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The potential effect and mechanism of Saikosaponin A against gastric cancer

BMC Complementary Medicine and Therapies volume 23, Article number: 295 (2023) Cite this article

Abstract

Background

Saikosaponin A (SSA) shows a series of pharmacological activities, such as anti-inflammatory, antioxidant and antitumor. However, there is a lack of comprehensive research or sufficient evidence regarding the efficacy of SSA in treating gastric cancer (GC), and the specific mechanisms by which it inhibits GC growth and progression are still not fully understood.

Methods

MTT and clonogenic assays were employed to detect the effect of SSA on the proliferation of GC cells. Bioinformatics predicted the SSA targets in the treatment of GC. The core genes and the underlying mechanism of SSA in anti-GC were obtained by analyzing the intersecting targets; molecular docking and Western blot were used to check the reliability of core genes. Flow cytometry was used to analyze apoptosis and cell cycle in GC cells treated with varying concentrations of SSA. Western blot was employed to detect the expression levels of related proteins.

Results

SSA significantly blocked GC cells in the S phase of the cell cycle and induced apoptosis to suppress the proliferation of GC cells. Network pharmacology revealed that the underlying mechanisms through which SSA acts against GC involve the modulation of several signaling pathways, including the PI3K-Akt, MAPK, RAS, and T-cell signaling pathways. Molecular docking showed pivotal target genes with a high affinity to SSA, including STAT3, MYC, TNF, STAT5B, Caspase-3 and SRC. Furthermore, western blot results revealed that SSA significantly increased the protein levels of Bax and Cleaved Caspase-3, whereas decreased the expression levels of p-JAK, p-STAT3, MYC, Bcl-2, p-PI3K, p-AKT and p-mTOR, confirming that the reliability of hub targets and SSA could promote GC cell apoptosis by suppressing PI3K/AKT/mTOR pathway.

Conclusions

The results suggest that SSA has the ability to trigger apoptosis in GC cells by blocking the PI3K/AKT/mTOR pathway. These findings highlight the potential of SSA as a promising natural therapeutic agent for the treatment of GC.

Peer Review reports

Introduction

Gastric cancer (GC) remains the fourth leading reason of cancer related death worldwide according to global cancer statistics in 2020 [1]. Despite significant advances in therapies for gastric cancer, including surgery, chemotherapy, targeted therapy and immune checkpoint inhibitors, the prognosis of gastric cancer is still very poor, causing a large socioeconomic burden, especially in China [2, 3]. Hence, the identification of novel treatment approaches for GC holds great importance.

Natural products have always played a crucial role in the pharmaceutical drugs, particularly in the field of oncology. Most currently available chemotherapy drugs, including paclitaxel, vincristine and vinblastine, are all derived from natural sources [4, 5]. Radix Bupleuri (RB) has been used for thousands of years in China, and its injection has also been of clinically useful for more than 80 years, with the disadvantages of complex composition and uncontrollable quality. Saikosaponin A (SSA) is a triterpenoid glycoside extracted from RB. As a major bioactive compound of RB, SSA shows a series of pharmacological activities, such as anti-inflammatory, antioxidant, antiviral and antitumor [6]. However, limited reports have mentioned the effect of SSA against GC cells, and its underlying anti-GC mechanism has yet to be elucidated [6,7,8]. In recent years, network pharmacology, an emerging area of pharmacology, has revealed multiple targets and mechanisms of drug action by mapping the human disease-gene network onto the polypharmacology network [9, 10]. Network pharmacology provides a new strategy for drug discovery and simultaneously becomes a powerful tool for exploring the mechanisms of natural products [11, 12].

In the present study, we first obtained potential common targets to predict the molecular mechanism of SSA against GC using network pharmacology. Subsequently, we showed the effect of SSA on the proliferation, migration, apoptosis and cycle of GC cells (AGS, HGC-27 and MKN-28). We confirmed that SSA induced GC cell apoptosis partly by inhibiting the PI3K/AKT pathway (Fig. 1).

Fig. 1
figure 1

Scheme of the study

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Materials and methods

Chemicals and reagents

SSA (purity > 95%) was obtained by Chengdu Must Bio-Technology Co., Ltd. (Chengdu, China) and dissolved in dimethyl sulfoxide (DMSO) at a concentration of 50 mg/ml. Antibodies against p-PI3K (No. 4228S), PI3K (No. 5405S), p-AKT (No. 2965S), AKT (No. 4691S), p-mTOR (No. 2971S), mTOR (No. 2972S), Cleaved Caspase-3 (No. 9554 T) and Bax (No. 2772 T) were acquired from Cell Signaling Technologies (MA, USA). Antibodies against Caspase-3 (No. 66470–2-lg), Bcl-2 (No. 26593–1-AP), p- JAK (No. 4869S), p- STAT (No. 5027S) and MYC (No. 6935S) were purchased from Proteintech Group (MA, USA). Anti-mouse (No. ZB-2305) and anti-rabbit (No. ZB-2301) horseradish peroxidase-conjugated antibodies were acquired from Zhongshanjingqiao Biotechnology (Beijing, China). 740 Y-PTFA (123,618–16-1), a potent and cell-permeable PI3K activator, was purchased from MedChemExpress (MA, USA).

Cell culture

The human gastric cancer cell lines AGS, HGC-27 and MKN-28 were acquired from the Shanghai Cell Bank of the Chinese Academy of Sciences. All cells were cultured in RPMI 1640 medium (Gibco, USA) supplemented with 10% newborn calf serum (Evergreen Company, Hangzhou, China) and 1% penicillin/streptomycin, and incubated at 37 °C in a humidified atmosphere of 5% CO2.

Cell proliferation assay

The effect of SSA on GC cell proliferation was evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT; Sigma, MO, USA). The cells were seeded in a 96-well plate at a density of 5000 cells per well. After overnight cultured, the cells were incubated with RPMI 1640 medium containing 0, 5, 10, 15, 20, 25, 30, 35, or 40 μg/ml SSA for 12, 24 and 48 h. Then, the cells were incubated with MTT (120 μl, 5 mg/ml) at 37 °C for 4 h. After removing the supernatant, the cells were incubated with 150 μl of DMSO for 10 min. Cell viability was examined with a microplate reader (Bio-Tek, USA) at a 570 nm absorbance. Ultimately, we selected 0, 5, 10 and 20 μg/ml SSA, and 24 h for further study according to the IC50 values at different times.

Colony formation assay

GC cells (500 cells/well) were seeded in a 6-well plate, treated with RPMI 1640 medium containing 0, 5, 10 and 20 μg/ml SSA for 24 h, and then cultured for 7–10 days. After washing twice with PBS, the cells were stained with 1 ml of paraformaldehyde (Beyotime Biotechnology, Shanghai, China) at 4 °C for 1 h, added to 1 ml of crystal violet (Beyotime Biotechnology, Shanghai, China) for 2 min, washed with water several times, and then photographed.

Target predictions for SSA and GC

Structural information regarding SSA (Fig. 2a, CAS: 20736–09-8) was acquired from NCBI PubChem (https://pubchem.ncbi.nlm.nih.gov/). The targets of SSA were obtained from the TCMSP database (http://lsp.nwu.edu.cn/tcmsp.php)  [13], Swiss Target Prediction database (http://www.swisstargetprediction.ch/) filtered by the condition of “probability > 0” [14] and Chem mapper database (https://lilab-ecust.cn/chemmapper/) [15] filtered by the condition of “Homo sapiens; score > 0”.

Fig. 2
figure 2

SSA suppressed proliferation in GC cells. a AGS, HGC-27 and MKN-28 cells were treated with different concentrations of SSA for 24 h. b MKN-28 cells were incubated with SSA for 12, 24 and 48 h. c Representative cell morphological changes. Scale bars: 50 μm. d Colony formation assay of SSA in AGS, HGC-27 and MKN-28 cells. Data are presented as the mean ± SD (n = 3), *P < 0.05, **P < 0.01, ***P < 0.001 compared to the control group

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Genes associated with GC were obtained from three public database sources, including the DisGeNET database (https://www.disgenet.org/) [16], DrugBank database (https://go.drugbank.com/) [17], and GeneCards database (https://www.genecards.org/) [18]. Using “Stomach Carcinoma” as the keyword, we searched and screened gastric cancer-related target datasets, and retained “Homo sapiens” proteins linked to GC. Subsequently, we submitted all targets to the UniProt database (http://www.UniProt.org/) [19], and restricted the types to “Homo sapiens” for standardization.

PPI network establishing and hub target screening

The common targets related to SSA and GC were acquired by Venny 2.1 (http://bioinfogp.cnb.csic.es/tools/venny/index.html) . Afterward, the interaction relationship of common targets was explored by the STRING database (https://string-db.org/) [20] with the condition “Homo sapiens” and score value > 0.4. Subsequently, the PPI network file of common goals was inputted into Cytoscape 3.7.2 for further study. Cytoscape can analyze complex networks with powerful data integration and visualization functions [21]. To discover the highly interconnected regions of interaction in PPI networks, the top 6 proteins based on the degree level were defined as hub targets.

GO and KEGG pathway enrichment study

Gene ontology (GO) is a commonly used approach for annotating genes and their products from organisms, including analyses of molecular function (MF), biological process (BP), and cellular component (CC) [10]. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a useful database resource that links genomic information with functional information by systematically analyzing gene functions [22,23,24]. The enrichment analyses of GO terms and KEGG pathways were carried out by the DAVID database (https://david.ncifcrf.gov/), which helps the genes’ functional interpretation [25]. The results of pathway enrichment might show critical mechanism of action for SSA against GC.

Molecular docking and Western blot verification

The MOL2 format of SSA was downloaded from the PubChem database and then converted to the 3D structure using Open Babel (ver 2.4.1). The crystal structure of the candidate protein was retrieved from the RCSB protein database (http://www.pdb.org/), and the structure was optimized by deleting water molecules, adding hydrogen, and adding charges. Molecular docking calculations were executed by AutoDock Vina 1.1.2, and all docking operation options were default values. Docking scores can predict the binding affinity between molecules and proteins. Finally, the docking results with the highest score were visualized through PyMoL and Western blot.

Apoptosis analysis

Cell death and apoptosis were assessed using the Annexin V-FITC Apoptosis Detection Kit I (Keygen Biotech, Nanjing, China). MKN-28 cells (5 × 105 cells/well) were inoculated into 6-well plates and cultured overnight. After culturing with 1640 complete medium containing 0, 5, 10 and 20 μg/ml SSA for 24 h, the cells were washed with PBS, centrifugated, resuspended, and collected into a flow cytometry tube. Afterward, the cells were incubated with Annexin V and propidium iodide (PI) for 15 min, and detected by a FACSAria III flow cytometer (BD Biosciences, USA).

Cell cycle analysis

MKN-28 cells (5 × 105 cells/well) were seeded into 6-well plates, and incubated for 24 h. After treatment with SSA at different concentrations (0, 5, 10, 20 μg/ml) for 24 h, the cells were washed with cold PBS, and then fixed with cold 70% ethanol at 4 °C overnight. After that, a cell cycle analysis kit (Beyotime Biotechnology, Shanghai, China) was used to detect the cell cycle distribution based on the manufacturer’s instruction. Cell cycle distribution was analyzed by a FACSAria III flow cytometer (BD Biosciences, USA) with Modifit software.

Western blot analysis

SSA (0, 5, 10, 20 μg/ml) were subjected to MKN-28 cells for 24 h, respectively. The cells were lysed with RIPA lysis buffer (containing a protease inhibitor and dephosphorylation inhibitor) on ice, and the supernatant was collected as cell protein extracts. Afterward, the protein concentration was measured with a BCA Protein Assay Kit (Beyotime Biotechnology, Shanghai, China). Protein extracts were separated by Smart PAGE™ Precast Protein Gel (4–20%) (Smart-lifesciences, Changzhou, China) and transferred to PVDF membranes. The membranes were blocked in 5% bovine serum albumin (BSA) for 1 h, and then incubated with the primary antibodies at 4 °C overnight. The molecular weight of the target proteins is approximate, so the partial original blots were cut before hybridizing with the primary antibodies. Then the blots were incubated with an appropriate secondary antibody at room temperature for 1 h. Finally, the protein bands were visualized using an enhanced chemiluminescence (ECL) kit and quantified with a Chemiluminescence Imaging System (ChemiDoc XRS, Bio-Rad). In addition, WB quantifications were performed by the Image Lab system, version 5.1.

Rescue experiment

SSA (0 μg/ml), SSA (20 μg/ml), 740 Y-PTFA, and 740 Y-PTFA + SSA (20 μg/ml) were cultured with MKN-28 cells for 24 h. Western blot was used to detect the expression of PI3K, p-PI3K, Akt, p-Akt, p-mTOR, mTOR, Bcl-2 and Bax in each group.

Statistical analysis

Data are represented as the mean ± SD. Comparisons between different groups were used with one-way ANOVA with LSD as a post hoc comparison in GraphPad Prism 8.0 software, and P < 0.05 was considered indicative of a statistically significant difference.

Results

SSA inhibited GC cell proliferation

We initially evaluated the in vitro effect of SSA in the treatment of GC cell lines HGC-27, AGS and MKN-28. The cells were cultured with SSA at different concentrations for 24 h to calculate the IC50 values. The results showed 18.99 μg/ml in MKN-28 cells, 24.73 μg/ml in HGC-27 cells, and 23.41 μg/ml in AGS cells (Fig. 2a), indicating that SSA exerted potent cytotoxicity in GC cells. We also found that SSA decreased GC cell viability in a time and dose-dependent manner (Fig. 2b). Specially, increasing the concentration of SSA led to a gradual decrease in the number of GC cells, accompanied by a change in their morphology towards a rounder shape, suggesting that SSA has the capability to inhibit the growth of GC cells and induce alterations in their normal morphology (Fig. 2c). Furthermore, the colony formation assay also indicated that SSA inhibited the generation of cell colonies in a concentration-dependent manner (Fig. 2d). These data suggested that SSA inhibited GC cell proliferation.

Screening the hub targets of SSA and GC

To explore the potential targets of SSA in the proliferation of GC cells, we conducted a screening process to identify the key targets that are common to both SSA and GC. The chemical structure of SSA is presented in Fig. 3a. In the study, we obtained 48 predicted genes associated with SSA and 2876 disease targets related to GC after removing duplicate targets. Subsequently, the targets associated with SSA or GC were imported into Venny 2.1 to acquire 29 common genes (Fig. 3b). Detailed information on the common target genes is shown in Table 1. Next, the PPI network was constructed by the STRING database and visualized with Cytoscape, which composed of 29 nodes and 152 edges (Fig. 3c). The top 6 potential candidate target genes, including signal transducer and activator of transcription 3 (STAT3), Myc proto-oncogene protein (MYC), tumor necrosis factor (TNF), signal transducer and activator of transcription 5B (STAT5B), Caspase-3 (CASP3) and proto-oncogene tyrosine-protein kinase Src (SRC), were identified as hub genes according to the degree value (Fig. 3d). Detailed information about the hub genes is shown in Table 2.

Fig. 3
figure 3

PPI network and hub targets. a The chemical structure of SSA. b Venn diagram of the intersecting targets for SSA and GC. c PPI network of SSA in the treatment of GC. d The top 6 hub genes of SSA against GC. The redder and larger the node is, the more important it is in the network

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Table 1 Potential common targets for SSA in the treatment of GC
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Table 2 Detailed information of hub genes
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Prediction of the mechanism of SSA against GC

To further probe the underlying mechanism of SSA in the treatment of GC, GO functional and KEGG pathway enrichment analyses of 29 common targets were carried out by the DAVID database. According to GO enrichment analysis, BP was significantly enriched in “response to drug”, “negative regulation of cell proliferation process” and “regulation of apoptotic”. CC was enriched in “nucleus”, “plasma membrane” and “cytosol”. The MF was mainly associated with “transmembrane receptor protein tyrosine kinase activity”, “protein tyrosine kinase activity” and “ATP binding” (Fig. 4a). GO enrichment analysis indicated that SSA could participate in various cellular anticancer processes.

Fig. 4
figure 4

Enrichment analysis of common target genes and verification of hub targets. a GO functional enrichment analysis involving BP, CC and MF categories. b KEGG pathway enrichment analysis. The size and color of the dots represented the number of genes and the corrected P value, respectively. c Docking results of SSA with the hub targets STAT3, MYC, TNF, STAT5B, CASP3, and SRC. d The protein expression levels of p-JAK, JAK, p- STAT3, STAT3 and MYC in MKN-28 cells after SSA treatment. e The relative protein expression of p-JAK/JAK, p-STAT3/STAT3 and MYC in MKN-28 cells after SSA treatment

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Meanwhile, the enrichment analysis of KEGG pathway showed a total of 55 signaling pathways with significance that were strongly related to “pathways in cancer”, “PI3K-Akt signaling pathway”, “Ras signaling pathway”, “MAPK signaling pathway”, “T-cell receptor pathway”, “apoptosis”, etc. (Fig. 4b). Among them, the PI3K-Akt signaling pathway corresponded to the most targets, including 10 potential therapeutic targets. The PI3K-Akt pathway is a critical signaling pathway for cell survival that regulates cell proliferation and motility, and activation of this pathway can lead to abnormal cell proliferation in GC [26]. In addition, triggering apoptosis is a common strategy in anticancer therapies [27]. Therefore, we speculated that the mechanism of SSA in the treatment of GC can partly induce cell apoptosis by regulating the PI3K-Akt signaling pathway. Moreover, to validate the reliability of the protein–ligand interactions, molecular docking was carried out for 6 hub proteins and SSA. In molecular docking, the affinity score is a measure of the binding strength between molecules. A lower score indicates a stronger binding affinity. A score < -5.0 kcal/mol suggested good binding activity between the ligand and targets [28]. The docking scores of the hub target proteins and SSA are shown in Table 3. The results indicated that the 6 hub targets had a better binding activity to SSA (Fig. 4c).

Table 3 Vina scores and cavity information of the hub targeted proteins
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To validate the aforementioned findings, we measured the expression levels of JAK/STAT/MYC proteins. The results showed that SSA decreased p-JAK, p-STAT3 and MYC levels in MKN-28 cells (Fig. 4d-e). These findings suggest that the inhibition of MKN-28 cell proliferation by SSA is achieved through the regulation of JAK/STAT/MYC levels. Moreover, these results provide further evidence for the reliability of the identified hub targets.

SSA induced apoptosis and cell cycle S phase arrest in GC cells

We further determined whether SSA inhibited GC cell growth via apoptosis. Annexin-V/PI double staining showed that early apoptotic rates were significantly increased in GC cells, especially in MKN-28 cells, following treatment with SSA (Fig. 5). These data suggested that SSA could induce early apoptosis in GC cells in a dose-dependent manner, and the primary pathway of GC cell death induced by SSA is apoptosis. At the same time, we found that the proportion of apoptosis in MKN-28 cells at 20 μg/ml was still about 25%, and the necrosis ratio was about 15%, indicating other ways of SSA inducing GC cell death, which is worth further exploration in more experiments.

Fig. 5
figure 5

SSA promoted apoptotic death in GC cells. a The apoptotic status of HGC-27, AGS and MKN-28 cells at different concentrations of SSA. b The percentages of apoptotic cells. Data are presented as the mean ± SD (n = 3), *P < 0.05, **P < 0.01, ***P < 0.001 compared to the control group

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To explore the influence of SSA on cell cycle progression in GC cells, a cell cycle assay was performed. After 24 h of SSA treatment, the proportion of GC cells in S-phase significantly increased, while that in G0/G1 phase decreased (Fig. 6). The results indicated that SSA suppressed GC cell proliferation by blocking the cell cycle in the S-phase.

Fig. 6
figure 6

SSA arrested the cell cycle at the S phase in GC cells. a The cell cycle status of HGC-27, AGS and MKN-28 cells treated with different concentrations of SSA. b The percentages of cell cycle cells

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SSA induced GC cells apoptosis by the PI3K-AKT pathway

Network pharmacology indicated that SSA induced the apoptosis of GC cells by partly regulating the PI3K-AKT pathway. Aberrant activation of the PI3K/Akt/mTOR signaling pathway promotes GC progression through inhibition of apoptosis, drug resistance, angiogenesis, metastasis and epithelial to mesenchymal transition. And clinical trials indicated that inhibition of this pathway could lead to regression of GC [26, 29]. Subsequently, we specifically chose MKN-28 cells that exhibited the most notable apoptotic effect to conduct additional experiments and further validate the hypothesis. Western blot analysis suggested that SSA reduced the protein expression levels of p-PI3K/PI3K, p-AKT/AKT and p-mTOR/mTOR in MKN-28 cells (P < 0.05) (Fig. 7a-b). Furthermore, the expression levels of apoptosis-related proteins were also measured. The results showed that SSA markedly enhanced the protein expression of Bax, while reducing the expression levels of Bcl-2 and Cleaved Caspase-3 (P < 0.05) (Fig. 7c-d). Additionally, the trend of the difference was more noticeable with the increasing concentration of SSA. These results indicated that SSA induced GC cell apoptosis by partly inhibiting the PI3K/AKT pathway. The rescue experiment also supported this point (Fig. 7e-f).

Fig. 7
figure 7

SSA inhibited apoptosis of MKN-28 cells by regulating the PI3K/AKT/mTOR pathway. a The protein expression levels of PI3K, p-PI3K, AKT, p-AKT, mTOR and p-mTOR in MKN-28 cells after SSA treatment. b The relative protein expression of p-PI3K/PI3K, p-AKT/AKT and p-mTOR/mTOR in MKN-28 cells after SSA treatment. c The protein expression levels of Bax, Bcl-2, Caspase-3, and Cleaved Caspase-3 in MKN-28 cells after SSA treatment. d The relative protein expression of Cleaved Caspase-3, Caspase-3, Bax and Bcl-2 was normalized to β-actin in MKN-28 cells after SSA treatment. e The protein expression levels of PI3K, p-PI3K, AKT, p-AKT, mTOR, p-mTOR, Bax and Bcl-2 in the rescue experiment. f The relative protein expression of p-PI3K/PI3K, p-AKT/AKT, p-mTOR/mTOR, Bax and Bcl-2 in the rescue experiment. Data are presented as the mean ± SD (n = 3), *P < 0.05, **P < 0.01, ***P < 0.001 compared to the control group. The Western blots have been cropped

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Discussion

In this study, we investigated the core targets and potential mechanism of SSA in the treatment of GC via network pharmacology, molecular docking and in vitro experiments. We found that SSA induced GC cell apoptosis by partly inhibiting the PI3K/AKT pathway.

According to the PPI network, STAT3, MYC, TNF, STAT5B, Caspase-3, and SRC were identified as hub nodes. STAT3 is a cytoplasmic transcription factor highly expressed in gastric cancer tissues [30], and its aberrant activation can promote tumor formation by regulating tumor cell proliferation, angiogenesis, migration and immune evasion [31]. MYC plays a valuable role in almost every stage of oncogenesis by orchestrating proliferation, differentiation and metabolism, and its blockade has been widely explored for the treatment of cancer [32]. According to research, TNF has higher levels in patients with GC [33]. STAT5B is an oncology target with a high value for pharmacologic intervention [34]. Caspase-3 is the terminally executed protease in apoptosis, whose activation commits the cell to enter the irreversible fate of apoptosis [35]. SRC is a nonreceptor tyrosine kinase that can promote oncogenesis advancement by regulating cellular proliferation and angiogenesis [36]. It was reported that the expression and activity of SRC are increased in GC. Furthermore, selective SRC inhibitors are entering the clinic to enrich the family of anticancer agents [37]. These hub targets were closely associated with the occurrence or treatment of GC and showed a better affinity for SSA in molecular docking, suggesting the potential effect against GC.

Among them, STAT3 and MYC were the most two common targets, which are typically in the JAK-STAT pathway. Therefore, we detected the levels of JAK/STAT/MYC, and found that SSA can inhibit MKN-28 cell proliferation by regulating JAK/STAT/MYC levels, indicating the reliability of core targets.

KEGG analysis suggested that PI3K-Akt, RAS, MAPK signaling, T-cell receptor, and apoptosis pathways may be involved in the mechanisms by which SSA against GC. It is well known that PI3K-Akt signaling can regulate cell proliferation to promote tumorigenesis [38, 39]. Apoptosis is an ordered process of cell death occurring under physiological or pathological conditions [40]. Dysregulation of apoptosis is one of the main causes of human cancer. Most anticancer therapies aim to trigger apoptosis to eliminate malignant cells [27]. In our study, we revealed that SSA could induce apoptosis of GC cells partly by regulating the PI3K-AKT signaling pathway.

Our results also indicated that SSA could block the cell cycle in the S-phase and induce cell apoptosis to inhibit the proliferation of GC cells. By Western blot experiments, we verified the changes of PI3K-Akt signaling pathway and the levels of the apoptosis-related proteins Bcl-2, Bax and Cleaved Caspase-3 after SSA treatment. Bcl-2 family members and Caspases are key effectors of apoptosis. In the Bcl family of proteins, Bcl-2 exerts an anti-apoptotic effect by blocking the release of cytochrome-c, while Bax shows a pro-apoptotic role by promoting the release of cytochrome-c. The balance between Bcl-2 and Bax regulates the apoptosis state of cells [41]. Caspase-3 is a downstream enzyme that executes the process of cell apoptosis, and is also the most important regulator of apoptosis. When Caspase-3 zymogen is cleaved by protease, Caspase-3 is activated (Cleaved Caspase-3) and expands the protease cascade, eventually leading to nuclear apoptosis [42]. Our results showed that SSA significantly reduced the levels of p-PI3K, p-AKT, p-mTOR, Bcl-2 and Cleaved Caspase-3, and markedly increased the levels of Bax in GC cells. These results suggested that SSA inhibited the PI3K-AKT pathway and regulated Cleaved Caspase-3, Bax and Bcl-2 proteins to promote GC cell apoptosis.

PI3K-AKT signaling pathway, one of the most common dysregulated molecular pathways in human cancer, could block cell death to induce the occurrence of cancer by activating different downstream effectors [43]. It has been reported that many PI3K/Akt/mTOR small molecule inhibitors are widely used in preclinical studies, and some inhibitors, such as idelalisib, duvelisib and deforolimus, have been clinically approved for cancer treatment [26]. In the present study, we also showed that SSA could induce GC cell apoptosis by inhibiting the PI3K/Akt/mTOR signaling pathway, suggesting the potential clinical application of SSA for GC.

Conclusions

In summary, our results indicated that SSA could induce apoptosis of GC cells partly by suppressing the PI3K/Akt/mTOR signaling pathway. SSA, as a natural compound, has the potential to be developed as a novel multitarget drug for GC treatment. Further experiments are still needed to explore the potential mechanism of SSA in the field of GC therapy.

Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

GC:

Gastric cancer

SSA:

Saikosaponin A

RB:

Radix Bupleuri

MTT:

3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide

GO:

Gene ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

MF:

Molecular function

BP:

Biological process

CC:

Cellular component

PI:

Propidium iodide

ECL:

Enhanced chemiluminescence

STAT3:

Signal transducer and activator of transcription 3

MYC:

Myc proto-oncogene protein

TNF:

Tumor necrosis factor

STAT5B:

Signal transducer and activator of transcription 5B

CASP3:

Caspase-3

SRC:

Proto-oncogene tyrosine-protein kinase Src

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.

    Article  PubMed  Google Scholar 

  2. Smyth EC, Nilsson M, Grabsch HI, van Grieken NCT, Lordick F. Gastric cancer. Lancet. 2020;396(10251):635–48.

    Article  CAS  PubMed  Google Scholar 

  3. Wang FH, Shen L, Li J, Zhou Z-W, Liang H, Zhang XT, et al. The Chinese Society of Clinical Oncology (CSCO): clinical guidelines for the diagnosis and treatment of gastric cancer. Cancer Commun. 2019;39(1):10.

    Article  Google Scholar 

  4. Mann J. Natural products in cancer chemotherapy: past, present and future. Nat Rev Cancer. 2002;2(2):143–8.

    Article  CAS  PubMed  Google Scholar 

  5. Zhu H, Zhao H, Zhang L, Xu J, Zhu C, Zhao H, et al. Dandelion root extract suppressed gastric cancer cells proliferation and migration through targeting lncRNA-CCAT1. Biomed Pharmacother. 2017;93:1010–7.

    Article  CAS  PubMed  Google Scholar 

  6. Li X, Li X, Huang N, Liu R, Sun R. A comprehensive review and perspectives on pharmacology and toxicology of saikosaponins. Phytomedicine. 2018;50:73–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lee KJ, Xu MY, Shehzad O, Seo EK, Kim YS. Separation of triterpenoid saponins from the root of Bupleurum falcatum by counter current chromatography: The relationship between the partition coefficients and solvent system composition. J Sep Sci. 2014;37(23):3587–94.

    Article  CAS  PubMed  Google Scholar 

  8. Yuan B, Yang R, Ma Y, Zhou S, Zhang X, Liu Y. A systematic review of the active saikosaponins and extracts isolated from Radix Bupleuri and their applications. Pharm Biol. 2017;55(1):620–35.

    Article  CAS  PubMed  Google Scholar 

  9. Berger SI, Iyengar R. Network analyses in systems pharmacology. Bioinformatics (Oxford, England). 2009;25(19):2466–72.

    CAS  PubMed  Google Scholar 

  10. Hopkins AL. Network pharmacology. Nat Biotechnol. 2007;25(10):1110–1.

    Article  CAS  PubMed  Google Scholar 

  11. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11):682–90.

    Article  CAS  PubMed  Google Scholar 

  12. Fang J, Liu C, Wang Q, Lin P, Cheng F. In silico polypharmacology of natural products. Brief Bioinform. 2017;19(6):1153–71.

    Google Scholar 

  13. Ru J, Li P, Wang J, Zhou W, Li B, Huang C, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6(1):13.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V. SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res. 2014;42(Web Server issue):W32-38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gong J, Cai C, Liu X, Ku X, Jiang H, Gao D, et al. ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method. Bioinformatics. 2013;29(14):1827–9.

    Article  CAS  PubMed  Google Scholar 

  16. Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu-Pons J, Centeno E, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2016;45(D1):D833–9.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 50: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2017;46(1):1074–82.

    Google Scholar 

  18. Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D. GeneCards: integrating information about genes, proteins and diseases. Trends Genet. 1997;13(4):163.

    Article  CAS  PubMed  Google Scholar 

  19. Consortium TU. UniProt: a hub for protein information. Nucleic Acids Res. 2014;43(D1):D204–12.

    Article  Google Scholar 

  20. Ge Q, Chen L, Tang M, Zhang S, Liu L, Gao L, et al. Analysis of mulberry leaf components in the treatment of diabetes using network pharmacology. Eur J Pharmacol. 2018;833:50–62.

    Article  CAS  PubMed  Google Scholar 

  21. Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007;2(10):2366–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28(11):1947–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51(D1):D587–92.

    Article  CAS  PubMed  Google Scholar 

  25. Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, et al. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007;35(suppl_2):W169–75.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Alzahrani AS. PI3K/Akt/mTOR inhibitors in cancer: at the bench and bedside. Semin Cancer Biol. 2019;59:125–32.

    Article  CAS  PubMed  Google Scholar 

  27. Mohammad RM, Muqbil I, Lowe L, Yedjou C, Hsu H-Y, Lin L-T, et al. Broad targeting of resistance to apoptosis in cancer. Semin Cancer Biol. 2015;35 Suppl(0):S78–103.

    Article  PubMed  Google Scholar 

  28. Hsin KY, Ghosh S, Kitano H. Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PLoS One. 2013;8(12):e83922.

    Article  PubMed  PubMed Central  Google Scholar 

  29. BagherySaghchyKhorasani A, Pourbagheri-Sigaroodi A, Pirsalehi A, Safaroghli-azar A, Zali MR, Bashash D. The PI3K/Akt/mTOR signaling pathway in gastric cancer; from oncogenic variations to the possibilities for pharmacologic interventions. Eur J Pharmacol. 2021;898:173983.

    Article  CAS  Google Scholar 

  30. Pan YM, Wang CG, Zhu M, Xing R, Cui JT, Li WM, et al. STAT3 signaling drives EZH2 transcriptional activation and mediates poor prognosis in gastric cancer. Mol Cancer. 2016;15(1):79.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lee H, Jeong AJ, Ye SK. Highlighted STAT3 as a potential drug target for cancer therapy. BMB Rep. 2019;52(7):415–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chen H, Liu H, Qing G. Targeting oncogenic Myc as a strategy for cancer treatment. Signal Transduct Target Ther. 2018;3:5.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Partida-Rodríguez O, Torres J, Flores-Luna L, Camorlinga M, Nieves-Ramírez M, Lazcano E, et al. Polymorphisms in TNF and HSP-70 show a significant association with gastric cancer and duodenal ulcer. Int J Cancer. 2010;126(8):1861–8.

    Article  PubMed  Google Scholar 

  34. de Araujo ED, Erdogan F, Neubauer HA, Meneksedag-Erol D, Manaswiyoungkul P, Eram MS, et al. Structural and functional consequences of the STAT5B(N642H) driver mutation. Nat Commun. 2019;10(1):2517.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Herr AB. Evolution of an allosteric “off switch” in apoptotic caspases. J Biol Chem. 2018;293(15):5462–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Aleshin A, Finn RS. SRC: a century of science brought to the clinic. Neoplasia (New York, NY). 2010;12(8):599–607.

    Article  CAS  Google Scholar 

  37. Alvarez RH, Kantarjian HM, Cortes JE. The role of Src in solid and hematologic malignancies. Cancer. 2006;107(8):1918–29.

    Article  CAS  PubMed  Google Scholar 

  38. Kim EK, Choi E-J. Compromised MAPK signaling in human diseases: an update. Arch Toxicol. 2015;89(6):867–82.

    Article  CAS  PubMed  Google Scholar 

  39. Chen S, Li F, Xu D, Hou K, Fang W, Li Y. The function of RAS mutation in cancer and advances in its drug research. Curr Pharm Des. 2019;25(10):1105–14.

    Article  CAS  PubMed  Google Scholar 

  40. Wong RSY. Apoptosis in cancer: from pathogenesis to treatment. J Exp Clin Cancer Res. 2011;30(1):87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ghobrial IM, Witzig TE, Adjei AA. Targeting apoptosis pathways in cancer therapy. CA Cancer J Clin. 2005;55(3):178–94.

    Article  PubMed  Google Scholar 

  42. Crawford ED, Wells JA. Caspase substrates and cellular remodeling. Annu Rev Biochem. 2011;80(1):1055–87.

    Article  CAS  PubMed  Google Scholar 

  43. Wang Y, Chu F, Lin J, Li Y, Johnson N, Zhang J, et al. Erianin, the main active ingredient of Dendrobium chrysotoxum Lindl, inhibits precancerous lesions of gastric cancer (PLGC) through suppression of the HRAS-PI3K-AKT signaling pathway as revealed by network pharmacology and in vitro experimental verification. J Ethnopharmacol. 2021;279:114399.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank the public databases from TCMSP, Chem mapper, Swiss Target Prediction, GeneCards, DisGeNET, DrugBank, UniProt and KEGG pathway database.

Funding

This study was supported by Jiangsu Provincial Medical Youth Talent (QNRC2016019), “333” Project of Jiangsu Province (LGY2018065), Jiangsu Provincial Hospital of Traditional Chinese Medicine Academic Talent Program (Y2018RC33); A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) (ZYX03KF019, ZYX03KF021, ZYX03KF029). Postgraduate Research &Practice Innovation Program of Jiangsu Province (SJCX21_0692).

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Authors and Affiliations

  1. China Pharmaceutical University, Nanjing Drum Tower Hospital, Nanjing, 210008, Jiangsu Province, China

    Chao Wang & Yali Jing

  2. Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China

    Ruijuan Zhang, Xu Chen & Mengyun Yuan

  3. Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China

    Jian Wu & Qingmin Sun

  4. Department of Gastroenterology, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, 224299, Jiangsu, China

    Chunrun Miao

  5. Department of Endocrinology, Drum Tower Hospital Affiliated to Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China

    Yali Jing

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  1. Chao Wang
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  2. Ruijuan Zhang
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  3. Xu Chen
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  6. Qingmin Sun
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  7. Chunrun Miao
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  8. Yali Jing
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Contributions

YL Jing and CR Miao designed and supervised the experiments; C Wang conducted the experiments and drafted the manuscript; RJ Zhang, X Chen, MY Yuan, J Wu carried out some experiments and evaluated the data; QM Sun revised this paper. All authors gave their approval to the final manuscript version.

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Correspondence to Chunrun Miao or Yali Jing.

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Supplementary Information

Additional file 1:

Supplementary Table 1. The information of SSA-related targets. Supplementary Table 2. The information of GC-related targets. Supplementary Table 3. The KEGG terms of therapy target genes and their corresponding count, corrected p-values, and gene count.

Additional file 2:

Supplementary Figure 1. The effect of SSA on migration of GC cells. 

Additional file 3.

Original WB Information.

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Wang, C., Zhang, R., Chen, X. et al. The potential effect and mechanism of Saikosaponin A against gastric cancer. BMC Complement Med Ther 23, 295 (2023). https://doi.org/10.1186/s12906-023-04108-3

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  • DOI: https://doi.org/10.1186/s12906-023-04108-3

Keywords

  • Network pharmacology
  • Saikosaponin A
  • Gastric cancer
  • Apoptosis

BMC Complementary Medicine and Therapies

ISSN: 2662-7671

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