Study on the mechanism of Euscaphic acid in the treatment of non-Hodgkin lymphoma based on network pharmacology and molecular docking

doi:10.21203/rs.3.rs-3926002/v1

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Study on the mechanism of Euscaphic acid in the treatment of non-Hodgkin lymphoma based on network pharmacology and molecular docking

https://doi.org/10.21203/rs.3.rs-3926002/v1

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Non-Hodgkin's lymphoma (NHL) is a highly heterogenous disease. 5-year survival duration after diagnosis is poor among patients with aggressive/relapsing form of NHL. Our previous research found for the first time that Euscaphic acid (EA) has anti-tumor effects in NHL. However, the underlying mechanism by which EA plays a role in NHL remains unclear. In this study, we used network pharmacology and molecular docking to investigate the target and mechanism of the pharmacological action of EA on NHL. The EA-related targets and NHL-related targets were collected from the public database and overlapped to obtain the potential targets of EA-related anti-NHL. Target interaction was analyzed using STRING database, and 10 core target genes (TNF, PPARG, MMP9, HSP90AA1, PTGS2, IGF1R, AR, ESR2, NR3C1, MMP2) was screened by Cytoscape software. In the GO enrichment analysis and KEGG pathway analysis, TNF, PTGS2, PPARG and MMP9 are mainly enriched in the IL-17 signaling pathway, PPAR signaling pathway. The molecular docking results show there was strong interaction between the top 10 core targets and the EA. In addition, we found that EA inhibited the proliferation of RAJI NHL cells and induced cell apoptosis. These results suggested that EA may act on TNF, PTGS2, PPARG, and MMP9 through the IL-17 and PPAR signaling pathways, thereby exerting anti-NHL effects.

Euscaphic acid

Non-Hodgkin lymphoma

Network pharmacology

Molecular docking

Apoptosis

Non-Hodgkin lymphoma (NHL) is the most common hematologic malignancy in the world, accounting for nearly 3% of cancer diagnoses and deaths (Armitage et al. 2017; Thandra et al. 2021). Currently, the incidence of NHL is increasing year by year, and the age of these patients trends to become young (Shen et al. 2023). NHL is a highly heterogenous disease, which can emerge in either aggressive or indolent form. 5-year survival duration after diagnosis is poor among patients with aggressive/relapsing form of NHL (Thandra et al. 2021). In the recent few decades, several treatment solutions of NHL mainly based on targeted therapies, including B-cell receptor signaling inhibitors, immunomodulatory agents, monoclonal antibodies, epigenetic modulators, Bcl-2 inhibitors, checkpoint inhibitors, and CAR-T cell therapy, have brought the hope of cure to patients (Marofi et al. 2021). Despite immense progress, drug resistance and the resulting relapsed or refractory disease remain the biggest challenges at present (Klener and Klanova. 2020).

Euscaphic acid (EA) is a natural triterpenoid saponin, which was first found in the peel of E.japonica (Takahashi et al. 1974). The EA has anti-HIV (Xu et al. 1996), anti-inflammatory (Banno et al. 2005; Kim et al. 2012) and anti-tumor effects (Numata et al. 1989; Cheng et al. 2010; Zhang et al. 2012), but almost no cytotoxicity on non-tumor cells (Kim et al. 2012). Our previous research found for the first time that EA has anti-tumor effects in NHL(Li et al. 2022). However, the specific mechanism by which EA plays a role in NHL remains unclear. Network pharmacology can analyze the chemical structure and action targets of drugs, and carry out systematic network analysis of the relevant targets of drugs and diseases, so as to predict the key targets and molecular mechanisms of drug treatment of diseases (Zhao et al. 2023). In this study, we analyzed the targets and underlying mechanisms of EA in the treatment of NHL through network pharmacology and molecular docking (Fig. 1). This study will provide a theoretical basis for the clinical research and application of EA in the treatment of NHL.

Network pharmacology analysis

EA and NHL-related target acquisition

Obtain the Canonical SMILES of EA using the PudChem database (https:// www.pubchem.ncbi.nlm.nih.gov/) and import the SwissTargetPrediction database (https://www.SwissTargetPrediction.cn) and pharmmapper database (https://lilab-ecust.cn/pharmmapper/index.html) screening EA-related targets. UniProt (http://www. uniprot.org) was used to obtain the names and IDs of the predicted target.

Human targets were searched in the Genecard database (https:// www.GeneCards.org) and OMIM database (https://www.OMIM.org) using the keywords "Non-Hodgkin lymphoma" and "Homo sapiens". Deduplication is then performed in both databases.

Construct EA-NHL common target and PPI Network.

Venny software was used to map the intersection of EA-related targets and NHL-related targets, and the common targets obtained were potential targets for EA treatment of NHL. The selected common targets were imported into String database (https:// www.cn.string-db.org), the species was selected as "homo sapiens", the minimum interaction threshold was set to medium confidence of 0.4, and the Protein-protein interactions (PPI) network. In the PPI network, the nodes represent the target proteins, and the lines between proteins represent the interaction relationship between targets, and the more lines indicate the greater correlation degree. The top 20 target proteins were counted and a bar chart was drawn. In CytoScape's Cytohubba plug-in, MCC algorithm was used to calculate the PPI network hub gene of EA treatment of NHL, and the related protein target network was constructed. Finally, the core objectives of the top 10 were shown.

GO and KEGG pathway enrichment analysis of potential target genes

These core targets were subjected to enrichment analysis of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) using the DAVID database (https://www.DAVID.ncifcrf.gov). Bubble maps of enrichment results were drawn using bioinfor‑matics sofeware. The method was to input the "Gene name" of the relevant intersection target, and the species was selected as "Homo sapiens". Under the condition of P≤0.05, Weishengxin online plotting website (http://www. bioinfor‑matics.com.cn/). The bioprocess, Cell Component and Molecular Function analysis of GO and the bubble map of KEGG signal pathway were drawn from the enrichment results.

Molecular docking

In order to verify the reliability of the interaction between key targets and compounds, this study selected the top ten key targets and compounds for molecular docking. Download the crystal structure of the target from the PDB database (http:// www.rcsb.org/pdb), and from the PubChem database (https://pubchem.Ncbi.While NLM.Nih.Gov/) download compound 3d structure, the use of CB-DOCK2 server (https://cadd.labshare.cn/cb-dock2/) for molecular docking to determine the binding affinity between the EA and the receptor protein, and the affinity value was used to measure the confidence of interaction between the intersection target and the compound. By default, the CB-DOCK2 server verifies the subject protein structure of missing hydrogen atoms and side chain atoms. Similarly, the submitted ligands are improved by adding hydrogen atoms and partial charges. In addition, by estimating the center size and size of the top N (default N¼5) cavities, the binding site of the selected protein is predicted based on the curvature based on cavity detection method.

Bioinformatics analysis

In TCGA-DLBCL, the online tool GEPIA (http://gepia2.cancer-pku.cn/index.html) was used to assess mRNA expression levels, pathological stages, and overall survival at core targets.

Experimental validation

Materials

Euscaphic acid (No.Y-176) was purchased from Herbpurify, Chengdu. Annexin V-FITC/PI kit (No.70110100) was obtained from Biosharp, Shanghai. CCK8 was purchased from Meilun, Dalian (No. MA0218-2-Sep-08F).

Cell culture

RAJI cells were purchased from Procell CL-0189.The cells were cultured in 1640 culture medium with 10% fetal bovine serum, 100 μg/mL penicillin, and 100 μg/mL streptomycin and placed at 37 °C in an incubator with 5% CO₂.

Cell proliferation assays

Cell proliferation was detected with cell Counting Kit-8 according to instruction. 2×10⁴ cells/well were inoculated in 96-well plates, and then different concentrations of EA were added to incubate for 48 h. At the certain time, add 10 μl CCK-8 solution to each well and wait for another 2 h. The absorbance was measured at 450 nm with the Infinite 200PRO (Science＆Technology, Männedorf, Swiss).

Apoptosis assay

RAJI cells at logarithmic growth stage were taken, centrifuged, collected, counted, and inoculated with 5×10⁵ Raji cells per well into a 6-well plate. At the same time, EA liquid with final concentrations of 0,50 and 100 μmol /L were added, and the cells were centrifuged and collected after co-culture in a carbon dioxide incubator for 48 h. The cells were washed twice with pre-cooled PBS, and the cells were re-suspended with 1 ×binding buffer. Appropriate amounts of cells were added with 5 μL Annexin V-FITC, mixed, incubated at room temperature for 15 min in dark light, then gently mixed with 10 μL PI dyeing solution and incubated for 5 min in dark light. Finally, 400 μL 1 × binding buffer was added and gently mixed, and apoptosis detection was completed within 1 h.

Cell cycle analysis

RAJI cells were inoculated in a 6-well plate (3 × 10⁵ cells per well) and treated with 0, 50, 100 µM of EA for 48 h, after 48 h of drug action. Centrifuge at 1500 rpm for 5 min, collect cells and discard the supernatant. Wash the cells twice with pre-cooled PBS, add 5 ml of pre-cooled 75% ethanol drop by drop, gently mix the cells, and place the cells in a refrigerator at -20°C overnight away from light. Centrifuge at 1500 rpm at 4°C for 5 min and discard the supernatant. Then, the supernatant was washed twice by pre-cooling PBS at 4°C, the supernatant was discarded, and the 500 μL PI/RNase staining solution was added and gently mixed. The samples were incubated at room temperature in the dark for 15 min. Before analysis, the samples were preserved in the dark at 4°C and detected by flow cytometry within 1 h.

Statistical analysis

The data were analyzed by GraphPad Prism 9.0.2 (San Diego, USA) and expressed as mean ± SD. Statistical analysis was carried out by one-way ANOVA followed by the Dunnett test. *p<0.05, **p<0.01, and ***p<0.001 were considered statistically significant differences.

Acquisition of EA and NHL related targets

The molecular structure and Canonical SMILES of EA were obtained from PubChem (Fig. 2A), SwissTargetPrediction and Pharmmapper database was imported, and 121 EA action targets were obtained. Genecard database (https://www.GeneCards.org) search for human NHL targets with the keyword "Non-Hodgkin lymphoma", OMIM database (https://www.OMIM.org) import "Non-Hodgin lymphoma", deweight, and get 2341targets. Venny 2.1.0 was used to map the intersection of EA-related targets and NHL-related targets, and 51 EA anti-NHL targets were obtained (Fig. 2B).

Identified 10 target genes for EA treatment of NHL

The drug-disease common target was submitted to STRING (http://string-db.org/) for PPI network construction. The confidence level was selected to be > 0.4, and the non-interacting free proteins were removed. There were 51 interacting proteins between EA and NHL. The protein-protein interaction network shows that 51 proteins and 208 interactions (edges) may interact at cross targets between EA and NHL (Fig. 3A). Based on the network topology analysis, the average intermediate centrality is 0.025 and the average degree is 8.16, which is the signal hub connection between genes. A total of 14 genes produced higher than average intermediate centrality and freedom values, but only the top 10 genes were shown (Table 1). Screening hub genes in interaction networks based on Cytohubba plug-ins. We used MCC algorithm to find out the top 10 Hub genes of target proteins, and constructed the Hub gene network map (Fig. 3B). We can conclude that TNF, PPARG, MMP9, HSP90AA1, PTGS2, IGF1R, AR, ESR2, NR3C1 and MMP2 are important targets of EA intervention in NHL (Fig. 3B).

Table 1

Target protein with potential critical role in EA treatment of NHL.
NO.	Target name	Betweenness centrality	Degree
1	TNF	0.33989357717653845	34
2	PPARG	0.11168608423692411	25
3	MMP9	0.06890531212322186	22
4	HSP90AA1	0.05298052645528479	19
5	PTGS2	0.02984749631339095	18
6	IGF1R	0.054040473006570736	16
7	AR	0.03706621069605661	15
8	ESR2	0.019215007477506464	12
9	NR3C1	0.5636363636363636	11
10	MMP2	0.5102040816326531	11

GO and KEGG pathway enrichment analysis of potential target genes

GO and KEGG enrichment analysis of 51 potential target proteins to reveal the potential mechanism of EA treatment of NHL. A total of 243 items were obtained from GO enrichment analysis, including 170 biological processes (BP), 30 cellular components (CC), and 120 molecular functions (MF). The top 20 GO enrichment analyses are shown in Fig. 4A, according to p < 0.05, p-values were corrected using the Benjamini–Hochberg procedure. In BP category, target proteins are mainly concentrated in cellular response to UV-A, extracellular matrix organization, positive regulation of protein kinase B signaling, etc. In CC category, target proteins are mainly concentrated in cytoplasm, nucleus, cytosol, nucleoplasm, etc. In MF category, target proteins are mainly concentrated in zinc ion binding, steroid binding, endopeptidase activity, serine-type endopeptidase activity, etc.

A total of 34 KEGG enrichment projects were selected, and the top 20 projects were selected according to KEGG analysis with BH correction p value < 0.05, mainly including pathways in cancer, Leishmaniasis, IL-17 signaling pathway, PPAR signaling pathway, etc. Based on the enrichment results of GO and KEGG, the first 20 related enrichment results were visualized (Fig. 4B). In the GO enrichment analysis and KEGG pathway analysis, TNF, PTGS2, PPARG and MMP9 are mainly enriched in the IL-17 signaling pathway, PPAR signaling pathway.

Molecular docking

To verify drug-target interactions, we performed molecular docking on 10 screened targets. CB-DOCK2 was used to analyze the docking potential of the 10 screened targets. The lower the binding energy score, the more stable the binding of the ligand-target complex, indicating a stronger interaction between the ligand and protein. The result of our analysis showed that the binding energy scores of the 10 targets and EA were all less than − 6kcal/mol, indicating that EA had good binding activity with the screened targets. The binding pose, 3D interaction were shown in Fig. 5 Docking Score, Cavity volume, docking center and docking size between selected targets and EA were provided in Table 2.

Table 2

Docking score, cavity volume, docking center, and docking size between selected targets.
Target	PDB ID	Vina score (kcal/mol)	Cavity volume (Å3)	Center (x, y, z)	Docking size (x, y, z)
TNF	2AZ5	-9.6	232	-12,69,18	23,23,23
PPARG	6C1I	-8.9	1405	23,63,27	23,31,23
MMP9	1GKC	-7.0	172	66,13,105	23,23,23
HSP90AA1	1UYD	-7.9	2198	6,10,23	23,23,23
PTGS2	5F1A	-9.3	16451	32,14,229	35,34,35
IGF1R	1JQH	-8.7	1432	-30,57,-5	23,23,23
AR	5CJ6	-8.2	222	-34,1,-24	23,23,23
ESR2	2J7X	-6.6	1472	57,46,84	23,29,23
NR3C1	6DXK	-8.5	3652	-5,9,-54	23,35,35
MMP2	1HOV	-7.9	415	5,-3,8	23,23,23

Bioinformatic analysis results

According to the analysis results of GEPIA2 database, mRNA expression levels of TNF, HSP90AA1 and PTGS2 in NHL tissues were significantly increased (Fig. 6A) (P < 0.05). Survival studies of key targets including TNF, PPARG, MMP9, HSP90AA1, and PTGS2 showed no significant difference in prognostic value (Fig. 6B). In addition, the correlation analysis between the mRNA levels of core targets and the pathological stages of CRC (Fig. 6C) showed that the expression levels of TNF, PPARG, MMP9, HSP90AA1 and PTGS2 had no significant differences with the pathological stages.

EA inhibited the proliferation of non-Hodgkin's lymphoma cells

To investigated the effect of EA on NHL cell proliferation, RAJI cells were treated with different concentrations of EA (12.5µM to 100 µM) for 48 h. The results of CCK8 experiment showed that the proliferation of RAJI cells could be inhibited by EA in a dose-dependent manner (Fig. 7). The growth of NHL cells was inhibited by EA.

EA induced the apoptosis of non-Hodgkin lymphoma cells

RAJI cells were incubated with different concentrations of EA (50 µM, 100µM) for 48 h. The RAJI cells apoptosis were detected using flow cytometry. The results showed that the apoptosis of RAJI cells was induced in a dose-dependent manner (50 µM, 100µM) by EA (Fig. 8).

EA induced NHL cells block in the G1 stage

RAJI cells were incubated with different concentrations of EA (50 µM and 100µM) for 48 h, and PI/RNase Staining Buffer was used to detect RAJI cell cycle. The results showed that EA induced the arrest of RAJI cells in G1 phase in a dose-dependent manner (50 µM, 100µM) (Fig. 9).

The incidence of NHL gradually increases in malignant lymphoma, accounting for about 90%, and the incidence increases with age, especially in males (Liu et al. 2022). Conventional treatments for NHL include chemotherapy, radiotherapy, stem cell transplantation and targeted therapy, overall survival rate of NHL within 5 years was only 70% (Sun et al. 2022). Rituximab was approved by the US FDA for the treatment of B-cell lymphoma, and the therapeutic effect of most patients has been significantly improved, but 30–45% of patients still suffering from relapse or progression (Wang and Li. 2020). There is therefore an urgent need to find new targets for the treatment of NHL.

EA is a triterpenoid acid compound found in rosaceae (e.g. Roseberry, golden cherry) (Tao et al. 2023), and has been reported to have anti-tumor activity in various types of cancer cells(Dai et al. 2019; Ouyang et al. 2019). Our previous research found that EA has an anti-NHL effect (Li et al., 2022). However, the exact mechanism by which EA acts on NHL is unclear. In this study, we investigated the mechanism of EA in the treatment of NHL based on network pharmacology. EA and NHL have a total of 51 intersection targets. The results of PPI network analysis showed that TNF, PPARG, MMP9, HSP90AA1, PTGS2, IGF1R, AR, ESR2, NR3C1 and MMP2 were the core targets that played an important role in the treatment of EA in NHL. Molecular docking results showed that EA had strong binding efficiency with 10 key genes. GO and KEGG enrichment analysis showed that EA acted on NHL through multiple pathways, especially IL-17 signaling pathway, PPAR signaling pathway, inducing apoptosis and inhibiting cell cycle. Furthermore, TNF, PTGS2, PPARG and MMP9 are mainly enriched in the IL-17 signaling pathway and PPAR signaling pathway.

Studies have shown that serum levels of TNF and its receptor are elevated in NHL patients, and higher levels of TNF are associated with poor prognosis of NHL (Metkar et al. 2000). PTGS2, also known as COX-2, is a key enzyme in prostaglandin biosynthesis. Overexpression of PTGS2 is also found in NHL patients and is related to the formation of new lymphatic vessels (Ma et al. 2012). PTGS2 is one of the inducers of lymphangiogenesis in NHL, and its expression level is negatively correlated with prognosis (Hazar et al. 2004). In NHL, activation of PPARγ was also found to have a dose-dependent effect against cell proliferation (Eucker et al. 2006). MMP9 is one of the most complex matrix metalloproteinases, which is highly expressed in a variety of tumors, including breast cancer, colon cancer, NHL, etc. (Negaard et al. 2009; Augoff et al. 2022). High levels of MMP9 play a crucial role in the aggressiveness of NHL by promoting ECM degradation, metastasis establishment and tissue destruction, and angiogenesis (Kossakowska et al. 2000; Sakata et al. 2004). Considering that core targets PPARG, PTGS2, TNF, and MMP9 are involved in the regulation of apoptosis (Naimi et al. 2018; Chen et al. 2019; Wu et al. 2019; Yu et al. 2019) and the results of GO and KEGG enrichment analysis, we further studied the effect of EA on apoptosis of RAJI. The results showed that EA could significantly promote apoptosis of RAJI cells, which was consistent with the prediction of network pharmacology.

In this study, the molecular and pharmacological mechanisms of EA on NHL were elucidated through network pharmacology and molecular docking studies. TNF, PPARG, MMP9, HSP90AA1, PTGS2, IGF1R, AR, ESR2, NR3C1 and MMP2 may play a key role in the anti-NHL mechanism of EA. We also found that TNF, PTGS2, PPARG, and MMP9 target genes enriched in IL-17 and PPAR signaling pathways. These results suggested that EA promoted NHL cells apoptosis by acting on TNF, PTGS2, PPARG, and MMP9 through the IL-17 and PPAR signaling pathways.

Author contribution Guangru Li and Chunyuan Liang performed the experiments, analysed thedata, and wrote the paper. Yan Liu, Guocai Wu and Zhiyuan Li performed parts of the experimentsand provided valuable suggestions for this study. Ruiting wen and Zhigang Yang designed the research and revised the paper. Yueyuan Pan completed the statistical analysis in the article. The authors declare that all data weregenerated in-house and that no paper mill was used.

Funding This work was supported by National Natural Science Foundation of China (82200238), the Natural Science Foundation of Guangdong Province (2021A1515010708, and 2023A1515010594), and Science and Technology Plan Project of Zhanjiang city (2020A0611, 2021A05153, 2021A05137, and 2021A05150).

Data availability The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.

Ethics approval The ethical approval was not necessary in this study because it did not involve human and animal subjects.

Consent for publication All authors agree to publish.

Competing interests The authors declare no competing interests.

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Study on the mechanism of Euscaphic acid in the treatment of non-Hodgkin lymphoma based on network pharmacology and molecular docking

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Abstract

Figures

Introduction

Materials and Methods

Results

Acquisition of EA and NHL related targets

Identified 10 target genes for EA treatment of NHL

GO and KEGG pathway enrichment analysis of potential target genes

Molecular docking

Bioinformatic analysis results

EA inhibited the proliferation of non-Hodgkin's lymphoma cells

EA induced the apoptosis of non-Hodgkin lymphoma cells

EA induced NHL cells block in the G1 stage

Discussion

Conclusion

Declarations

References

Additional Declarations

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