MDM2; MDM4; MTHFR.; CCR5; and CASP8 gene polymorphisms in lung cancer. Oncotarget 2018, 9, 3214–3229. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Parkin, D.M.; Bray, F.; Ferlay, J.; Pisani, P. Global cancer statistics: 2002. CA Cancer J. Clin. 2005, 55, 74–108. [Google Scholar] [CrossRef] [PubMed]
  3. Zhao, P.; Dai, M.; Chen, W.Q.; Li, N. Cancer trends in China. Jpn. J. Clin. Oncol. 2010, 40, 281–285. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Chen, W.Q.; Zheng, R.S.; Baade, P.D.; Zhang, S.W.; Zeng, H.M.; Bray, F.; Jemal, A.; Yu, X.Q.; He, J. Cancer statistics in China: 2015. CA Cancer J. Clin. 2016, 66, 115–132. [Google Scholar] [CrossRef] [Green Version]
  5. Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics: 2015. CA Cancer J. Clin. 2015, 65, 5–29. [Google Scholar] [CrossRef]
  6. De-Freitas, A.C.; Gurgel, A.P.; De-Lima, E.G.; Sao-Marcos, B.D.; Do-Amaral, C.M.M. Human papillomavirus and lung carcinogenesis: An overview. J. Cancer Res. Clin. Oncol. 2016, 142, 2415–2427. [Google Scholar] [CrossRef]
  7. Cheung, F. TCM: Made in China. Nature 2011, 480, S82–S83. [Google Scholar] [CrossRef] [Green Version]
  8. Jiang, W.Y. Therapeutic wisdom in traditional Chinese medicine: A perspective from modern science. Trends Pharmacol. Sci. 2005, 26, 558–563. [Google Scholar] [CrossRef]
  9. Zimmermann, G.R.; Lehar, J.; Keith, C.T. Multi-target therapeutics: When the whole is greater than the sum of the parts. Drug Discov. Today 2007, 12, 34–42. [Google Scholar] [CrossRef]
  10. Navaneethan, S.D.; Aloudat, S.; Singh, S.A. systematic review of patient and health system characteristics associated with late referral in chronic kidney disease. BMC Nephrol. 2008, 9, 3. [Google Scholar] [CrossRef] [Green Version]
  11. Shi, S.H.; Cai, Y.P.; Cai, X.J.; Zheng, X.Y.; Cao, D.S.; Ye, F.Q.; Xiang, Z. A network pharmacology approach to understanding the mechanisms of action of traditional medicine: Bushenhuoxue formula for treatment of chronic kidney disease. PLoS ONE. 2014, 9, e89123. [Google Scholar] [CrossRef] [PubMed]
  12. Lin, X.J.; Zhang, X.; Wang, S.R.; Li, X. Experience of Professor Li Xian in the Application of Xia Qi Decoction. Chin. Med. Mod. Distance Educ. China 2017, 15, 61–62. [Google Scholar]
  13. Du, C.; Lin, J. Xia Qi Decoction clinical application and experience. Guangming J. Chin. Med. 2012, 27, 1262–1263. [Google Scholar]
  14. Lin, Y.; Huang, S.; Huang, Z.X. Clinical observation of xieqi decoction in the treatment of acute exacerbation of chronic bronchitis. Guangming J. Chin. Med. 2019, 34, 2163–2165. [Google Scholar]
  15. Gao, H.L.; Chen, G.F.; Wu, R.T.; Li, S.J. Observation on the effect of XQD plus or minus in the treatment of lung cancer with obstructive inflammation. Da Jia Jian Kang (Acad. Version) 2013, 7, 21–22. [Google Scholar]
  16. Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y.; et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform. 2014, 6, 13. [Google Scholar] [CrossRef] [Green Version]
  17. Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 2017, 46, D1074–D1082. [Google Scholar] [CrossRef]
  18. Zhang, B.X.; Luo, S.J.; Yan, J.; Gu, H.; Luo, J.; Zhang, Y.L.; Tao, O.; Wang, Y. Construction of automatic elucidation platform for mechanism of traditional Chinese medicine. Chin. J. Chin. Mater. Med. 2015, 40, 3697–3702. [Google Scholar]
  19. Barrett, T.; Troup, D.B.; Wilhite, S.E.; Ledoux, P.; Rudnev, D.; Evangelista, C.; Kim, I.F.; Soboleva, A.; Tomashevsky, M.; Edgar, R. NCBI GEO: Mining tens of millions of expression profiles—database and tools update. Nucleic Acids Res. 2007, 35, D760–D765. [Google Scholar] [CrossRef] [Green Version]
  20. Li, Y.H.; Yu, C.Y.; Li, X.X.; Zhang, P.; Tang, J.; Yang, Q.; Fu, T.; Zhang, X.; Cui, X.; Tu, G.; et al. Therapeutic target database update 2018: Enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res. 2017, 46, D1121–D1127. [Google Scholar]
  21. Hamosh, A.; Scott, A.F.; Amberger, J.; Bocchini, C.; Valle, D.; Mckusick, V.A. Online Mendelian inheritance in man (OMIM): A knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005, 33, 514–517. [Google Scholar] [CrossRef] [PubMed]
  22. Hsia, C.W.; Ho, M.Y.; Shui, H.A.; Tsai, C.B.; Tseng, M.J. Analysis of dermal papilla cell interactome using STRING database to profile the ex vivo hair growth inhibition effect of a vinca alkaloid drug, colchicine. Int. J. Mol. Sci. 2015, 16, 3579–3598. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Yan, J.; Wang, Y.; Luo, S.J.; Qiao, Y.J. TCM grammar systems: An approach to aid the interpretation of the molecular interactions in Chinese herbal medicine. J. Ethnopharmacol. 2011, 137, 77–84. [Google Scholar] [CrossRef] [PubMed]
  24. Bateman, A.; Martin, M.J.; Orchard, S.; Magrane, M.; Alpi, E.; Bely, B.; Bingley, M.; Britto, R.; Bursteinas, B.; Busiello, G. UniProt, a worldwide hub of protein knowledge. Nucleic Acids Res. 2009, 47, D506–D515. [Google Scholar]
  25. Yu, G.; Wang, W.; Wang, X.; Xu, M.; Zhang, L.; Ding, L.; Guo, R.; Shi, Y. Network pharmacology-based strategy to investigate pharmacological mechanisms of zuojinwan for treatment of gastritis. BMC Complement. Altern. Med. 2018, 18, 292. [Google Scholar] [CrossRef]
  26. Bindea, G.; Mlecnik, B.; Hackl, H.; Charoentong, P.; Tosolini, M.; Kirilovsky, A. ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 2009, 25, 1091–1093. [Google Scholar] [CrossRef] [Green Version]
  27. Kanehisa, M.; Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef]
  28. Safran, M.; Dalah, I.; Alexander, J.; Rosen, N.; Iny-Stein, T.; Shmoish, M.; Nativ, N.; Bahir, I.; Doniger, T.; Krug, H.; et al. GeneCards Version 3: The human gene integrator. Database 2010, 2010, 1–16. [Google Scholar] [CrossRef]
  29. Xu, X.; Zhang, W.; Huang, C.; Li, Y.; Yu, H.; Wang, Y.; Duan, J.; Ling, Y. A novel chemometric method for the prediction of human oral bioavailability. Int. J. Mol. Sci. 2012, 13, 6964–6982. [Google Scholar] [CrossRef]
  30. Hong, M.; Li, S.; Tan, H.Y.; Cheung, F.; Wang, N.; Huang, J.; Feng, Y. A network-based pharmacology study of the herb-induced liver injury potential of traditional hepatoprotective Chinese herbal medicines. Molecules 2017, 22, 632. [Google Scholar] [CrossRef] [Green Version]
  31. Bin-Sayeed, M.S.; Ameen, S.S. Beta-Sitosterol: A Promising but Orphan Nutraceutical to Fight Against Cancer. Nutr. Cancer 2015, 67, 1214–1220. [Google Scholar] [CrossRef] [PubMed]
  32. Nguyen, T.T.T.; Tran, E.; Ong, C.K.; Lee, S.K.; Do, P.T.; Huynh, T.T.; Nguyen, T.H.; Lee, J.J.; Tan, Y.; et al. Kaempferol-induced growth inhibition and apoptosis in A549 lung cancer cells is mediated by activation of MEK-MAPK. J. Cell Physiol. 2003, 197, 110–121. [Google Scholar] [CrossRef] [PubMed]
  33. Luo, H.; Rankin, G.O.; Li, Z.L.; DePriest, L.; Chen, Y.C. Kaempferol induces apoptosis in ovarian cancer cells through activating p53 in the intrinsic pathway. Food Chem. 2011, 128, 513–519. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Choi, E.J.; Ahn, W.S. Kaempferol induced the apoptosis via cell cycle arrest in human breast cancer MDA-MB-453 cells. Nutr. Res. Pract. 2008, 2, 322–325. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Ming, Y.; Chen, X.B.; Wang, S.L.; Li, Y. Expression of ER and AR in lung cancer. Chin. J. Lung Cancer 2008, 11, 126–129. [Google Scholar]
  36. Wang, Y.; Romigh, T.; He, X.; Tan, M.H.; Orloff, M.S.; Silverman, R.H. Differential regulation of PTEN expression by androgen receptor in prostate and breast cancers. Oncogene 2011, 30, 4327–4338. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Stabile, L.P.; Lyker, J.S.; Gubish, C.T.; Zhang, W.P.; Grandis, J.R.; Siegfried, J.M. Combined targeting of the estrogen receptor and the epidermal growth factor receptor in non-small cell lung cancer shows enhanced antiproliferative effects. Cancer Res. 2005, 65, 1459–1470. [Google Scholar] [CrossRef] [Green Version]
  38. Yuk, J.E.; Woo, J.S.; Yun, C.Y.; Lee, J.S.; Kim, J.H.; Song, G.Y.; Yang, E.J.; Hur, I.K.; Kim, I.S. Effects of lactose-β-sitosterol and β-sitosterol on ovalbumin-induced lung inflammation in actively sensitized mice. Int. Immunopharmacol. 2007, 7, 1517–1527. [Google Scholar] [CrossRef]
  39. Fraile, L.; Crisci, E.; Lorena, C.; María, A.; Navarro, J.O.; María, M. Immunomodulatory properties of beta-sitosterol in pig immune responses. Int. Immunopharmacol. 2012, 13, 316–321. [Google Scholar] [CrossRef]
  40. Wang, X.; Li, M.; Hu, M.; Wei, P.; Zhu, W. BAMBI overexpression together with β-sitosterol ameliorates NSCLC via inhibiting autophagy and inactivating TGF-β/Smad2/3 pathway. Oncol. Rep. 2017, 37, 3046–3054. [Google Scholar] [CrossRef] [Green Version]
  41. Rajavel, T.; Mohankumar, R.; Archunan, G.; Ruckmani, K.; Devi, K.P. Beta sitosterol and Daucosterol (phytosterols identified in Grewia tiliaefolia) perturbs cell cycle and induces apoptotic cell death in A549 cells. Sci. Rep. 2017, 7, 3418. [Google Scholar] [CrossRef] [PubMed]
  42. Niu, X.; Zhang, H.; Li, W.; Wang, Y.; Mu, Q.; Wang, X.; He, Z.; Yao, H. Protective effect of cavidine on acetic acid-induced murine colitis via regulating antioxidant, cytokine profile and NF-kB signal transduction pathways. Chem. Biol. Interact. 2015, 239, 34–45. [Google Scholar] [CrossRef] [PubMed]
  43. Niu, X.; Zhang, H.; Li, W.; Mu, Q.; Yao, H.; Wang, Y. Anti-inflammatory effects of Cavidine in Vitro and in Vivo, a Selective COX-2 Inhibitor in LPS-Induced Peritoneal Macrophages of Mouse. Inflammation 2015, 38, 923–933. [Google Scholar] [CrossRef] [PubMed]
  44. Qin, L.; Jin, L.T.; Lu, L.L.; Lu, X.Y.; Zhang, C.L.; Zhang, F.Y.; Liang, W. Naringenin reduces lung metastasis in a breast cancer resection model. Protein Cell 2011, 2, 507–516. [Google Scholar] [CrossRef] [PubMed]
  45. Parashar, P.; Rathor, M.; Dwivedi, M.; Saraf, S.A. Hyaluronic Acid Decorated Naringenin Nanoparticles, Appraisal of Chemopreventive and Curative Potential for Lung Cancer. Pharmaceutics 2018, 10, 23. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Jin, C.Y.; Park, C.; Hwang, H.J.; Kim, G.Y.; Choi, B.T.; Kim, W.J. Naringenin up-regulates the expression of death receptor 5 and enhances TRAIL-induced apoptosis in human lung cancer A549 cell. Mol. Nutr. Food Res. 2011, 55, 300–309. [Google Scholar] [CrossRef]
  47. Yang, Y.; Zhao, Y.; Ai, X.H.; Cheng, B.J.; Lu, S. Formononetin suppresses the proliferation of human non-small cell lung cancer through induction of cell cycle arrest and apoptosis. Int. J. Clin. Exp. Pathol. 2014, 7, 8453–8461. [Google Scholar]
  48. Cathcart, M.C.; Useckaite, Z.; Drakeford, C.; Semik, V.; Lysaght, J.; Gately, K.; O’Byrne, K.J.; Pidgeon, G.P. Anti-cancer effects of baicalein in non-small cell lung cancer in-vitro and in-vivo. BMC Cancer 2016, 6, 707. [Google Scholar] [CrossRef] [Green Version]
  49. Lee, H.Z.; Leung, H.W.C.; Lai, M.Y.; Wu, C.H. Baicalein induced cell cycle arrest and apoptosis in human lung squamous carcinoma ch27 cells. Anticancer. Res. 2015, 25, 959–964. [Google Scholar]
  50. Ohdaira, H.; Sekiguchi, M.; Miyata, K.; Yoshida, K. MicroRNA-494 suppresses cell proliferation and induces senescence in A549 lung cancer cells. Cell Prolif. 2012, 45, 32–38. [Google Scholar] [CrossRef]
  51. Roberson, R.S.; Kussick, S.J.; Vallieres, E.; Chen, S.Y.J.; Wu, D.Y. Escape from Therapy-I Induced Accelerated Cellular Senescence in p53-Null Lung Cancer Cells and in Human Lung Cancers. Cancer Res. 2005, 65, 2795–2803. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Arden, K.C. FoXO: Linking New Signaling Pathways. Mol. Cell 2004, 14, 416–418. [Google Scholar] [CrossRef]
  53. Li, H.; Ouyang, R.Y.; Wang, Z.; Zhou, W.H.; Chen, H.Y.; Jiang, Y.W.; Zhang, Y.; Li, H.; Liao, M.; Wang, W.; et al. MiR-150 promotes cellular metastasis in non-small cell lung cancer by targeting FOXO4. Sci. Rep. 2016, 6, 1–11. [Google Scholar] [CrossRef] [PubMed]
  54. Chen, Z.; Zhao, L.; Zhao, F.; Yang, G.; Wang, J.J. Tetrandrine suppresses lung cancer growth and induces apoptosis, potentially via the VEGF/HIF-1α/ICAM-1 signaling pathway. Oncol Lett. 2018, 15, 7433–7437. [Google Scholar] [CrossRef] [Green Version]
  55. Xu, R.T.; Shu, Y.Q. Estrogen and its signaling pathway in non-small cell lung cancer (NSCLC). J. Nanjing Med. Univ. 2009, 23, 217–223. [Google Scholar] [CrossRef]
  56. Marquez-Garban, D.C.; Pietras, R.J. Estrogen-Signaling Pathways in Lung Cancer. Adv. Exp. Med. Biol. 2008, 617, 281–289. [Google Scholar]
  57. Bikkavilli, R.K.; Avasarala, S.; Van Scoyk, M.; Arcaroli, J.; Brzezinski, C.; Zhang, W.; Edwards, M.G.; Rathinam, M.K.; Zhou, T.; Tauler, J. Wnt7a is a novel inducer of β-catenin-independent tumor-suppressive cellular senescence in lung cancer. Oncogene 2015, 34, 5317–5328. [Google Scholar] [CrossRef] [Green Version]
  58. Mohd, F.; Haitao, W.; Uma, G.; Little, P.J.; Xu, J.P.; Zheng, W.H. FOXO Signaling Pathways as Therapeutic Targets in Cancer. Int. J. Biol. Sci. 2017, 13, 815–827. [Google Scholar]
  59. Liu, W.; Chen, X.; He, Y.; Tian, Y.; Xu, L.; Ma, Y.; Hu, P.; Su, K.; Luo, Z.; Wei, L.; et al. TNF α inhibits xenograft tumor formation by A549 lung cancer cells in nude mice via the HIF 1α/VASP signaling pathway. Oncol. Rep. 2019, 41, 2418–2430. [Google Scholar] [CrossRef] [Green Version]
  60. Zhong, G.W.; Chen, X.; Fang, X.; Wang, D.S.; Xie, M.X.; Chen, Q. Fra-1 is upregulated in lung cancer tissues and inhibits the apoptosis of lung cancer cells by the P53 signaling pathway. Oncol. Rep. 2015, 35, 447–453. [Google Scholar] [CrossRef] [Green Version]
  61. Zhang, H.Y.; Yang, W.; Lu, J.B. Knockdown of GluA2 induces apoptosis in non-small-cell lung cancer A549 cells through the p53 signaling pathway. Oncol. Lett. 2017, 14, 1005–1010. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Hoque, M.O.; Brait, M.; Rosenbaum, E.; Poeta, M.L.; Pal, P.; Begum, S.; Dasgupta, S.; Carvalho, A.L.; Ahrendt, S.A.; Westra, W.H.; et al. Genetic and Epigenetic Analysis of erbB Signaling Pathway Genes in Lung Cancer. J. Thorac. Oncol. 2010, 5, 1887–1893. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Xie, S.T.; Li, J.; Wang, J.H.; Wu, Q.; Yang, P.A.; Hsu, H.C.; Smythies, L.E.; Mountz, J.D. IL-17 Activates the Canonical NF-kB Signaling Pathway in Autoimmune B Cells of BXD2 Mice To Upregulate the Expression of Regulators of G-Protein Signaling 16. J. Immunol. 2010, 184, 2289–2296. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Zhao, Y.F.; Li, Y.Y.; Wang, J.M.; Manthari, R.K.; Wang, J.D. Fluoride induces apoptosis and autophagy through the IL-17 signaling pathway in mice hepatocytes. Arch. Toxicol. 2018, 92, 3277–3289. [Google Scholar] [CrossRef] [PubMed]
  65. Thurston, G.; Kitajewski, J. VEGF and Delta-Notch, interacting signalling pathways in tumor angiogenesis. Br. J. Cancer. 2008, 99, 1204–1209. [Google Scholar] [CrossRef] [Green Version]
  66. Stacker, S.A.; Achen, M.G. The VEGF signaling pathway in cancer, the road ahead. Chin. J. Cancer 2013, 32, 297–302. [Google Scholar]
  67. Cao, X.T.; He, W. Medical Immunology; People’s Medical Publishing House: Beijing, China, 2015; p. 133. [Google Scholar]
  68. Geng, J.; Altman, J.D.; Krishnakumar, S.; Raghavan, M. Empty conformers of HLA-I preferentially bind CD8 and regulate CD8+ T cell function. ELife Sci. 2018, 7, 1–20. [Google Scholar] [CrossRef]
  69. Yang, L.; Wang, L.J.; Shi, G.L.; Ni, L.; Song, C.X.; Zhang, Z.X.; Xu, S.F. Analysis of HLA-A, HLA-B and HLA-DRB1 alleles in Chinese patients with lung cancer. Genet Mol. Res. 2010, 9, 750–755. [Google Scholar] [CrossRef]
  70. Ji, G.H.; Li, M.; Cui, Y.; Wang, J.F. The relationship of CASP8 polymorphism and cancer susceptibility, a meta-analysis. Cell Mol. Biol. 2014, 6, 20–28. [Google Scholar]
  71. Zhou, C.C.; Wu, Y.L.; Fei, K. Targeted Therapy for Lung Cancer; People’s Medical Publishing House: Beijing, China, 2016; p. 18. [Google Scholar]
  72. Scatena, R.; Bottoni, P.; Botta, G.; Martorana, G.E.; Giardina, B. The role of mitochondria in pharmaco-toxicology: A reevaluation of an old, newly emerging topic. AJP Cell Physiol. 2007, 293, C12–C21. [Google Scholar] [CrossRef] [Green Version]
  73. Hacker, G.; Paschen, S.A. Therapeutic targets in the mitochondrial apoptotic pathway. Expert. Opin. Targets 2007, 11, 515–526. [Google Scholar] [CrossRef] [PubMed]
  74. Hart, K.; Landvik, N.E.; Lind, H.; Skaug, V.; Haugen, A.; Zienolddiny, S. A combination of functional polymorphisms in the CASP8, MMP1, IL10 and SEPS1 genes affects risk of non-small cell lung cancer. Lung Cancer 2011, 71, 123–129. [Google Scholar] [CrossRef] [PubMed]
  75. Maelfait, J.; Vercammen, E.; Janssens, S.; Schotte, P.; Haegman, M.; Magez, S.; Beyaert, R. Stimulation of Toll-like receptor 3 and 4 induces interleukin-1beta maturation by caspase-8. J. Exp. Med. 2008, 205, 1967–1973. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Chun, H.J.; Zheng, L.; Ahmad, M.; Wang, J.; Speirs, C.K.; Siegel, R.M.; Dale, J.K.; Puck, J.; Davis, J.; Hall, C.G.; et al. Pleiotropic defects in lymphocyte activation caused by caspase-8 mutations lead to human immunodeficiency. Nature 2002, 419, 395–399. [Google Scholar] [CrossRef] [Green Version]
  77. Rébé, C.; Cathelin, S.; Launay, S.; Filomenko, R.; Prévotat, L.; L’Ollivier, C.; Gyan, E.; Micheau, O.; Grant, S.; Dubart-Kupperschmitt, A.; et al. Caspase-8 prevents sustained activation of NF-kB in monocytes undergoing macrophagic differentiation. Blood 2007, 109, 1442–1450. [Google Scholar] [CrossRef] [Green Version]
  78. Galluzzi, L.; Joza, N.; Tasdemir, E.; Maiuri, M.C.; Hengartner, M.; Abrams, J.M.; Tavernarakis, N.; Penninger, J.; Madeo, F.; Kroemer, G. No death without life: Vital functions of apoptotic effectors. Cell Death Differ. 2008, 15, 1113–1123. [Google Scholar] [CrossRef] [Green Version]
  79. Cuenda, A.; Rousseau, S. p38 MAP-kinases pathway regulation, function and role in human diseases. Biochim. Biophys. Acta 2007, 1773, 1358–1375. [Google Scholar] [CrossRef] [Green Version]
  80. Desideri, E.; Vegliante, R.; Cardaci, S.; Nepravishta, R.; Paci, M.; Ciriolo, M.R. MAPK14/p38α-dependent modulation of glucose metabolism affects ROS levels and autophagy during starvation. Autophagy 2014, 10, 1652–1665. [Google Scholar] [CrossRef] [Green Version]
  81. Valladares, A.; Alvarez, A.M.; Ventura, J.J.; Roncero, C.; Benito, M.; Porras, A. p38 Mitogen-Activated Protein Kinase Mediates Tumor Necrosis Factor-α-Induced Apoptosis in Rat Fetal Brown Adipocytes. Endocrinology 2000, 141, 4383–4395. [Google Scholar] [CrossRef]
  82. Edlund, S.; Bu, S.; Schuster, N.; Aspenström, P.; Heuchel, R.; Heldin, N.E.; Dijke, P.; Heldin, C.H.; Landström, M. Transforming growth factor-beta 1 (TGF-beta)-induced apoptosis of prostate cancer cells involves Smad7-dependent activation of p38 by TGF-beta-activated kinase 1 and mitogen-activated protein kinase 3. Mol. Bio. Cell 2003, 14, 529–544. [Google Scholar] [CrossRef] [Green Version]
  83. Zhuang, S.; Demirs, J.T.; Kochevar, I.E. p38 Mitogen-activated Protein Kinase Mediates Bid Cleavage, Mitochondrial Dysfunction, and Caspase-3 Activation during Apoptosis Induced by Singlet Oxygen but Not by Hydrogen Peroxide. J. Bio. Chem. 2000, 275, 25939–25948. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Chen, Y.J.; Chen, H.P.; Cheng, Y.J.; Lin, Y.H.; Liu, K.W.; Chen, Y.J.; Hou, M.F.; Wu, Y.C.; Lee, Y.C.; Yuan, S.S. The synthetic flavonoid WYC02-9 inhibits colorectal cancer cell growth through ROS-mediated activation of MAPK14 pathway. Life Sci. 2013, 92, 1081–1092. [Google Scholar] [CrossRef] [PubMed]
  85. Chien, S.T.; Lin, S.S.; Wang, C.K.; Lee, Y.B.; Chen, K.S.; Fong, Y.; Shih, Y.W. Acacetin inhibits the invasion and migration of human NSCLC a549 cells by suppressing the p38α MAPK signaling pathway. Mol. Cell Biochem. 2011, 350, 135–148. [Google Scholar] [CrossRef] [PubMed]
  86. Juo, P.; Kuo, C.J.; Reynolds, S.E.; Konz, R.F.; Raingeaud, J.; Davis, R.J.; Ulevitch, R.J.; Nemerow, G.R.; Han, J. Fas activation of the p38 mitogen-activated protein kinase signaling pathway requires ICE CED-3 family proteases. Mol. Cell Biol. 1997, 17, 24–35. [Google Scholar] [CrossRef] [Green Version]
  87. Pogozelski, A.; Li, P.; Jiang, H.P.; Yan, Z. p38 gamma MAPK is required for exercise-induced Pgc-1alpha expression and mitochondrial biogenesis in skeletal muscle. FASEB J. 2008, 22, 754–767. [Google Scholar]
  88. Toshiyuki, O.; Glenn, E.; Michael, B. Yaffe1MAP kinase pathways activated by stress, the p38MAPK pathway. Crit. Care Med. 2000, 28, 67–71. [Google Scholar]
  89. Saitou, Y.; Shiraki, K.; Yamanaka, T.; Miyashita, K.; Inoue, T.; Yamanaka, Y.; Yamaguchi, Y.; Enokimura, N.; Yamamoto, N.; Itou, K.; et al. Augmentation of tumor necrosis factor family-Induced apoptosis by E3330 in human hepatocellular carcinoma cell lines via inhibition of NF kappa B. World J. Gastroenterol. 2005, 11, 6258–6261. [Google Scholar] [CrossRef]
  90. Pearl-Yafe, M.; Yolcu, E.S.; Stein, J.; Kaplan, O.; Yaniv, I.; Shirwan, H.; Askenasy, N. Fas ligand enhances hematopoietic cell engraftment through abrogation of alloimmune responses and nonimmunogenic interactions. Stem. Cells 2007, 25, 1448–1455. [Google Scholar] [CrossRef]
Medsci 08 00001 g001 550
Figure 1. Herb-ingredient-target network. Blue square: herbs, green diamond: ingredients, orange triangle: targets, Blue edges: the relationship between herbs and components; Pink edges: the relationship components herbs and targets.
Figure 1. Herb-ingredient-target network. Blue square: herbs, green diamond: ingredients, orange triangle: targets, Blue edges: the relationship between herbs and components; Pink edges: the relationship components herbs and targets.
Medsci 08 00001 g001
Medsci 08 00001 g002 550
Figure 2. The protein–protein interaction (PPI) of 82 targets.
Figure 2. The protein–protein interaction (PPI) of 82 targets.
Medsci 08 00001 g002
Medsci 08 00001 g003 550
Figure 3. The shapes of different colors represent different types of nodes. Blue ellipse represents traditional Chinese medicine, green square represents chemical composition, orange triangle represents the predictive target of XQD, red triangle represents the direct target of XQD for lung cancer treatment, and light blue represents the indirect target of XQD for LC treatment.
Figure 3. The shapes of different colors represent different types of nodes. Blue ellipse represents traditional Chinese medicine, green square represents chemical composition, orange triangle represents the predictive target of XQD, red triangle represents the direct target of XQD for lung cancer treatment, and light blue represents the indirect target of XQD for LC treatment.
Medsci 08 00001 g003
Medsci 08 00001 g004 550
Figure 4. Target-pathway network. Small nodes stand for therapeutic targets for lung cancer. Large nodes stand for main pathways based on enrichment analysis.
Figure 4. Target-pathway network. Small nodes stand for therapeutic targets for lung cancer. Large nodes stand for main pathways based on enrichment analysis.
Medsci 08 00001 g004
Medsci 08 00001 g005 550
Figure 5. Main pathways enriched by major targets. The top 10 pathways selected to demonstrate the crucial biological actions of major targets. The abscissa stands for target counts in each pathway, and the ordinate stands for main pathways.
Figure 5. Main pathways enriched by major targets. The top 10 pathways selected to demonstrate the crucial biological actions of major targets. The abscissa stands for target counts in each pathway, and the ordinate stands for main pathways.
Medsci 08 00001 g005
Medsci 08 00001 g006 550
Figure 6. Target size and color settings were used to reflect Degree value size. The larger the shape and the darker the color of the node indicates that the target (gene) could play a role in the more immune tissues and cells.
Figure 6. Target size and color settings were used to reflect Degree value size. The larger the shape and the darker the color of the node indicates that the target (gene) could play a role in the more immune tissues and cells.
Medsci 08 00001 g006
Medsci 08 00001 g007 550
Figure 7. The target of XQD for LC therapy regulates immune-mediated tumor cell apoptosis. DC, dendritic cell; MHC, Major histocompatibility complex, TCR, T-cell receptor, CTL, Cytotoxic T cells, DD, Death domain, FADD, Fas-associated death domain, TRADD, TNF receptor associated death domain. HLA-B, CASP8 and MAPK14 were three therapeutic targets of XQD for LC.
Figure 7. The target of XQD for LC therapy regulates immune-mediated tumor cell apoptosis. DC, dendritic cell; MHC, Major histocompatibility complex, TCR, T-cell receptor, CTL, Cytotoxic T cells, DD, Death domain, FADD, Fas-associated death domain, TRADD, TNF receptor associated death domain. HLA-B, CASP8 and MAPK14 were three therapeutic targets of XQD for LC.
Medsci 08 00001 g007
Table
Table 1. Active ingredients and ADME parameters of Xia Qi Decoction (XQD).
Table 1. Active ingredients and ADME parameters of Xia Qi Decoction (XQD).
IDMOL IDMolecule NameOB/%DLHerb
ID001MOL000359sitosterol36.910.75CR, PR, FR, AC
ID002MOL004328naringenin59.290.21CR, LO
ID003MOL0051005,7-dihydroxy-2-(3-hydroxy-4-methoxyphenyl)chroman-4-one47.740.27CR
ID004MOL005815Citromitin86.90.51CR
ID005MOL005828nobiletin61.670.52CR
ID006MOL00175524-Ethylcholest-4-en-3-one36.080.76AT
ID007MOL002670Cavidine35.640.81AT
ID008MOL002714baicalein33.520.21AT
ID009MOL002776Baicalin40.120.75AT
ID010MOL000358beta-sitosterol36.910.75AT, FR, PR
ID011MOL000449Stigmasterol43.830.76AT, AC
ID012MOL005030gondoic acid30.70.2AT, AC
ID013MOL000519coniferin31.110.32AT
ID014MOL00693610,13-eicosadienoic39.990.2AT
ID015MOL00693712,13-epoxy-9-hydroxynonadeca-7,10-dienoic acid42.150.24AT
ID016MOL006957(3S,6S)-3-(benzyl)-6-(4-hydroxybenzyl)piperazine-2,5-quinone46.890.27AT
ID017MOL003578Cycloartenol38.690.78AT
ID018MOL006967beta-D-Ribofuranoside, xanthine-944.720.21AT
ID019MOL00191011alpha,12alpha-epoxy-3beta-23-dihydroxy-30-norolean-20-en-28,12beta-olide64.770.38PR
ID020MOL001918paeoniflorgenone87.590.37PR
ID021MOL001919(3S,5R,8R,9R,10S,14S)-3,17-dihydroxy-4,4,8,10,14-pentamethyl-2,3,5,6,7,9-hexahydro-1H-cyclopenta[a]phenanthrene-15,16-dione43.560.53PR
ID022MOL001921Lactiflorin49.120.8PR
ID023MOL001924paeoniflorin53.870.79PR
ID024MOL001925paeoniflorin_qt68.180.4PR
ID025MOL001928albiflorin_qt66.640.33PR
ID026MOL001930benzoyl paeoniflorin31.270.75PR
ID027MOL000211Mairin55.380.78PR, LO, AC
ID028MOL000422kaempferol41.880.24PR
ID029MOL000492(+)-catechin54.830.24PR, AC
ID030MOL004624Longikaurin A47.720.53SC
ID031MOL005317Deoxyharringtonine39.270.81SC
ID032MOL008956Angeloylgomisin O31.970.85SC
ID033MOL008957Schizandrer B30.710.83SC
ID034MOL008968Gomisin-A30.690.78SC
ID035MOL008974Gomisin G32.680.83SC
ID036MOL008978Gomisin R34.840.86SC
ID037MOL008992Wuweizisu C46.270.84SC
ID038MOL001749ZINC0386043443.590.35FR
ID039MOL004440Peimisine57.40.81FR
ID040MOL009027Cyclopamine55.420.82FR
ID041MOL009572Chuanbeinone41.070.71FR
ID042MOL009579ent-(16S)-atisan-13,17-oxide47.740.43FR
ID043MOL009586isoverticine48.230.67FR
ID044MOL009588Korseveriline35.160.68FR
ID045MOL009589Korseverinine53.510.71FR
ID046MOL009593verticinone60.070.67FR
ID047MOL009596sinpemine A46.960.71FR
ID048MOL009599songbeinone45.350.71FR
ID049MOL010921estrone53.560.32AC
ID050MOL010922Diisooctyl succinate31.620.23AC
ID051MOL00221111,14-eicosadienoic acid39.990.2AC
ID052MOL002372(6Z,10E,14E,18E)-2,6,10,15,19,23-hexamethyltetracosa-2,6,10,14,18,22-hexaene33.550.42AC
ID053MOL000953CLR37.870.68AC
ID054MOL002311Glycyrol90.780.67AC, LO
ID055MOL003410Ziziphin_qt66.950.62AC
ID056MOL004355Spinasterol42.980.76AC
ID057MOL004841Licochalcone B76.760.19AC, LO
ID058MOL004903liquiritin65.690.74AC, LO
ID059MOL004908Glabridin53.250.47AC, LO
ID060MOL005017Phaseol78.770.58AC, LO
ID061MOL007207Machiline79.640.24AC
ID062MOL012922l-SPD87.350.54AC
ID063MOL000273(2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-dihydroxy-4,4,10,13,14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-6-methylhept-5-enoic acid30.930.81PC
ID064MOL000275trametenolic acid38.710.8PC
ID065MOL0002767,9(11)-dehydropachymic acid35.110.81PC
ID066MOL000279Cerevisterol37.960.77PC
ID067MOL000280(2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-dihydroxy-4,4,10,13,14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-5-isopropyl-hex-5-enoic acid31.070.82PC
ID068MOL000282ergosta-7,22E-dien-3beta-ol43.510.72PC
ID069MOL000283Ergosterol peroxide40.360.81PC
ID070MOL000285(2R)-2-[(5R,10S,13R,14R,16R,17R)-16-hydroxy-3-keto-4,4,10,13,14-pentamethyl-1,2,5,6,12,15,16,17-octahydrocyclopenta[a]phenanthren-17-yl]-5-isopropyl-hex-5-enoic acid38.260.82PC
ID071MOL0002873beta-Hydroxy-24-methylene-8-lanostene-21-oic acid38.70.81PC
ID072MOL000289pachymic acid33.630.81PC
ID073MOL000290Poricoic acid A30.610.76PC
ID074MOL000291Poricoic acid B30.520.75PC
ID075MOL000292poricoic acid C38.150.75PC
ID076MOL000296hederagenin36.910.75PC
ID077MOL000300dehydroeburicoic acid44.170.83PC
ID078MOL005020dehydroglyasperins C53.820.37LO
ID079MOL005018Xambioona54.850.87LO
ID080MOL005012Licoagroisoflavone57.280.49LO
ID081MOL005007Glyasperins M72.670.59LO
ID082MOL005003Licoagrocarpin58.810.58LO
ID083MOL005001Gancaonin H50.10.78LO
ID084MOL005000Gancaonin G60.440.39LO
ID085MOL0049938-prenylated eriodictyol53.790.4LO
ID086MOL0049907,2′,4′-trihydroxy-5-methoxy-3-arylcoumarin83.710.27LO
ID087MOL0049591-Methoxyphaseollidin69.980.64LO
ID088MOL004941(2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one71.120.18LO
ID089MOL0049141,3-dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone62.90.53LO
ID090MOL004912Glabrone52.510.5LO
ID091MOL004910Glabranin52.90.31LO
ID092MOL004907Glyzaglabrin61.070.35LO
ID093MOL004904licopyranocoumarin80.360.65LO
ID094MOL004891shinpterocarpin80.30.73LO
ID095MOL004885licoisoflavanone52.470.54LO
ID096MOL004879Glycyrin52.610.47LO
ID097MOL0048633-(3,4-dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone66.370.41LO
ID098MOL004856Gancaonin A51.080.4LO
ID099MOL004855Licoricone63.580.47LO
ID100MOL0048493-(2,4-dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin59.620.43LO
ID101MOL0048388-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol58.440.38LO
ID102MOL004835Glypallichalcone61.60.19LO
ID103MOL004829Glepidotin B64.460.34LO
ID104MOL004824(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one60.250.63LO
ID105MOL004820kanzonols W50.480.52LO
ID106MOL004810glyasperin F75.840.54LO
ID107MOL004808glyasperin B65.220.44LO
ID108MOL003656Lupiwighteone51.640.37LO
ID109MOL001484Inermine75.180.54LO
ID110MOL000500Vestitol74.660.21LO
ID111MOL000392formononetin69.670.21LO
ID112MOL000239Jaranol50.830.29LO
Table
Table 2. The topological properties of key components.
Table 2. The topological properties of key components.
HerbMOL NameMOL IDDegree
FR PR ATBeta-sitosterolMOL00035849
PRKaempferolMOL00042244
ACL-SPDMOL01292244
ATCavidineMOL00267043
AC ATStigmasterolMOL00044939
ACEstroneMOL01092134
ACMachilineMOL00720732
LOShinpterocarpinMOL00489130
LOFormononetinMOL00039230
LONaringeninMOL00432829
LO CR1-MethoxyphaseollidinMOL00495928
LO LicoagrocarpinMOL00500328
Table
Table 3. The Topological Properties of Targets.
Table 3. The Topological Properties of Targets.
Target IDGene NameProtein NameDegree
P10275ARAndrogen receptor83
P03372ESR1Estrogen receptor74
P00918CA2Carbonic anhydrase 260
P37231PPARGPeroxisome proliferator-activated receptor gamma56
P35354PTGS2Prostaglandin G/H synthase 256
P35228NOS2Nitric oxide synthase, inducible53
P00734F2Prothrombin49
P27487DPP4Dipeptidyl peptidase 449
Q92731ESR2Estrogen receptor beta47
Q07785CRK2Cell division control protein 2 homolog45
P49841GSK3BGlycogen synthase kinase-3 beta46
O14757CHEK1Serine/threonine-protein kinase Chk145
P18031PTPN1Tyrosine-protein phosphatase non-receptor type 143
Q16539MAPK14Mitogen-activated protein kinase 1439
P07900HSP90AA1Heat shock protein HSP 90-alpha38
P08238HSP90AB1Heat shock protein HSP 90-beta37
P23219PTGS1Prostaglandin G/H synthase 132
P00742F10Coagulation factor X31
Q14524SCN5ASodium channel protein type 5 subunit alpha30
Table
Table 4. Key components of XQD for lung cancer (LC) treatment.
Table 4. Key components of XQD for lung cancer (LC) treatment.
HerbMOL IDMOL NameDegree
FR PR ATMOL000358Beta-sitosterol17
PRMOL000422Kaempferol14
ATMOL002670Cavidine13
CR LOMOL004328Naringenin12
LOMOL000392Formononetin11
ACMOL012922l-SPD11
ATMOL002714Baicalein10
AT ACMOL000449Stigmasterol10
LOMOL005003Licoagrocarpin10
LOMOL0049591-Methoxyphaseollidin10
LO ACMOL004908Glabridin10
LOMOL004891Shinpterocarpin10
ACMOL004841Licochalcone B10
LOMOL001484Inermine10
ACMOL010921Estrone10
AC PRMOL000492(+)-catechin9
CRMOL005828Nobiletin9
LO ACMOL005017Phaseol9
LOMOL005000Gancaonin G9
LOMOL004941(2R)-7-hydroxy-2-(4-Hydroxyphenyl)chroman-4-one9
LOMOL0048493-(2,4-dihydroxyphenyl)-8-(1,1-Dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin9
LOMOL004835Glypallichalcone9
LOMOL004829Glepidotin B9
LOMOL004824(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one9
ACMOL007207Machiline9
Table
Table 5. Direct Action of 14 LC Therapeutic targets.
Table 5. Direct Action of 14 LC Therapeutic targets.
Types of ActionDegreeUniprot IDGene Name
Direct action75P03372ESR1
Direct action45O14757CHEK1
Direct action40Q16539MAPK14
Direct action28P07550ADRB2
Direct action16P29474NOS3
Direct action14P35968KDR
Direct action11P35372OPRM1
Direct action6P10415BCL2
Direct action3P08588ADRB1
Direct action1P27169PON1
Direct action1P14416DRD2
Direct action1P05164MPO
Direct action1P03956MMP1
Direct action1P01130LDLR
Table
Table 6. Indirect action of 27 LC Therapeutic Targets.
Table 6. Indirect action of 27 LC Therapeutic Targets.
Types of ActionDegreeUniprot IDGene Name
Indirect action5P60568IL2
Indirect action5O14746TERT
Indirect action4Q9UJU2LEF1
Indirect action3Q03468ERCC6
Indirect action3P69905HBA2
Indirect action2P62736ACTA2
Indirect action2P16473TSHR
Indirect action2P04035HMGCR
Indirect action2P02741CRP
Indirect action1Q9UBP4DKK3
Indirect action1Q9H3N8HRH4
Indirect action1Q9H3D4TP63
Indirect action1Q14790CASP8
Indirect action1Q12988HSPB3
Indirect action1P84022SMAD3
Indirect action1P57071PRDM15
Indirect action1P42345MTOR
Indirect action1P35568IRS1
Indirect action1P30874SSTR2
Indirect action1P25391LAMA1
Indirect action1P18074ERCC2
Indirect action1P08183ABCB1
Indirect action1P05556ITGB1
Indirect action1P04626ERBB2
Indirect action1P01889HLA-B
Indirect action1P00519ABL1
Indirect action1O60469DSCAM
Table
Table 7. Expression of 8 LC treatment targets in 14 immune tissues and cells.
Table 7. Expression of 8 LC treatment targets in 14 immune tissues and cells.
NameHLA-BCASP8MAPK14SMAD3ACTA2ADRB2BCL2LEF1
Bone Marrow26865481054
Whole Blood6891213492569
Lymph Node31683423368
Thymus229634172435
Tonsil30263411356
Myeloid6301121592264
Monocytes57791358954
Dentritic Cells55881066654
NK cells7161310584175
T Cells(CD+4)5141677651130
T Cells(CD+8)5121575714827
B-Lymphoblasts41078512394
B Cells41185873114
Endothelial1546545364

Share and Cite

MDPI and ACS Style

Zhang, S.; Wang, Y. Network Pharmacology Approach Reveals the Potential Immune Function Activation and Tumor Cell Apoptosis Promotion of Xia Qi Decoction in Lung Cancer. Med. Sci. 2020, 8, 1. https://doi.org/10.3390/medsci8010001

AMA Style

Zhang S, Wang Y. Network Pharmacology Approach Reveals the Potential Immune Function Activation and Tumor Cell Apoptosis Promotion of Xia Qi Decoction in Lung Cancer. Medical Sciences. 2020; 8(1):1. https://doi.org/10.3390/medsci8010001

Chicago/Turabian Style

Zhang, Song, and Yun Wang. 2020. "Network Pharmacology Approach Reveals the Potential Immune Function Activation and Tumor Cell Apoptosis Promotion of Xia Qi Decoction in Lung Cancer" Medical Sciences 8, no. 1: 1. https://doi.org/10.3390/medsci8010001

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop