Molecular Mechanism of Gleditsiae Spina for the Treatment of High-grade Serous Ovarian Cancer Based on Network Pharmacology and Experimental Verication

In this study, we aimed to analyze the pharmacological mechanism of Gleditsiae Spina in the treatment of high-grade serous ovarian cancer (HGSC) based on network pharmacology and in vitro experiments. The main active ingredients of Gleditsiae Spina were identied by high performance liquid chromatography and mass spectrometry, and ADME screening was performed. The component targets of Gleditsiae Spina were screened using the pharmMapper platform, and differentially expressed genes in normal and HGSC tissues were identied through GEO database. Thereafter, Cytoscape 3.7.2 software was used to construct the network of "active ingredient-targets," and the BioGenet database was used for protein-protein interaction analysis. Furthermore, the protein-protein interaction network was established, and the potential protein function module was mined. Biological processes and pathways were analyzed through gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis. prediction results by molecular docking, molecular dynamics simulation, and western blot analysis.

of the differential genes of HGSC and normal patients, where gene targets of Gleditsiae Spina, and the predicted Protein-Protein Interaction (PPI) network were intersected. The data mining method was used to obtain the candidate genes that Gleditsiae Spina acts on in HGSC. PPI network diagrams, Gene ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathways and gene-signal pathway network diagrams were used to systematically predict and explore the potential active ingredients and key therapeutic targets of Gleditsiae Spina interference in HGSC.

Mass Spectrometry Analysis of Gleditsiae Spina
Production of Freeze-dried Powder of the Gleditsiae Spina Gleditsiae Spina, mainly used as medicine, is obtained from the dry spines of the leguminous Gleditsia sinensis Lam. In this study, Gleditsiae Spina was provided by Beijing Xinglin Pharmaceutical Co. (Beijing, China), and identi ed by Professor Mao Kechen according to the Chinese Pharmacopoeia standards.
Gleditsiae Spina pieces (200 g) were placed in a vessel and cooked twice for 1 h each time. The two resulting decoctions were mixed to obtained a volume of 450 mL which was transferred to a vacuum freeze dryer (Epsilon 2-4LSC; Martin Christ Gefriertrocknungsanlagen, Harz, Germany) to obtain 18.72 g of freeze-dried powder, with a production rate of 8.28%. The powder was placed into a closed container containing silica gel to keep it dry. The powder was dissolved in deionized water to prepare a 100 mg/mL solution and stored at −20 °C for subsequent experiments.

Identi cation of the Gleditsiae Spina Components Using High Performance Liquid Chromatography and Mass Spectrometry
Gleditsiae Spina freeze-dried powder (100 mg) was dissolved in 50% methanol and sonicated for 10 min.
The solution was centrifuged at 3,000 r/min for 5 min (Beckman Coulter, Brea, CA, USA), the supernatant was collected and ltered through a 0.22 μm lter (Millipore, Burlington, MA, USA). The ltered solution was subjected to mass spectrometry analysis using a Q-Exactive Orbitrap quadrupole-electrostatic eld orbitrap mass spectrometer equipped with a thermal spray ion source and a Vanquish ultra-high performance liquid chromatography system (Thermo Fisher Scienti c, Waltham, MA, USA).

Target Prediction Prediction of Active Components in Gleditsiae Spina
Based on the mass spectrometry results of Gleditsiae Spina, the active ingredients were initially selected.
The molecular structure of the active pharmaceutical ingredients and 3Dsdf were obtained on the Pubchem platform. The 3Dsdf les of the active ingredients were uploaded to the SwissADME platform, and the pharmacokinetics and drug similarity were screened. The compound ingredients were required to have good gastrointestinal absorption, and the drug similarity received more than 3 positive evaluations.
The potential targets of the ingredients were predicted on the PharmMapper platform (19).We required the norm t value to be greater than 0.75

Construction of the Active Ingredient-Target Network of the Gleditsiae Spina
The "active ingredient-target" network of the Gleditsiae Spina was constructed and analyzed by Cytoscape 3.7.2 software (20). "node" was used to indicate the component or target, and "edge" was used to indicate the relationship among them. The "Network Analyzer" analyzing tool built in Cytoscape 3.7.2 software was used to analyze the network characteristics, including Degree, Betweeness, and Closeness, to study important components and target relationships of Gleditsiae Spina.

Construction of protein interaction network and screening of key targets
The PPI were constructed by BisoGenet3.0 (21). The targets related to the active ingredients of Gleditsiae Spina, and the targets of disease were introduced into BisoGenet, each generated a PPI network. The intersection network of the two PPI networks was extracted through the Merge function in Cytoscape, and CytoNCA2.1 (22) was used to analyse the nodes of the intersection network. The targets were mapped and visualized by Cytoscape 3.7.2 and the protein-protein internetwork (PPI) of the shared genes were constructed through the String APP in Cytoscape.

Pathway enrichment analysis
The Metascape platform (23) was used to perform pathway enrichment analysis on the target. The platform integrates many authoritative functional databases such as GO, KEGG, etc., and supports batch genes or annotates, enriches and analyses proteins and builds PPI networks. The platform is updated once a month to ensure data is reliable. Imported potential ovarian cancer targets were inserted into the Cytoscape platform for GO and KEGG analysis, the results were saved and visualized with R software3.6.1.

Molecular Docking and Molecular Dynamics Simulation
In order to further determine the credibility of the relationship between the ovarian cancer target and the core components of Gleditsiae Spina, the top two compounds of traditional Chinese medicine-compoundtarget were selected as ligands genistein and luteolin, and four important targets were selected to analyse molecularly docking.
First, the crystal structure of the three proteins in pdb format from the RCSB database was downloaded, and the SDF from the PubChem database. We also downloaded the 3D chemical structure of the candidate compound and used Open Babel 2.4 to convert to the pdb format le. AutoDock Tools 1.5.6 was used to delete the water molecules in the ligand, separate the ligand from the receptor, add non-polar hydrogen, calculate the Gasteiger charge, and save the pdbqt format le. The selected potential core constituent ligands were subjected to energy minimization treatment, the ligand atom type was given, the charge was calculated, and stored in pdbqt format. Molecular docking operations were performed using Autodock Vina 1.1.2, and re ect the matching degree and docking activity between the target and the ligand through the docking score value, where we believe that a docking score > 4.25 means that the ligand and the target have binding activity, a score > 5.0 means good matching activity, and a score > 7.0 means strong docking activity (24).
The MD simulation of docked complexes were carried out using Desmond version 2020. Here, OPLS3e force eld was used to initiate the MD simulation, and the system was solvated using TIP3 water model.
The neutralization of the system was performed by adding counter ions. Energy minimization of the entire system was performed using OPLS3e, as it is an all-atom type force eld. The geometry of water molecules, the bond lengths and the bond angles of heavy atoms was restrained using the SHAKE algorithm. Simulation of the continuous system was executed by applying periodic boundary conditions and long-range electrostatics was maintained by the particle mesh Ewald method. The equilibration of the system was done using NPT ensemble with temperature at 300 k and pressure at 1.0 bar. The coupling of temperature-pressure parameters was done using the Berendsen coupling algorithm. On postpreparation of the system, the production run was performed for 200 ns with a time step of 1.2 fs and trajectory recording was done for every 200 ps summing up to the recording of 10,00 frames. The calculation of the RMSD (Root mean square deviation) was done for the backbone atoms and was analyzed graphically to understand the nature of protein-ligand interactions. RMSF (Root Mean Square Fluctuation) for every residue was calculated to understand the major conformational changes in the residues in comparison between the initial state and dynamics state.

Cells and cell proliferation
Human ovarian cancer cell line A2780 purchased from ATCC was used in this study. A2780 cells were cultured in RPMI-1640 (Gibco Company, USA) with 10% foetal bovine serum (Gibco Company, USA) and 1% penicillin-streptomycin (Gibco; 10000 units/mL Penicillin, 10000 μg/mL Streptomycin) and cultured at 37°C in a humidi ed atmosphere under 5% CO2. Cells used in this experiment were in the exponential growth phase.
For proliferation tests, The MTT was used to evaluate cell proliferation.3000 cells were seeded in each of the non-edge well of 96-well plates. Freeze-dried powder of the Gleditsiae Spina was added after the cells adhered to the wall. The freeze-dried powder of maximum freezing 20 mg/mL degree was diluted into the cells by multiple dilutions. After 24 hours, 20 L of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) (Sigma, St. Louis, MO, USA) solution (5 mg/mL in phosphate-buffered saline (PBS)) was added to the culture medium in each well at a nal concentration of 0.5 mg/mL and the cells were incubated at 37 °C for 4 hours. The supernatants were replaced with 150 L of dimethyl sulfoxide (Sigma, St. Louis, MO, USA). Then the 96-well plates were measured by Microplate reader at 490 nm.

Western blot
After exposure to the test compounds (1.25mg/mL, 2.5 mg/mL Freeze-dried powder of the Gleditsiae Spina) for 24 h, the A2780 cells were harvested and lysed with RIPA lysis buffer (Beyotime Biotechnology, Beijing, China) containing Protease Inhibitor Cocktail Set III (Calbiochem, San Diego, CA, USA). A2780 cell lysates were centrifuged at 12,000 rpm for 15 min at 4 °C, and the supernatants were collected. The protein concentration was determined using the Pierce BCA Assay Kit (Thermo Scienti c, Rockford, IL, USA). Equal amounts (30 μg/lane) of total protein were separated by 10% SDS-PAGE and transferred onto PVDF membranes. The membranes were blocked with 5% BSA (Amresco, Solon, OH, USA) at room temperature for 1 h and incubated overnight at 4 °C with the following primary antibodies . After washing the membranes in Tris-buffered saline with 0.1% Tween-20 (TBST), the membranes were probed with secondary antibodies (1:10,000) for 1 h at room temperature. The signals were detected using an Odyssey Infrared Imaging System (Li-cor Biosciences, Lincoln, NE, USA). The relative density of the protein bands was measured by Odyssey version 3.0 software (LI-COR Biosciences). Each experiments were repeated three times. The ratios of the protein band intensities relative to that of β-Actin were calculated for each sample using Image J.

Mass spectrometry results
The extract of Gleditsiae Spina was analyzed according to the method in 2.1, and the total ion current diagram under the positive and negative ion mode was obtained ( Figure 1). According to the retention time of each chemical component, high-resolution precise molecular weight, MSn multi-level fragment information obtained by LC-MS detection, combined with the extracted ion current map and standard product information, Mzcloud database and related literature, the composition was con rmed, and a total of 39 were identi ed. (See Table 1 for the results). Among them, the number identi ed in the positive ion mode is 30, the number identi ed in the negative ion mode is 18, and the 9 compounds are the compounds identi ed by the positive and negative ions.

Network Construction and Target Prediction
Active Ingredients and Targets of Gleditsiae Spina Thirtynine Gleditsiae Spina components were observed in 3.1, screened according to the pharmacokinetics and drug similarity in SwissADME platform, and supplemented according to previous literature reports. This resulted in 26 active components. The SDF structure of 26 selected components were obtained from Pubchem, and the PharmMapper target prediction model was used to predict the above 26 targets of active ingredients. The 26 medicinal active ingredients are shown by in Table 2.
Relevant target prediction technology was used to predict the active targets, eliminating duplicate targets, and a total of 610 predicted targets were obtained, as shown in Figure 2.
Construction and Analysis of "Active Ingredient-Target" Network Cytoscape 3.7.2 was used to draw and analyse the relationship network between the effective components of Gleditsiae Spina and its active targets, a total of 635 nodes (including 609 targets and 26 active components) and 1090 relationships were obtained. The size represented the corresponding Degree value. The larger the node area was, the larger the Degree value, indicating that the more biological functions involved, the higher its biological importance ( Figure 2).

Ovarian Cancer-Related Target Searching
Ovarian cancer-related targets (1483) were identi ed from the Gene Expression Omnibus (GEO) database. Figure 3 shows heat maps and volcano maps indicating the distribution of differentially expressed genes, which are represented by red dots on the map.

Screening of Key Targets for Gleditsiae Spina Treatment in Ovarian Cancer
To obtain richer node-node connection information in PPI networks, the e ciency of node information transmission was optimized, the targets that play an important role in the network were identi ed, and the network topology characteristic attributed values of the above-mentioned intersection PPI network graph was calculated. Through two screenings, a total of 87 key targets were obtained. The targets were shown in Figure 4-A and the PPI network of the key genes was shown in Figure 4-B.

Pathway Enrichment Analysis and Visualization of Gleditsiae Spina Treatment for Ovarian Cancer
Metascape platform was used to perform gene enrichment analysis on the above 87 key nodes, including GO-BP (Biological Process), GO-CC (Cellular Component), GO-MF (Molecular Function) and KEGG pathway. R (version 6.1) was used to draw KEGG pathway a bubble chart (shown in Figure 5). The bubble colour changed from red to purple to indicate that the log P value is from small to large. The smaller the log10(P) value, the stronger the signi cance, and the larger the bubble, the larger the gene count (Count value) of the pathway.

Molecular docking and Molecular Dynamics Simulation results
The three core potential compounds luteolin and genistein were molecularly docked with the four core targets PIK3CA, CTNNB1, HPSE and AKT1 to obtain the group receptor-ligand docking results. Among the nine groups, the highest docking score is for luteolin-HPSE (-8.97 kcal/mol), and the lowest docking score is for genistein-HPSE (-7.35 kcal/mol). This indicates that the selected potential core compounds have better binding activity with the target. The diagram depicting eight docking modes is shown in Figure 6. It can be seen from the gure that each ligand is embedded in the active pocket of the target and that it interacts with multiple residues of the target through hydrophobic interactions and hydrogen bond formation.
Next Genistein, Luteolin, and Berberine as the core components in the network were selected to perform molecular dynamics simulation tests with AKT1, HPSE, and PI3K. The binding of Genistein and Luteolin to the ligand quickly stabilized and continued to work and after a short period of uctuation, the binding of Berberine to the ligand also forms a stable state. The results were shown in Figure 7.

Veri cation of Therapeutic Effects of Gleditsiae Spina in Ovarian Cancer
Next, we conducted in vitro experiments to verify the therapeutic potential of Gleditsiae Spina. The MTT assay was performed to evaluate the viability of A2780 cancer cells treated with increasing concentrations of Gleditsiae Spina solution (156.25-20,000 μg/mL) for 24 and 48 h. The cell viability decreased with the increase of the Gleditsiae Spina concentration. To further explore the therapeutic mechanisms of Gleditsiae Spina in combination with the above-described KEGG analysis data, HGSC cells were stimulated with 1.25 and 2.5 mg/mL Gleditsiae Spina solution for 24 h, followed by protein extraction and analysis by western blotting, with heparinase used as Core ( Figure 8B-D). Our results showed that the levels of heparinase 1, MMP9, β-catenin, and N-cadherin were signi cantly downregulated in ovarian cancer cells in response to Gleditsiae Spina treatment in a dose-dependent manner. However, the level of E-cadherin increased in the high-dose group, although the increase was not statistically signi cant. Furthermore, treatment with Gleditsiae Spina inhibited the expression of proteins associated with the PI3K/AKT/mTOR pathway; however, expression of these molecules increased at higher doses of Gleditsiae Spina. This may be attributed to the interaction between the complex components of traditional Chinese medicine and the cells, which lead to programmed cell death. At high Gleditsiae Spina concentrations, only a small number of cells survived for more than 48 h. Therefore, although the autocompensation mechanism of cells can be ruled out, the speci c mechanism in vivo needs to be further explored.

Discussion
Traditional Chinese medicine has a long history and has nurtured the Chinese nation for millennia. It protects human health through dialectical theory. Traditional Chinese medicine works against diseases through a single or compound prescription by multiple ingredients and targets. The cause of ovarian cancer is considered to be the accumulation of pathological products in TCM theory, and Gleditsiae Spina has the potential to clear these toxic metabolites. It is a summary of experience obtained in clinical practice of TCM and has obvious clinical effects based on clinical experience. The concept of network pharmacology and the theory of TCM have similar aspects, which can explore the unknown mechanism of action of Chinese herbal medicine from the perspective of overall composition and function (25).
Based on the mass spectrometry results, along with previously reported data, we inferred that luteolin, genistein, D-(+)-tryptophan, ursolic acid, and berberine in the Gleditsiae Spina play a core role in the treatment of ovarian cancer. It was previously reported that D-(+)-tryptophan exhibits anticancer activity, which can stimulate mTORC1 and enhance the activity of T-cells within the tumor microenvironment (26). Luteolin is a avonoid compound that inhibits tumor cell proliferation, blocks cell cycle, and reverses tumor epithelial-mesenchymal transition (27). Luteolin downregulates the expression of aromatase, and consequently inhibits estrogen synthesis in ovarian cancer (28). Genistein can modulate the cell cycle and regulate the ERK1/2, NF-κB, Wnt, β-catenin, and PI3K/Akt signaling pathways to exert its anticancer effects. Moreover, it can synergize with paclitaxel and other ovarian cancer drugs (29). Ursolic acid can downregulate the expression of YAP1 of the hippo pathway in tumor treatment (30). Studies have shown that berberine can induce apoptosis in various tumor cell lines. By inhibiting the transcriptional activity of β-catenin, berberine modulates the Wnt signaling pathway (31), and increase the expression of caspase-3 and -8; thus, it promotes apoptosis of ovarian cancer cells, when used in combination with cisplatin (32).
The KEGG results showed that Gleditsiae Spina affects ovarian cancer development via multiple pathways and thus plays a therapeutic role. The expression levels of heparinase 1, MMP9, β-catenin, Ncadherin, PI3K/AKT/mTOR, as well as their phosphorylation levels, which are involved in tumor progression, were reduced after treatment with Gleditsiae Spina.
Proteoglycans are widely distributed on the cell surface and in the cytoplasmic matrix; moreover, they play an important role in tumor development. Their glycosaminoglycan chains are modulated by heparinase, the only endoglycosidase in mammals; hence, heparinase plays an important role in regulating the function of proteoglycans (33). Several studies have found that the expression of heparinase is positively correlated with malignancy, with heparinase overexpressing tumors being associated with a worse prognosis compared with tumors in which heparinase is underexpressed (34).The extracellular matrix is mainly composed of keratansulfate proteoglycans, chondroitin sulfate proteoglycans, and dermatan sulfate proteoglycans (35); therefore, heparinase can accelerate the remodeling of tumor extracellular matrix and basement membrane (36) and contribute to tumor.
Moreover, the cleaved heparan sulfate proteoglycans can bind to growth factors and increase the expression of VEGF by promoting p38 phosphorylation and Src kinase activity (37), thereby promoting angiogenesis and accelerating tumor metastasis and invasion (38). Our in vitro experiments showed that Gleditsiae Spina can inhibit the tumor growth and downregulate heparinase expression in tumor cells. By reducing the expression of heparinase, Gleditsiae Spina can regulate signaling cascades within the tumors (39).
MMP9 plays an important role in tumor extracellular matrix remodeling and communication between tumor cells (40). MMP9 is considered a potential biomarker for tumors, including ovarian cancer (41). Our western blotting results showed that Gleditsiae Spina signi cantly inhibits the expression of MMP9 to reduce tumor cell activity. E-cadherin is a calcium ion-dependent transmembrane protein closely related to cell adhesion. Adjacent cells interact via an extracellular domain of E-cadherin, which is connected to βcatenin and the cell cytoskeleton. Thus, E-cadherin expression is negatively correlated with the degree of tumor invasion (42). Numerous studies have shown that the conversion of E-cadherin to N-cadherin often indicates the completion of the epithelial-mesenchymal transition process. Herein, although the expression of E-cadherin was low and changes in its expression could not be measured, the expression of N-cadherin was inhibited after treatment with Gleditsiae Spina, indicating that Gleditsiae Spina can reverse the expression of the two proteins, thereby inhibiting tumor development. β-catenin is a cytoplasmic protein, and its nuclear expression plays a role in activating transcription factors. Abnormal expression of β-catenin is related to the hippo and HIF1 signaling pathways, and it can also activate the Wnt signaling pathway (43). Moreover, its abnormal expression is closely related to colon cancer (44) and cervical cancer. Our western blotting results showed that Gleditsiae Spina inhibited the expression of βcatenin protein, which were consistent with the prediction results of the network pharmacology.
The PI3K/AKT/mTOR signaling pathway plays key role in cancer, as is related to cell proliferation, angiogenesis, chemotherapy resistance, and several other pathological conditions. Its downstream molecule mTOR can accelerate the formation of tumor stem cells (45), leading to tumor progression and relapse. Overexpression of PI3K can lead to RAS mutations, loss of PTEN, and is associated with HIF1, hippo, and MAPK signaling pathways. Moreover, it activates the epidermal growth factor receptor, stimulates the expression of the vascular endothelial growth factor, and accelerates angiogenesis (46). Approximately 70% of ovarian cancer patients present with overexpressed PI3K/AKT/mTOR cascade (47); thus, using inhibitors to target this pathway is an important strategy to treat ovarian cancer. In our study, Gleditsiae Spina signi cantly inhibited the expression of PI3K and AKT; low-dose treatment with Gleditsiae Spina inhibited the phosphorylation of AKT and mTOR, whereas the remarkably increased phosphorylation in the high-dose-treated group suggested its own S6K1-IRS1 negative feedback. Additional experiments are warranted to elucidate the underlying mechanism of the regulatory effect exerted by Gleditsiae Spina.
The molecular docking experiments can predict the binding ability of the ligand and the target at the molecular level. Using this approach, we determined that several components of Gleditsiae Spina have high binding ability to the ovarian cancer targets. Gleditsiae Spina can interfere with the activities of heparinase 1, β-catenin, PI3K, and AKT at the molecular level, thereby proving that Gleditsiae Spina can exert signi cant therapeutic effects on ovarian cancer. Molecular dynamics simulations, which can monitor time-resolved motions of molecules (48), further showed that the ingredients of Gleditsiae Spina stably interact with the disease-speci c molecules in ovarian cancer, which indirectly provides a basis for verifying its therapeutic e cacy.
Cancer is a complex systemic disease characterized by complex, mutual feedback mechanisms of multiple pathways. This explains why many patients fail to respond to treatments targeting a single molecule. Traditional Chinese medicine is also a complex system. The interaction of complex components of traditional Chinese medicine, as well as the interactions between traditional Chinese medicine components and the human body may be useful in treating diseases including cancers. Nevertheless, these interactions and their target mechanisms remain to be elucidated in vivo.

Conclusions
In this study, we adopted a network pharmacology approach to explore the mechanisms underlying the therapeutic effects of Gleditsiae Spina on HGSC. Our data demonstrates that Gleditsiae Spina can modulate the cytoplasmic matrix components and structure. Moreover, luteolin, genistein, D-(+)tryptophan, ursolic acid, and berberine were identi ed among its main pharmacological components. Overall, Gleditsiae Spina was found to modulate various biological processes and pathways, including cell cycle, apoptosis, ubiquitin-dependent protein metabolism, viral infection and carcinogenesis, chromatin, transcription factor binding protein structure, and binding ability. Herein, HPSE, PI3KCA, AKT1, and CTNNB1 were identi ed as the key target genes involved in therapeutic outcomes of ovarian cancer, which needs further exploration and validation in future studies. Our study outcomes provide new insights for designing new experimental models and strategies to validate the therapeutic targets in ovarian cancer proposed in this study. Furthermore, detailed analysis of the molecular mechanism involved in Gleditsiae Spina-mediated therapeutic effects on ovarian is warranted.

Declarations
Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable.
Competing interests: The authors declare that the research was conducted in the absence of any commercial or nancial relationships that could be construed as a potential con ict of interest. Authors' contributions: BZ and WD performed the main analysis and drafted the manuscript. BZ and WD designed the research and carried out the introduction and discussion. XC helped to complete the experimental veri cation, XY helped to nish the image processing. GL and TM assisted in the preparation of the manuscript. GZ, CD and XW revised the manuscript. All authors wrote, read, and approved the manuscript.     The key targets of the Gleditsiae Spina treating for Ovarian Cancer(A) and the PPI network(B).    The cellular veri cation results. A: Dose-dependent inhibitory effect of Gleditsiae Spina on ovarian cancer cell proliferation. B and C: After exposure to the Gleditsiae Spina for 24 h, the total protein from each cell lysate was analyzed by Western blotting to measure the expression of proteins. D: Data are presented as the mean ± SD and normalized to β-actin (* P < 0.05, ** P < 0.01, *** P < 0.001).