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. 2019 Dec 29;8(1):1. doi: 10.3390/medsci8010001

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

Song Zhang 1, Yun Wang 1,*
PMCID: PMC7151561  PMID: 31905767

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.

Keywords: traditional Chinese medicine, Xia Qi Decoction, network pharmacology, lung cancer, immunocyte, apoptosis of tumor cells

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).

Table 1.

Active ingredients and ADME parameters of Xia Qi Decoction (XQD).

ID MOL ID Molecule Name OB/% DL Herb
ID001 MOL000359 sitosterol 36.91 0.75 CR, PR, FR, AC
ID002 MOL004328 naringenin 59.29 0.21 CR, LO
ID003 MOL005100 5,7-dihydroxy-2-(3-hydroxy-4-methoxyphenyl)chroman-4-one 47.74 0.27 CR
ID004 MOL005815 Citromitin 86.9 0.51 CR
ID005 MOL005828 nobiletin 61.67 0.52 CR
ID006 MOL001755 24-Ethylcholest-4-en-3-one 36.08 0.76 AT
ID007 MOL002670 Cavidine 35.64 0.81 AT
ID008 MOL002714 baicalein 33.52 0.21 AT
ID009 MOL002776 Baicalin 40.12 0.75 AT
ID010 MOL000358 beta-sitosterol 36.91 0.75 AT, FR, PR
ID011 MOL000449 Stigmasterol 43.83 0.76 AT, AC
ID012 MOL005030 gondoic acid 30.7 0.2 AT, AC
ID013 MOL000519 coniferin 31.11 0.32 AT
ID014 MOL006936 10,13-eicosadienoic 39.99 0.2 AT
ID015 MOL006937 12,13-epoxy-9-hydroxynonadeca-7,10-dienoic acid 42.15 0.24 AT
ID016 MOL006957 (3S,6S)-3-(benzyl)-6-(4-hydroxybenzyl)piperazine-2,5-quinone 46.89 0.27 AT
ID017 MOL003578 Cycloartenol 38.69 0.78 AT
ID018 MOL006967 beta-D-Ribofuranoside, xanthine-9 44.72 0.21 AT
ID019 MOL001910 11alpha,12alpha-epoxy-3beta-23-dihydroxy-30-norolean-20-en-28,12beta-olide 64.77 0.38 PR
ID020 MOL001918 paeoniflorgenone 87.59 0.37 PR
ID021 MOL001919 (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-dione 43.56 0.53 PR
ID022 MOL001921 Lactiflorin 49.12 0.8 PR
ID023 MOL001924 paeoniflorin 53.87 0.79 PR
ID024 MOL001925 paeoniflorin_qt 68.18 0.4 PR
ID025 MOL001928 albiflorin_qt 66.64 0.33 PR
ID026 MOL001930 benzoyl paeoniflorin 31.27 0.75 PR
ID027 MOL000211 Mairin 55.38 0.78 PR, LO, AC
ID028 MOL000422 kaempferol 41.88 0.24 PR
ID029 MOL000492 (+)-catechin 54.83 0.24 PR, AC
ID030 MOL004624 Longikaurin A 47.72 0.53 SC
ID031 MOL005317 Deoxyharringtonine 39.27 0.81 SC
ID032 MOL008956 Angeloylgomisin O 31.97 0.85 SC
ID033 MOL008957 Schizandrer B 30.71 0.83 SC
ID034 MOL008968 Gomisin-A 30.69 0.78 SC
ID035 MOL008974 Gomisin G 32.68 0.83 SC
ID036 MOL008978 Gomisin R 34.84 0.86 SC
ID037 MOL008992 Wuweizisu C 46.27 0.84 SC
ID038 MOL001749 ZINC03860434 43.59 0.35 FR
ID039 MOL004440 Peimisine 57.4 0.81 FR
ID040 MOL009027 Cyclopamine 55.42 0.82 FR
ID041 MOL009572 Chuanbeinone 41.07 0.71 FR
ID042 MOL009579 ent-(16S)-atisan-13,17-oxide 47.74 0.43 FR
ID043 MOL009586 isoverticine 48.23 0.67 FR
ID044 MOL009588 Korseveriline 35.16 0.68 FR
ID045 MOL009589 Korseverinine 53.51 0.71 FR
ID046 MOL009593 verticinone 60.07 0.67 FR
ID047 MOL009596 sinpemine A 46.96 0.71 FR
ID048 MOL009599 songbeinone 45.35 0.71 FR
ID049 MOL010921 estrone 53.56 0.32 AC
ID050 MOL010922 Diisooctyl succinate 31.62 0.23 AC
ID051 MOL002211 11,14-eicosadienoic acid 39.99 0.2 AC
ID052 MOL002372 (6Z,10E,14E,18E)-2,6,10,15,19,23-hexamethyltetracosa-2,6,10,14,18,22-hexaene 33.55 0.42 AC
ID053 MOL000953 CLR 37.87 0.68 AC
ID054 MOL002311 Glycyrol 90.78 0.67 AC, LO
ID055 MOL003410 Ziziphin_qt 66.95 0.62 AC
ID056 MOL004355 Spinasterol 42.98 0.76 AC
ID057 MOL004841 Licochalcone B 76.76 0.19 AC, LO
ID058 MOL004903 liquiritin 65.69 0.74 AC, LO
ID059 MOL004908 Glabridin 53.25 0.47 AC, LO
ID060 MOL005017 Phaseol 78.77 0.58 AC, LO
ID061 MOL007207 Machiline 79.64 0.24 AC
ID062 MOL012922 l-SPD 87.35 0.54 AC
ID063 MOL000273 (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 acid 30.93 0.81 PC
ID064 MOL000275 trametenolic acid 38.71 0.8 PC
ID065 MOL000276 7,9(11)-dehydropachymic acid 35.11 0.81 PC
ID066 MOL000279 Cerevisterol 37.96 0.77 PC
ID067 MOL000280 (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 acid 31.07 0.82 PC
ID068 MOL000282 ergosta-7,22E-dien-3beta-ol 43.51 0.72 PC
ID069 MOL000283 Ergosterol peroxide 40.36 0.81 PC
ID070 MOL000285 (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 acid 38.26 0.82 PC
ID071 MOL000287 3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid 38.7 0.81 PC
ID072 MOL000289 pachymic acid 33.63 0.81 PC
ID073 MOL000290 Poricoic acid A 30.61 0.76 PC
ID074 MOL000291 Poricoic acid B 30.52 0.75 PC
ID075 MOL000292 poricoic acid C 38.15 0.75 PC
ID076 MOL000296 hederagenin 36.91 0.75 PC
ID077 MOL000300 dehydroeburicoic acid 44.17 0.83 PC
ID078 MOL005020 dehydroglyasperins C 53.82 0.37 LO
ID079 MOL005018 Xambioona 54.85 0.87 LO
ID080 MOL005012 Licoagroisoflavone 57.28 0.49 LO
ID081 MOL005007 Glyasperins M 72.67 0.59 LO
ID082 MOL005003 Licoagrocarpin 58.81 0.58 LO
ID083 MOL005001 Gancaonin H 50.1 0.78 LO
ID084 MOL005000 Gancaonin G 60.44 0.39 LO
ID085 MOL004993 8-prenylated eriodictyol 53.79 0.4 LO
ID086 MOL004990 7,2′,4′-trihydroxy-5-methoxy-3-arylcoumarin 83.71 0.27 LO
ID087 MOL004959 1-Methoxyphaseollidin 69.98 0.64 LO
ID088 MOL004941 (2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one 71.12 0.18 LO
ID089 MOL004914 1,3-dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone 62.9 0.53 LO
ID090 MOL004912 Glabrone 52.51 0.5 LO
ID091 MOL004910 Glabranin 52.9 0.31 LO
ID092 MOL004907 Glyzaglabrin 61.07 0.35 LO
ID093 MOL004904 licopyranocoumarin 80.36 0.65 LO
ID094 MOL004891 shinpterocarpin 80.3 0.73 LO
ID095 MOL004885 licoisoflavanone 52.47 0.54 LO
ID096 MOL004879 Glycyrin 52.61 0.47 LO
ID097 MOL004863 3-(3,4-dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone 66.37 0.41 LO
ID098 MOL004856 Gancaonin A 51.08 0.4 LO
ID099 MOL004855 Licoricone 63.58 0.47 LO
ID100 MOL004849 3-(2,4-dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin 59.62 0.43 LO
ID101 MOL004838 8-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol 58.44 0.38 LO
ID102 MOL004835 Glypallichalcone 61.6 0.19 LO
ID103 MOL004829 Glepidotin B 64.46 0.34 LO
ID104 MOL004824 (2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one 60.25 0.63 LO
ID105 MOL004820 kanzonols W 50.48 0.52 LO
ID106 MOL004810 glyasperin F 75.84 0.54 LO
ID107 MOL004808 glyasperin B 65.22 0.44 LO
ID108 MOL003656 Lupiwighteone 51.64 0.37 LO
ID109 MOL001484 Inermine 75.18 0.54 LO
ID110 MOL000500 Vestitol 74.66 0.21 LO
ID111 MOL000392 formononetin 69.67 0.21 LO
ID112 MOL000239 Jaranol 50.83 0.29 LO
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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.

Figure 1.

Figure 1

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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.

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.

Table 2.

The topological properties of key components.

Herb MOL Name MOL ID Degree
FR PR AT Beta-sitosterol MOL000358 49
PR Kaempferol MOL000422 44
AC L-SPD MOL012922 44
AT Cavidine MOL002670 43
AC AT Stigmasterol MOL000449 39
AC Estrone MOL010921 34
AC Machiline MOL007207 32
LO Shinpterocarpin MOL004891 30
LO Formononetin MOL000392 30
LO Naringenin MOL004328 29
LO CR 1-Methoxyphaseollidin MOL004959 28
LO Licoagrocarpin MOL005003 28
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Table 3.

The Topological Properties of Targets.

Target ID Gene Name Protein Name Degree
P10275 AR Androgen receptor 83
P03372 ESR1 Estrogen receptor 74
P00918 CA2 Carbonic anhydrase 2 60
P37231 PPARG Peroxisome proliferator-activated receptor gamma 56
P35354 PTGS2 Prostaglandin G/H synthase 2 56
P35228 NOS2 Nitric oxide synthase, inducible 53
P00734 F2 Prothrombin 49
P27487 DPP4 Dipeptidyl peptidase 4 49
Q92731 ESR2 Estrogen receptor beta 47
Q07785 CRK2 Cell division control protein 2 homolog 45
P49841 GSK3B Glycogen synthase kinase-3 beta 46
O14757 CHEK1 Serine/threonine-protein kinase Chk1 45
P18031 PTPN1 Tyrosine-protein phosphatase non-receptor type 1 43
Q16539 MAPK14 Mitogen-activated protein kinase 14 39
P07900 HSP90AA1 Heat shock protein HSP 90-alpha 38
P08238 HSP90AB1 Heat shock protein HSP 90-beta 37
P23219 PTGS1 Prostaglandin G/H synthase 1 32
P00742 F10 Coagulation factor X 31
Q14524 SCN5A Sodium channel protein type 5 subunit alpha 30
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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.

Figure 2.

Figure 2

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The protein–protein interaction (PPI) of 82 targets.

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.

Figure 3.

Figure 3

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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.

Table 4.

Key components of XQD for lung cancer (LC) treatment.

Herb MOL ID MOL Name Degree
FR PR AT MOL000358 Beta-sitosterol 17
PR MOL000422 Kaempferol 14
AT MOL002670 Cavidine 13
CR LO MOL004328 Naringenin 12
LO MOL000392 Formononetin 11
AC MOL012922 l-SPD 11
AT MOL002714 Baicalein 10
AT AC MOL000449 Stigmasterol 10
LO MOL005003 Licoagrocarpin 10
LO MOL004959 1-Methoxyphaseollidin 10
LO AC MOL004908 Glabridin 10
LO MOL004891 Shinpterocarpin 10
AC MOL004841 Licochalcone B 10
LO MOL001484 Inermine 10
AC MOL010921 Estrone 10
AC PR MOL000492 (+)-catechin 9
CR MOL005828 Nobiletin 9
LO AC MOL005017 Phaseol 9
LO MOL005000 Gancaonin G 9
LO MOL004941 (2R)-7-hydroxy-2-(4-Hydroxyphenyl)chroman-4-one 9
LO MOL004849 3-(2,4-dihydroxyphenyl)-8-(1,1-Dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin 9
LO MOL004835 Glypallichalcone 9
LO MOL004829 Glepidotin B 9
LO MOL004824 (2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one 9
AC MOL007207 Machiline 9
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Table 5.

Direct Action of 14 LC Therapeutic targets.

Types of Action Degree Uniprot ID Gene Name
Direct action 75 P03372 ESR1
Direct action 45 O14757 CHEK1
Direct action 40 Q16539 MAPK14
Direct action 28 P07550 ADRB2
Direct action 16 P29474 NOS3
Direct action 14 P35968 KDR
Direct action 11 P35372 OPRM1
Direct action 6 P10415 BCL2
Direct action 3 P08588 ADRB1
Direct action 1 P27169 PON1
Direct action 1 P14416 DRD2
Direct action 1 P05164 MPO
Direct action 1 P03956 MMP1
Direct action 1 P01130 LDLR
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Table 6.

Indirect action of 27 LC Therapeutic Targets.

Types of Action Degree Uniprot ID Gene Name
Indirect action 5 P60568 IL2
Indirect action 5 O14746 TERT
Indirect action 4 Q9UJU2 LEF1
Indirect action 3 Q03468 ERCC6
Indirect action 3 P69905 HBA2
Indirect action 2 P62736 ACTA2
Indirect action 2 P16473 TSHR
Indirect action 2 P04035 HMGCR
Indirect action 2 P02741 CRP
Indirect action 1 Q9UBP4 DKK3
Indirect action 1 Q9H3N8 HRH4
Indirect action 1 Q9H3D4 TP63
Indirect action 1 Q14790 CASP8
Indirect action 1 Q12988 HSPB3
Indirect action 1 P84022 SMAD3
Indirect action 1 P57071 PRDM15
Indirect action 1 P42345 MTOR
Indirect action 1 P35568 IRS1
Indirect action 1 P30874 SSTR2
Indirect action 1 P25391 LAMA1
Indirect action 1 P18074 ERCC2
Indirect action 1 P08183 ABCB1
Indirect action 1 P05556 ITGB1
Indirect action 1 P04626 ERBB2
Indirect action 1 P01889 HLA-B
Indirect action 1 P00519 ABL1
Indirect action 1 O60469 DSCAM
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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.

Figure 4.

Figure 4

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Target-pathway network. Small nodes stand for therapeutic targets for lung cancer. Large nodes stand for main pathways based on enrichment analysis.

Figure 5.

Figure 5

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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.

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.

Figure 6.

Figure 6

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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.

Table 7.

Expression of 8 LC treatment targets in 14 immune tissues and cells.

Name HLA-B CASP8 MAPK14 SMAD3 ACTA2 ADRB2 BCL2 LEF1
Bone Marrow 268 6 5 4 8 10 5 4
Whole Blood 689 12 13 4 9 25 6 9
Lymph Node 316 8 3 4 23 3 6 8
Thymus 229 6 3 4 17 2 4 35
Tonsil 302 6 3 4 11 3 5 6
Myeloid 630 11 21 5 9 22 6 4
Monocytes 577 9 13 5 8 9 5 4
Dentritic Cells 558 8 10 6 6 6 5 4
NK cells 716 13 10 5 8 41 7 5
T Cells(CD+4) 514 16 7 7 6 5 11 30
T Cells(CD+8) 512 15 7 5 7 14 8 27
B-Lymphoblasts 410 7 8 5 12 3 9 4
B Cells 411 8 5 8 7 3 11 4
Endothelial 154 6 5 4 5 3 6 4
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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.

Figure 7.

Figure 7

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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.

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. doi: 10.18632/oncotarget.22756. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Parkin D.M., Bray F., Ferlay J., Pisani P. Global cancer statistics: 2002. CA Cancer J. Clin. 2005;55:74–108. doi: 10.3322/canjclin.55.2.74. [DOI] [PubMed] [Google Scholar]
  • 3.Zhao P., Dai M., Chen W.Q., Li N. Cancer trends in China. Jpn. J. Clin. Oncol. 2010;40:281–285. doi: 10.1093/jjco/hyp187. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.3322/caac.21338. [DOI] [PubMed] [Google Scholar]
  • 5.Siegel R.L., Miller K.D., Jemal A. Cancer statistics: 2015. CA Cancer J. Clin. 2015;65:5–29. doi: 10.3322/caac.21254. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1007/s00432-016-2197-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Cheung F. TCM: Made in China. Nature. 2011;480:S82–S83. doi: 10.1038/480S82a. [DOI] [PubMed] [Google Scholar]
  • 8.Jiang W.Y. Therapeutic wisdom in traditional Chinese medicine: A perspective from modern science. Trends Pharmacol. Sci. 2005;26:558–563. doi: 10.1016/j.tips.2005.09.006. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1016/j.drudis.2006.11.008. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1186/1471-2369-9-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1371/journal.pone.0089123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1186/1758-2946-6-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1093/nar/gkx1037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [PubMed] [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. doi: 10.1093/nar/gkl887. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1093/nar/gkx1076. [DOI] [PMC free article] [PubMed] [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. doi: 10.1093/nar/gki033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.3390/ijms16023579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1016/j.jep.2011.04.057. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1093/nar/gky1049. [DOI] [PMC free article] [PubMed] [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. doi: 10.1186/s12906-018-2356-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1093/bioinformatics/btp101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Kanehisa M., Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28:27–30. doi: 10.1093/nar/28.1.27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1093/database/baq020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.3390/ijms13066964. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.3390/molecules22040632. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1080/01635581.2015.1087042. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1002/jcp.10340. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1016/j.foodchem.2011.03.073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.4162/nrp.2008.2.4.322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.3779/j.issn.1009-3419.2008.01.027. [DOI] [PubMed] [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. doi: 10.1038/onc.2011.144. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1158/0008-5472.CAN-04-1872. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1016/j.intimp.2007.07.026. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1016/j.intimp.2012.04.017. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.3892/or.2017.5508. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1038/s41598-017-03511-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1016/j.cbi.2015.06.026. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1007/s10753-014-0054-4. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1007/s13238-011-1056-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.3390/pharmaceutics10010033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1002/mnfr.201000024. [DOI] [PubMed] [Google Scholar]
  • 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. [PMC free article] [PubMed] [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. doi: 10.1186/s12885-016-2740-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [PubMed] [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. doi: 10.1111/j.1365-2184.2011.00798.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1158/0008-5472.CAN-04-1270. [DOI] [PubMed] [Google Scholar]
  • 52.Arden K.C. FoXO: Linking New Signaling Pathways. Mol. Cell. 2004;14:416–418. doi: 10.1016/S1097-2765(04)00213-8. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1038/srep39001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.3892/ol.2018.8190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1016/S1007-4376(09)60059-9. [DOI] [Google Scholar]
  • 56.Marquez-Garban D.C., Pietras R.J. Estrogen-Signaling Pathways in Lung Cancer. Adv. Exp. Med. Biol. 2008;617:281–289. doi: 10.1007/978-0-387-69080-3_26. [DOI] [PubMed] [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. doi: 10.1038/onc.2015.2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.7150/ijbs.20052. [DOI] [PMC free article] [PubMed] [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. doi: 10.3892/or.2019.7026. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.3892/or.2015.4395. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.3892/ol.2017.6234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1097/JTO.0b013e3181f77a53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.4049/jimmunol.0903133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1007/s00204-018-2305-x. [DOI] [PubMed] [Google Scholar]
  • 65.Thurston G., Kitajewski J. VEGF and Delta-Notch, interacting signalling pathways in tumor angiogenesis. Br. J. Cancer. 2008;99:1204–1209. doi: 10.1038/sj.bjc.6604484. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Stacker S.A., Achen M.G. The VEGF signaling pathway in cancer, the road ahead. Chin. J. Cancer. 2013;32:297–302. doi: 10.5732/cjc.012.10319. [DOI] [PMC free article] [PubMed] [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. doi: 10.7554/eLife.36341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.4238/vol9-2gmr735. [DOI] [PubMed] [Google Scholar]
  • 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. [PubMed] [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. doi: 10.1152/ajpcell.00314.2006. [DOI] [PubMed] [Google Scholar]
  • 73.Hacker G., Paschen S.A. Therapeutic targets in the mitochondrial apoptotic pathway. Expert. Opin. Targets. 2007;11:515–526. doi: 10.1517/14728222.11.4.515. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1016/j.lungcan.2010.04.016. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1084/jem.20071632. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1038/nature01063. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1182/blood-2006-03-011585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1038/cdd.2008.28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Cuenda A., Rousseau S. p38 MAP-kinases pathway regulation, function and role in human diseases. Biochim. Biophys. Acta. 2007;1773:1358–1375. doi: 10.1016/j.bbamcr.2007.03.010. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.4161/auto.29456. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1210/endo.141.12.7843. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1091/mbc.02-03-0037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1074/jbc.M001185200. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1016/j.lfs.2013.04.007. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1007/s11010-010-0692-2. [DOI] [PubMed] [Google Scholar]
  • 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. doi: 10.1128/MCB.17.1.24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.3748/wjg.v11.i40.6258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. doi: 10.1634/stemcells.2007-0013. [DOI] [PubMed] [Google Scholar]

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