Mechanism of <i>Synsepalum dulcificum</i> Daniell. Inhibiting Lung Adenocarcinoma

Chen, Qi; Liu, Tingting; Bai, Tuya; Zhang, Mengdi; Hu, Yuxia; Li, Jun; Chang, Fuhou

doi:https://doi.org/10.1155/2022/5242179

Evidence-Based Complementary and Alternative Medicine

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Abstract Materials and Methods Results Discussion Conclusion Data Availability Disclosure Conflicts of Interest Authors’ Contributions Acknowledgments Supplementary Materials References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 5242179 | https://doi.org/10.1155/2022/5242179

Mechanism of Synsepalum dulcificum Daniell. Inhibiting Lung Adenocarcinoma

Qi Chen,¹Tingting Liu,¹Tuya Bai,^1,2,3Mengdi Zhang,^1,2,3Yuxia Hu,^1,2,3Jun Li,^1,2,3and Fuhou Chang^1,2,3

Academic Editor: Siyaram Pandey

Received23 Nov 2021

Accepted12 Jan 2022

Published12 Feb 2022

Abstract

Objective: Synsepalum dulcificum Daniell. (SD) is a natural plant fruit and is famous for containing miraculin. It has been reported that SD can be used as an adjuvant treatment to correct patients’ loss of taste during the antitumor process, but the effect of SD itself as an antitumor is not clear. In this study, we investigated the mechanism of action of SD on lung adenocarcinoma using network pharmacology. Materials and Methods. The components of SD were identified by liquid chromatography-mass spectrometry, and then the compounds that affect tumor immunity of SD were screened and the related targets were predicted by TCMIO database. At the same time, the results were associated with lung adenocarcinoma targets included in the MalaCards and CTD databases, so as to construct a compound-target action network diagram and explore the mechanism of SD in the treatment of lung adenocarcinoma. In in vitro experiments, cell viability was determined and western blotting was used to detect the related expression of action targets to determine the therapeutic effect of SD. Results. In this experiment, 335 chemical components were identified in SD, and 107 components were related to tumor immunity. After screening by ADME, it was found that 11 compounds might be inhaled into the human body and affect the growth of lung adenocarcinoma. In vitro experiments showed that SD could inhibit the growth of lung adenocarcinoma A549 cells. SD could reduce the expression of PCNA () and significantly increase the expression of Caspase-3 (). The results of further experiments showed that SD could significantly reduce the phosphorylation of EGFR (), and SD could also effectively inhibit the expression of JAK and STAT3 phosphorylation () and inhibit the expression of PI3K and AKT phosphorylation (). Conclusion. SD can inhibit the growth of lung adenocarcinoma A549 cells and the potential mechanism was found to be the inhibition of EGFR/JAK/STAT3 and EGFR/PI3K/AKT signaling pathway, and the substance basis for SD to exert antitumor effect may be catechin, taxifolin, betaine, epigallocatechin gallate, erucamide, guanosine, kaempferol, lanosterol, morin, oleanolic acid, and quercetin.

1. Preface

Lung adenocarcinoma (LUAD) is the most common form of lung cancer, which seriously affects the quality of life of patients and is the cause of death in cancer patients worldwide [1]. Despite tremendous research efforts to develop effective diagnostic techniques and treatments, the overall survival time of LUAD patients is still very short. There is still a lack of treatment options for LUAD patients. Although chemotherapy has made great progress in the treatment of LUAD in recent years, drug resistance is an inevitable outcome, which has a nonnegligible impact on the prognosis and overall survival of patients. Studies have shown that some natural plants can directly inhibit the growth and proliferation of malignant tumors [2]. There are certain functional components of plants, such as alkaloids, dietary polyphenols, and saponins, that are the main functional components of herbs that inhibit the development of lung cancer [3, 4]. However, there is still a lack of natural antitumor plants for clinical use. Therefore, finding new plants that can fight against lung adenocarcinoma and clarifying its pharmacodynamic components is a new method for the treatment of cancer.

Synsepalum dulcificum Daniell. (SD) belongs to the mangosteen family [5] and is an evergreen shrub native to tropical West Africa, commonly known as miracle berry, miracle fruit, or miraculous fruit [6]. This is a magical plant with unique features that the sour taste felt by people can be converted into sweet taste [7]. This feature stems from its richness in a sweet glycoprotein called “miracle protein” [8]. All plant parts of SD have medicinal value. Berry fruits and leaves contain many nutrients and many beneficial properties [9, 10]. It has the ability to improve insulin sensitivity, antioxidation, and anticancer ability [11]. Therefore, it can be used as an adjuvant treatment of insulin resistance in diabetic patients [6]. As a valuable plant species, SD is currently used in cosmetics and food. In addition, it is widely used in the pharmaceutical industry [12]. This research mainly focuses on its antitumor properties. At present, there are very limited studies on the types of tumors that this plant can inhibit. Only two compounds found in SD {(+)-syringaresinol and (+)-epi-syringaresinol} have been reported to have inhibitory effects on human skin cancer cells [13]. Studies have also reported the cytotoxic activity of SD berry and stem extracts on colorectal cancer cells (HCT-116, HT-29) and their effects on apoptosis [14]. In the early days of our laboratory, we found that SD has an inhibitory effect on lung adenocarcinoma cells. This study adopts a systematic pharmacological approach to preliminarily explore the mechanism of SD hindering the growth of lung adenocarcinoma, which provides new evidence and basis for the development and utilization of SD, as well as new insights and options for the treatment and prevention of lung cancer.

2. Materials and Methods

2.1. Identification of SD Components

SD was purchased from Jiangmen, Guangdong Province, China, and was identified as a plant of the genus mysterious fruit of the Solanaceae by a researcher from Inner Mongolia Medical University. The SD samples are stored at −80°C. Methanol, acetonitrile, and formic acid used in this part of the experiment were chromatographic pure, and the other reagents were analytically pure. AB Triple TOF 5600/6600 Mass Spectrometer (AB SCIEX), Agilent 1290 Infinity LC Ultra-High Pressure Liquid Chromatograph (Agilent), and Low Temperature High Speed Centrifuge (Eppendorf 5430R) were used. Column: Waters, ACQUITY UPLC BEH Amide 1.7 μm, 2.1 mm × 100 mm column; Waters, ACQUITY UPLC HSS T3 1.8 μm, 2.1 × 100 mm column.

2.1.1. Sample Pretreatment

First of all, we took an appropriate amount of SD samples, removed the stone, and freeze-dried them into powder for accurate weighing. In order to ensure the stability of the test, the samples were divided into three kinds of samples: peeled pulp (S1), peel (S2), and unpeeled pulp (S3). Preparation of parallel quality control samples (QC): all samples were mixed in equal amounts to prepare QC samples. The QC sample is used to determine the state of the instrument and balance the chromatography-mass spectrometry system before sampling and to evaluate the stability of the system during the entire experiment. Samples were taken out at −80°C, and 80 mg samples were weighed. Then, 200 μl water was added for homogenization, followed by vortexing for 60 s, and 800 μl methanolic acetonitrile solution (1 : 1, v/v) was added, followed by vortexing for 60 s and low-temperature ultrasound for 30 min, twice. Subsequently, the samples were placed at −20°C for 1 h for protein precipitation and centrifuged at 14000 rcf, 4°C for 20 min. Finally, the supernatant was lyophilized, and the samples were stored at −80°C.

2.1.2. Analysis Condition of Liquid Chromatography-Mass Spectrometry

Chromatographic conditions: the sample was separated using Agilent 1290 Infinity LC Ultra-High Performance Liquid Chromatography (UHPLC) HILIC column: column temperature 25b°C; flow rate 0.3 mL/min; mobile phase composition A: water + 25 mM ammonium acetate + 25 mM ammonia, B: acetonitrile; the gradient elution procedure is as follows: 0–0.5 min, 95% B; 0.5–7 min, B linearly changes from 95% to 65%; 7-8 min, B linearly changes from 65% to 40%; 8-9 min, B maintained at 40%; 9-9.1 min, B changed linearly from 40% to 95%; 9.1–12 min, B maintained at 95%; samples were placed in the 4°C autosampler during the entire analysis. In order to ensure the stability of the experiment, the random sequence was adopted for continuous analysis of the samples. QC samples were inserted into the sample queue to monitor and evaluate the stability of the experimental process and the reliability of the experimental data.

2Q-TOF mass spectrometry conditions: Electrospray ionization (ESI) positive ion and negative ion modes are used for detection. The samples were separated by UHPLC and analyzed by Agilent 6550 Mass Spectrometer. The ESI source conditions are as follows: gas temperature: 250°C, drying gas: 16 L/min, nebulizer: 20 psig, sheath gas temperature: 400°C, sheath gas flow: 12 L/min, Vcap: 3000 V, and nozzle voltage: 0 V. Fragment: 175 V, mass range: 50–1200, acquisition rate: 4 Hz, and cycle time: 250 ms. After the sample was tested, the metabolites were identified by AB Triple TOF 6600 Mass Spectrometer, and the primary and secondary spectra of QC samples were collected. The ESI source conditions are as follows: ion source Gas1 (Gas1): 40, ion source Gas2 (Gas2): 80, curtain gas (CUR): 30, source temperature: 650°C, and IonSapary Voltage Floating (ISVF): ±5000 V (positive and negative two modes); the secondary mass spectrum is obtained by information dependent acquisition (IDA), and the high sensitivity mode is adopted, declustering potential (DP): ±60 V (both positive and negative modes), collision energy: 35 ± 15 eV, IDA settings are as follows: exclude isotopes within 4 Da, and candidate ions to monitor per cycle: 10. The data collection is divided into segments according to mass range, 50–300, 290–600, 590–900, and 890–1200, thereby expanding the collection rate of secondary spectra. Each method collects four repetitions per segment. The collected data were used MetDDA and LipDDA methods to identify the structure of metabolites.

2.2. Substances of SD Intervention Tumor

In order to find out the substances that affect tumor immunity in SD, we first converted the names of the compounds obtained by liquid-phase technology to the corresponding inchikey identifiers on the PubChem compound website (https://pubchem.ncbi.nlm.nih.gov/) for subsequent analysis. After that, we used the TCMIO database (http://tcmio.xielab.net/) to screen compounds related to tumor immunity in SD. Through the analysis of the results of the previous pharmacokinetic studies on the active ingredients of natural plants, it is very necessary to further adopt the ADME evaluation of the ingredients. In this study, the parameters of SD potential absorption components were set as human oral bioavailability (OB) ≥ 20% and drug similarity (DL) ≥ 0.18 [15]. Finally, we showed more detailed ADME data for the screened compounds using the SwissADME (http://www.swissadme.ch/) online tool.

2.3. Target of Predictive Screening Component

In this part, we used the method of predicting the corresponding targets of small-molecule compounds in the SwissTargetPrediction tool (http://www.swisstargetprediction.ch/) to collect the screened targets of compounds and analyzed the species to select human in the prediction process and excluded the data with the credible value of 0. When the SwissTargetPrediction tool failed to query the target data of certain compounds, we used the target data included in the TCMSP (https://www.tcmsp-e.com/) database to supplement this blank.

2.4. Targets of Lung Adenocarcinoma

For targeted information on lung adenocarcinoma, we chose to obtain it from the MalaCards database (https://www.malacards.org/). We searched the disease module of the database for genetic information about lung adenocarcinoma and screened elite genes, which means that the selection results are highly correlated with lung adenocarcinoma. In order to ensure the comprehensiveness of the data, we also merged the lung adenocarcinoma-related gene information collected in the CTD database (http://ctdbase.org/). In this database, only the genes that are directly related to the disease were selected. After integrating the relevant information on the diseases, we crossed them with the targets that SD affects and thus obtained the targets that SD may affect lung adenocarcinoma.

2.5. Protein-Protein Interaction

In order to fully understand the pharmacological mechanism of SD on lung adenocarcinoma, we performed protein-protein interaction (PPI) analysis on the target. The target names obtained in the previous step are input into the string (https://www.string-db.org/) website, and the research species are selected human. The protein interaction selection was only obtained from the experimental conditions, and no more than 20 interaction objects were selected in the expanded two-layer interaction numbers. The confidence selection was greater than 0.7, and the rest settings were set as the system default. We imported the interaction information into Cytoscape software to draw the protein-protein interaction network, used Generate Style tool to use the node size and color to reflect the degree value and the edge thickness setting to reflect the comprehensive score, and finally obtained PPI [16].

2.6. Bio-Functional Enrichment

In this part, we used the results obtained in 2.5 to perform routine biological function enrichment so that we can understand the functions and positioning of these proteins and other information. Function enrichment mainly used David online tools (https://david.ncifcrf.gov/). Enrichment items included biological process (BP), location in the cell (CC), and molecular function (MF) under the gene ontology (GO). Kyoto Encyclopedia of Genes and Genomes (KEGG) in pathway analysis was selected as pathway analysis. The analysis process conditions were set as significance , and the auxiliary screening conditions FDR < 0.01.

2.7. Bioinformatics Analysis of Targets

Differential expression and clinical significance of lung adenocarcinoma targets affected by SD are the primary basis for ensuring SD to have a clear therapeutic effect. We analyzed the details of the target gene in lung adenocarcinoma using the GEPIA 2.0 (http://gepia2.cancer-pku.cn/#index) database. First, we verified the expression differences of the genes obtained in Section 2.4 between the lung adenocarcinoma and the control group. The target gene was input into the expression module, and the analysis conditions were set as | Log 2FC |; the cutoff value was the default value of 1, and the cutoff value of value was the default value of 0.01. The tumor type was LUAD, and log 2 (TPM + 1) was used as the logarithmic scale. Then, among the targets with clear differences, we continued to search for genes related to prognosis. The genes were input into the survival analysis column of the website, and only the information of overall survival was collected. Group cutoff chose the median method, and cutoff-high (%) and cutoff-low (%) both chose 50. The risk ratio was calculated according to Cox PH model. Target expression determines whether a patient can be successfully distinguished from a control group, which is one of the important bases for targeting therapeutic targets. To achieve this, principal component analysis (PCA) was performed on genes with clear differences.

2.8. Immune Infiltration Analysis of Targets

In screening the active ingredients of SD against lung adenocarcinoma, all compounds selected from the TCMIO database were related to tumor immunity, so it was necessary to carry out immune infiltration analysis against the target. In this part of the work, data were collected using Timer 2.0 (https://cistrome.shinyapps.io/timer/) database. The differential genes obtained in the previous part were mainly input to the immune module. The types of immune cells were selected as B cells, CD8+T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells.

2.9. In Vitro Experiment Part

2.9.1. Cell Culture

Human lung adenocarcinoma cell line A549 was cultured in DMEM containing 10% fetal bovine serum and 1% antibiotics in a cell incubator at 37°C and 5% CO₂.

2.9.2. Effects of SD on Cytotoxicity

The cells cultured in advance were counted by using a blood cell counting plate. The cell suspension was diluted by a certain factor and the cell density was adjusted to 5000 cells/mL and added into a 96-well plate, which was divided into 10 drug concentration gradients, 1, 2, 4, 8, 16, 32, 64, 128, 256, and 512 μg/ml. Five wells were set for each drug concentration, as well as a control well and a zero adjustment well. After placed in a 37°C cell incubator for 24 h, drugs were added. After stimulation with drugs for 20 h, 20 μL of MTT solution was added into each well for incubation at 37°C for 4 h, and then 150 μL of dimethyl sulfoxide (DMSO) was added into each well for shaking for 10 min to fully dissolve the formazan. The average proliferation rate of cells in each group of wells was calculated and the IC50 curve was plotted using log-logistic method (effect minimum value 0 and effect maximum value 1). The preparation method of the medicine was that the SD freeze-dried powder was dissolved by DMSO. In our subsequent mechanistic studies, the dose concentration of 1/2 time the IC50, 1 time the IC50, and 2 times the IC50 was selected as the low, medium, and high dose.

2.9.3. Western Blot Analysis

The cultured cells were taken for immunoblot analysis. After lysis and centrifugation, the sample supernatant was quantified by BCA protein analysis kit. Then, protein samples were separated by 10% polyacrylamide gel electrophoresis. Protein was transferred to polyvinylidene fluoride membrane and blocked with 5% skim milk to prevent nonspecific binding and then incubated overnight with appropriate antibodies and primary antibodies of internal reference at 4°C. After culture, the cell membrane was washed with TBST and then cultured with appropriate secondary antibody labeled with peroxidase. Compared with β-actin expression, all protein blots expressed average area density. Antibodies were sourced from Abcam, specific product information is anti-EGFR (ab52894), anti-EGFR (phosphoY1068, ab40815), anti-JAK1 (ab133666), anti-JAK1 (phosphoY1022 + Y1023, ab13805), anti-STAT3 (ab68153), anti-STAT3 (phosphoY705, ab76315), anti-PI3K (ab191606), anti-PI3K (phosphoY607, ab182651), anti-AKT1 (ab179463), and anti-AKT1 (phosphoS473, ab81283).

2.10. Statistical Analysis

The experimental data were analyzed using SPSS 22.0 statistical software. All experiments were repeated at least three times. Data are presented as mean standard deviation. One-way analysis of variance was used to compare the means of multiple groups of independent samples, and the result when was considered to be statistically significant.

3. Results

3.1. Components of SD

The total ion chromatogram (TIC) obtained from the analysis of samples by UHPLC-Q-TOF MS is shown in Figure 1. Through the retention time (tR), fragmentation pattern, and sample search in the database, the results of the three samples were combined and deduplicated to obtain a total of 355 components of SD. The component information is shown in Table 1.

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3.2. Compounds of SD Intervention Tumor

After search comparison, we found 107 relevant components of SD that could interfere with tumor immunity in the TCMIO database, and the compound details are populated in Table 2. Eleven active components were obtained after screening by ADME, and the molecular weight and OB and DL parameters of these eleven compounds are shown in Table 3. The ADME values for these compounds are presented in Supplementary Table 1.

3.3. The Relationship between Compound and Lung Adenocarcinoma Targets

After summarizing the results, we obtained 255 related targets for 11 compounds. There were 23 targets of lung adenocarcinoma in MalaCards database and 159 targets in CTD database, and a total of 177 targets related to lung cancer were obtained after eliminating duplicate values. After the intersection with the targets affected by SD was completed, a total of 15 action targets were obtained. This indicates SD can affect lung adenocarcinoma through multiple targets. By using Cytoscape software to construct the network topology diagram of “chemical composition-potential action target-lung adenocarcinoma target” (Figure 2), we can more intuitively display the action target of SD. The C-T-D topology is constructed with a total of 270 nodes and 582 edges.

3.4. Result of PPI and Functional Enrichment

There were 48 targets and 101 edges in PPI network diagram (Figure 3). Among them, large spots represented key genes. The biological processes of target enrichment included ERBB2 signal pathway, epidermal growth factor receptor signal pathway, and phosphatidylinositol-mediated signal transduction. The target genes were mainly located in the cytoplasm, membrane raft, plasma membrane, etc. The molecular functions performed involve protein tyrosine kinase activity, protein phosphatase binding, epidermal growth factor receptor binding, guanylate exchange factor activity, etc. The enriched signal pathways included ErbB signal pathway, cancer pathway, proteoglycan in cancer, and neurotrophic factor signal pathway. More detailed enrichment data results are stored in Table 4. The above results reflect that lung adenocarcinoma, as a complex disease, involves many biological processes, and SD can play a therapeutic role by regulating these biological processes. Through the analysis in this part, it is found that SD in the intervention of lung adenocarcinoma not only acts on a single target but as a herb containing rich compounds that plays an anticancer role with multiple targets. Previous studies have shown that natural plants acting on multiple targets always play a good role in inhibiting lung adenocarcinoma [17, 18].

3.5. Bioinformatics Results

The results indicated that there were 6 target genes with different expression in lung adenocarcinoma, namely, MET proto-oncogene (MET), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), thymidine kinase 1 (TK1), arachidonate 5-lipoxygenase (ALOX5), arginase 1 (ARG1), and DNA topoisomerase II alpha (TOP2A) (Figure 4(a)). Compared with their respective control groups, the expressions of MET, GAPDH, TK1, and TOP2A increased (), while the expressions of ALOX5 and ARG1 decreased (). Among the survival information results, the prognosis of GAPDH with high expression was worse and statistically different from that of low expression group (), while the prognosis of high expression group of TK1 was worse (), and the prognosis of high expression group of TOP2A was poor () (Figure 4(b)). Six genes with clear differential expression were included in principal component analysis, and PCA results showed that the selection of target gene could distinguish the expression of lung adenocarcinoma from that of the control group (Figure 4(c)). Studies have found that the progression of lung adenocarcinoma is not always characterized by mutations in a single gene or protein, but when patient diagnosed with lung adenocarcinoma, it is always accompanied by changes in multiple genes, RNA, or enzymes [19]. This indicates that the target drugs designed for the characteristics of lung adenocarcinoma should target multiple targets to play a role, which is also the advantage of natural plants as drugs for the treatment of lung adenocarcinoma.

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3.6. Results of Immune Infiltration of Genes

In recent decades, more and more attention has been paid to tumor microenvironment, which includes the infiltration of immune cells. We found that patients with LUAD with high risk score had a higher proportion of activated CD4+T cells, NK cells, M0 and M1 macrophages, and activated mast cells [20]. The six hub genes selected here are all highly correlated with B cells, CD8+T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells (Figure 5), which offers potential applications for cancer immunotherapy. Remarkably, tumor-infiltrating immune cells in lung cancer may be an important determinant of prognosis and immunotherapy response [21, 22]. However, further experiments are needed to explore new biomarkers and the complex mechanisms of immune cells.

3.7. Results of In Vitro Experiments

3.7.1. Cytotoxicity Results of SD

According to the value-added rate of MTT detection, it was found that compared with the blank group, the cell viability was decreased with the increase of drug concentration. When the concentration of SD was >8 μg/ml, the cell viability was significantly decreased (). The IC50 of SD for this preparation was calculated as 22.49 μg/mL (Figure 6(a)). The concentrations in the low, medium, and high dose groups for subsequent experiments were selected as 10 μg/ml, 20 μg/ml, and 40 μg/ml.

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3.7.2. Effects of SD on A549 Proliferation and Apoptosis

The results of proliferative cell nuclear antigen (PCNA), a classical marker of cell proliferation and apoptosis, and Caspase-3 were used to explore the effect of SD on A549 cell growth rate. PCNA is an acidic nucleic acid protein and a polypeptide necessary for DNA synthesis that specifically reflects the cell proliferation state. It is an endogenous nuclear protein specifically expressed in cells at the proliferative phase. Caspase-3 is a key signal regulatory protein that promotes cancer cell apoptosis in Caspase family. The expression of both proteins can rapidly and accurately evaluate tumor proliferation and apoptosis capacity at the molecular biological level, which has important guiding value for the selection of clinical treatment plan and prognosis judgment. In order to further study the mechanism of SD affecting the growth of tumor cells, in the network pharmacology part, we have observed that the epidermal growth factor receptor signaling pathway is an important medium for SD affecting lung adenocarcinoma, and in the in vitro part, we have verified the mechanism of SD affecting the epidermal growth factor receptor-related pathways, focusing on EGFR/JAK/STAT and EGFR/PI3K/AKT.

Specifically, compared with the blank group, the proteins of the solvent control group were not significantly changed (P > 0.05). Compared with the solvent control group, the expression of PCNA was significantly decreased in the medium and high dose drug treatment groups (), and the expression of Caspase-3 was significantly increased in the medium and high dose drug treatment groups (). Moreover, compared with the control group, the phosphorylation of EGFR was significantly reduced in the medium and high dose treatment groups (), and the drug at this concentration could effectively inhibit the phosphorylation expression of JAK and STAT3 (). Finally, compared with the solvent control group, the medium and high dose medication group significantly inhibited the expression of PI3K phosphorylation (), while the low, medium, and high dose medication group significantly inhibited the expression of AKT phosphorylation ().

4. Discussion

Although lung cancer has remained as a type of cancer with a high mortality rate over the past decades, there have been significant improvements in the way lung cancer is diagnosed and treated [23]. In recent years, targeted therapeutic drugs such as epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKIs) and anaplastic lymphoma kinase (ALK) inhibitor, as well as immunotherapy drugs targeting programmed cell death protein 1 or programmed death ligand 1, have greatly improved the therapeutic effect of lung cancer and provided additional treatment options for patients with advanced and refractory lung cancer [24]. Although EGFR-TKI is very effective in the treatment of EGFR mutant lung cancer, resistance to these agents develops within an average of about 1 year [25]. Therefore, overcoming the drug resistance of EGFR-targeted related drugs remains one of the difficulties in the development of antitumor drugs. Natural plants are rich in a variety of compounds, which can well avoid the resistance of chemical drugs to a single target, which is also the main reason of traditional Chinese medicine as one of the most important complementary and alternative medicine types.

Like many advantages of natural plants, SD also affects many targets in the process of inhibiting tumor. Based on the literature, we describe the relatively important targets. MET is a tyrosine kinase receptor encoding hepatocyte growth factor, which can promote the proliferation, migration, and invasion of tumor cells by binding HGF ligand. Recent studies have found that about 3%∼5% of NSCLC patients have MET mutation, and 1%∼5% of them show MET amplification [26]. At the same time, it is found that MET gene amplification is independently related to CD8+T cell infiltration level, and the infiltration rate of CD8+T cells in NSCLC is as high as 68.9%, which indicates that MET may become a potential target for immunotherapy of NSCLC [27]. GAPDH plays a role in glycolysis and nuclear transcription, RNA transport, DNA replication, and apoptosis. Studies have pointed out that the expression of GAPDH in lung cancer, kidney cancer, breast cancer, and other tumors is out of control [28]. It has been found that the high expression of GAPDH is related to the proliferation and invasion of lung cancer and esophageal cancer [29]. In addition, research based on RNA-binding protein-related prognosis model has pointed out that the prognosis model containing GAPDH can better diagnose and predict the survival time of LUAD patients [30]. TK1 is a cytoplasmic enzyme involved in pyrimidine metabolism that catalyzes the addition of γ-phosphate groups to thymidine. TK1 has been studied as a biomarker for the diagnosis and prognosis of many types of cancer, including lung cancer [31]. In addition, the missing TK1 has been shown to inhibit the growth and metastatic ability of lung adenocarcinoma in vitro and in mice by reducing the expression of growth differentiation factors [32]. ALOX5 is a member of the family of genes encoding lipoxygenase and plays a dual role in the synthesis of leukotrienes from arachidonic acid. Mutations in the promoter region of this gene may be associated with several cancers. ALOX5 is considered to be a candidate biomarker for noninvasive molecular diagnosis of lung cancer [33]. ARG1 is a cytoplasmic enzyme that is mainly expressed in the liver. At the same time, as a part of urea cycle, it is also expressed in immune cells of peripheral blood. ARG1 metabolizes L-arginine into urea and L-ornithine and generates proline and polyamines downstream, which is crucial for cell proliferation and collagen synthesis [34]. Previous studies have revealed the fact that ARG1 is involved in anti-inflammation, tumor immunity, and immunosuppression-related diseases. The results have demonstrated that ARG1 may play a key role in the progression of hepatocellular carcinoma by promoting the EMT process. Systemic or bone marrow-specific ARG1 deletions can improve antigen-induced proliferation of adoptive transferred T cells and lead to inhibition of lung cancer tumor growth. These results suggest that ARG1, as an oncogene, may play a role in lung adenocarcinoma [35]. TOP2A is an enzyme that can change and control the DNA topological state during transcription. Abnormal expression of this enzyme has been shown to be associated with increased risk of tumor metastasis, drug resistance, and abnormal cell cycle [36, 37]. At the same time, it is considered to be the target of several anticancer drugs, such as etoposide and topotecan [38]. In addition, TOP2A is upregulated in various tumors such as colon and ovarian malignant tumors and can be used as a sensitive biomarker for early detection and treatment of these tumors [39]. In summary, we have found that the targets affected by SD play a very important role in lung cancer, which indicates that SD synergistically exerts the antilung cancer effect through multiple impact targets.

To clarify the material basis of SD acting on multiple targets, we constructed a network topology diagram of SD acting on lung adenocarcinoma and analyzed the main components that may play a role as catechin, taxifolin, betaine, epigallocatechin gallate (EGCG), erucamide, guanosine, kaempferol, lanosterol, morin, oleanolic acid, and quercetin. Previous numerous studies have shown that these substances have antitumor effects. For example, EGCG is the most abundant and bioactive catechin. Studies have shown that it can inhibit tumor proliferation and metastasis and induce lung cancer cell apoptosis in vitro and in vivo [40]. In addition, EGCG can also inhibit the growth of lung cancer cells through Ras-GTPase activating protein SH3 domain binding protein-1 [41]. At the same time, as an inhibitor of neutrophil elastase, EGCG could block the migration of A549 cells induced by neutrophil elastase by up-regulating AAT expression [42]. Furthermore, guanosine also has excellent antitumor activity and significant inhibitory activity on lung adenocarcinoma A5449 cell line [43]. Kaempferol and quercetin are the most widely studied antitumor substances; it has been reported that Kaempferol can reduce the expression of CLDN2 in A549 cells and further inhibit cell proliferation and migration [44]. It has been demonstrated that the anticancer activity of quercetin is facilitated via the activation of the adenosine monophosphate (AMP)-activated protein kinase (AMPK) pathway, suppression of the phosphoinositide 3-kinase/PI3K/AKT/mammalian target of rapamycin/NF-κB pathway, upregulation of p53 activation, and the apoptosis pathway, and when used with other chemotherapeutic agents, quercetin can achieve tumor-improving results by enhancing apoptosis and reducing side effects [45]. Based on the research results of this experiment and literature reports, we believe that SD has the effect of multiple natural compounds affecting multiple targets to play an antilung adenocarcinoma role.

At the same time, in the part of network pharmacology, we found that an important signaling pathway for the intervention of SD on lung adenocarcinoma was EGFR signaling pathway. In the experimental part, we focused on the mechanism of SD affecting the proliferation and apoptosis of A549 cells through EGFR. EGFR is a receptor tyrosine kinase that transduces a signal cascade across that plasma membrane (from the extracellular environment to the intracellular environment). EGFR is highly expressed in a variety of malignant tumors, and its receptor dimerization can activate JAK1 and STAT, thereby regulating the cell cycle and apoptosis of lung cancer cells [46]. The continuous activation of STAT3 is closely related to malignant transformation of cells and participates in the occurrence and development of various tumors. Studies have found that constitutive activation of STAT3 can be detected in lung cancer, so it is considered to be closely related to the occurrence and development of lung cancer. Subsequent studies have further revealed abnormal activation of STAT3 in approximately 55% of NSCLC patients and in most NSCLC cell lines. This activation is more common in patients with small tumors, patients with a short history of smoking, and patients with lung adenocarcinoma [47]. In addition, the phosphorylation of EGFR activates phosphatidylinositol 3-kinase (PI3K), which activates downstream signaling molecules in the pathway and promotes the proliferation, infiltration, and metastasis of tumor cells [48]. Thus, effective control of the expression of JAK/STAT3 and PI3K/AKT by EGFR contributes to the regulation of the growth and apoptotic state of tumor cells. We have found in the experiment that SD can effectively inhibit the growth of lung adenocarcinoma cells from the point of view of the effect of SD on the proliferation of A549 cells, and we have also found that SD can inhibit the expression of PCNA and increase the expression of Caspase-3. We further found that SD significantly inhibited the phosphorylated expression of EGFR, which in turn inhibited the phosphorylated expression of JAK/STAT and PI3K/AKT. This indicated that SD had the effect of inhibiting the growth of lung adenocarcinoma cells, and the mechanism might be related to the inhibition of EGFR pathway.

5. Conclusion

In the whole experiment, we used different technical means to identify the medicinal value of SD as a natural plant against lung adenocarcinoma. First of all, we identified the chemical constituents of SD by liquid chromatography-mass spectrometry and related the compound information with the medicinal materials database and then mined out the key compounds in SD that interfered with tumor. It is found that the main substances that exert the efficacy of SD are catechin, taxifolin, betaine, epigallocatechin gallate, erucamide, guanosine, kaempferol, lanosterol, morin, oleanolic acid, and quercetin. SD containing the main components acts through different targets, among which the more important targets are MET, GAPDH, TK1, ALOX5, ARG1, and TOP2A. We also found that SD affected various signaling pathways to play a pharmacological role against the growth of lung adenocarcinoma cells, including ERBB2 signaling pathway, epidermal growth factor receptor signaling pathway, and phosphatidylinositol-mediated signaling. Finally, the in vitro experiments showed that SD could block the EGFR/JAK/STAT and EGFR/PI3K/AKT signaling pathways to different degrees to inhibit the proliferation of lung adenocarcinoma A549 cells and increase apoptosis. In summary, SD has a significant anticancer effect on lung adenocarcinoma A549 cells. Next, whether SD exerts the same antitumor effect in vivo is the direction of our main experiment.

Data Availability

Data used to support this study are available on request from the corresponding author.

Disclosure

Qi Chen and Tingting Liu are the co-authors of the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Qi Chen and Tingting Liu have contributed equally to this work and share first authorship. Qi Chen and Tingting Liu did experimental operation; Tuya Bai and Tingting Liu collected and analyzed the data; Qi Chen and Yuxia Hu drafted the thesis ; Tingting Liu, Mengdi Zhang, and Fuhou Chang put forward the research ideas and designed the research scheme; Qi Chen and Jun Li revised the final edition of the paper.

Acknowledgments

This research was supported by the Innovation and Entrepreneurship Training Program for College Students in Inner Mongolia Autonomous Region (202010132012), National Natural Science Foundation of China (81760676), and Natural Science Foundation of Inner Mongolia Autonomous Region (2019LH08017).

Supplementary Materials

Supplementary schedule 1 mainly stores ADME data related to compounds screened in SD. (Supplementary Materials)

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