Molecular mechanism of Saikosaponin-d in the treatment of gastric cancer based on network pharmacology and in vitro experimental verification

doi:10.21203/rs.3.rs-4002897/v1

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Research Article

Molecular mechanism of Saikosaponin-d in the treatment of gastric cancer based on network pharmacology and in vitro experimental verification

https://doi.org/10.21203/rs.3.rs-4002897/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 12 Jun, 2024

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Aim

Network pharmacology combined with cellular experiments to research the mechanism of action of Saikosaponin-d in the treatment of gastric cancer.

Methods

Drug target genes were obtained from the PubChem database and the Swiss Target Prediction database. Additionally, target genes for gastric cancer were obtained from the GEO database and the Gene Cards database. The core targets were then identified and further analyzed through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and GESA enrichment. The clinical relevance of the core targets was assessed using the GEPIA database. Molecular docking of drug monomers and core target proteins was performed using Auto Duck Tools and Pymol software. Finally, in vitro cellular experiments including cell viability, apoptosis, cell scratch, Transwell invasion, Transwell migration, qRT-PCR, and Western blot were conducted to verify these findings of network pharmacology.

Results

The network pharmacology analysis predicted that the drug monomers interacted with 54 disease targets. Based on clinical relevance analysis, six core targets were selected: VEGFA, IL2, CASP3, BCL2L1, MMP2, and MMP1. Molecular docking results showed binding activity between the Saikosaponin-d monomer and these core targets.

Conclusion

Saikosaponin-d could inhibit gastric cancer cell proliferation, induce apoptosis, and inhibit cell migration and invasion.

Saikosaponin-d

gastric cancer

network pharmacology

apoptosis

migration

invasion

Gastric cancer is a type of carcinoma that originates from the gastric mucosal epithelium. It has a complex pathogenesis that is closely associated with factors such as Helicobacter pylori infection, age, salt concentration, and lack of fruits and vegetables (Smyth et al., 2020). The five-year survival rate for early stomach carcinoma is over 90%, but there is a 50% chance of recurrence and metastasis within one year after surgery. The growth rate reaches 70% within two years for less severe cases of gastric cancer (Mehlen & Puisieux, 2006). Currently, surgery is the primary treatment for stomach carcinoma, followed by radiochemotherapy as the second option. However, chemotherapy often results in various side effects, including gastrointestinal symptoms, bone marrow suppression, peripheral neurotoxicity, hair loss, and drug resistance (Hara et al., 2018). In recent years, Chinese medicine has been used as an alternative and adjuvant therapy. Studies have shown that herbal medicine can help reduce the adverse effects of traditional therapies, providing a more conservative treatment approach (Li et al., 2013). Therefore, it is crucial to explore the significance of traditional Chinese medicine in treating gastric tumors, which has become a significant research focus both domestically and internationally.

Bupleurum chinense refers to the dried root of the Umbrellaceae plant Bupleurum chinense, also known as Bupleurum narrow-leaved. It was first mentioned in the Shennong Baicao Classic. Bupleurum primarily contains bupleurum saponin, volatile oil, flavonoids, sterols, and other effective components. The saponide isolated from the root and stem of bupleurum is a representative bioactive ingredient, possessing antiviral, anti-inflammatory, anti-tumor, and immunomodulatory properties (Yu et al., 2017). Pharmacological experiments have demonstrated that Saikosaponin-d, found in Saikosaponin extracts, is the most biologically active monomer (Yuan et al., 2017). It exhibits significant effects in anti-tumor therapy (Zhou et al., 2021) and reversing drug resistance in tumor cells. Its mechanism of action involves inhibiting the evolution and differentiation of cancer cells, blocking the cancer cell cycle, and promoting apoptosis. However, there is limited research on the part of Saikosaponin-d in the advancements of stomach carcinoma, and its mechanism of action in gastric carcinoma remains inconclusive. Therefore, further in-depth discussions are required.

The pathogenesis of tumor-related diseases is highly complex. To better understand and combat tumors, researchers have been studying the use of herbal therapies in combination with network pharmacology approaches. In research supervised by Jihan Huang et al., the medicinal effects of Huanglian Jiedu Decoction were investigated for the treatment of hepatocellular carcinoma. The study utilized network pharmacology and laboratory findings to conclude that HLJDD exhibits anti-tumor activity against hepatocellular carcinoma cells. This activity is characterized by inducing cell apoptosis, blocking cell cycle, and inhibiting the migration and invasion of hepatocellular carcinoma cells (Huang et al., 2020). Similarly, Zefeng Wang et al. explored the potential of shiksin as a treatment for colorectal carcinoma using network pharmacology and cell experiments. Their findings suggested that shiksin promotes apoptosis and inhibits the activity of colorectal carcinoma cells via the PI3K-Akt axis (Wang et al., 2022). Bingjie Huo et al. investigated the effects of dandelion alcohol intervention on gastric carcinoma. Their findings demonstrated that dandelion alcohol effectively suppressed the propagation and metastasis of gastric carcinoma. It also disrupted the G1 phase cycle and induced apoptosis, thereby exerting an anti-cancer effect (Huo et al., 2022). Shuxian Yu et al. examined the impact of Polyphyllin I intervention in hepatitis B virus-associated hepatocellular carcinoma. They exerted network pharmacology and vitro experiments inquiry to elucidate underlying mechanism and found that Polyphyllin I inhibited the proliferation of hepatitis B virus-related liver cancer by promoting carcinoma cell apoptosis (Yu et al., 2022). Linlin Chen et al. explored the direct inhibitory impact of Saikosaponin-d on STAT3 in reducing cancer cell cachexia. Their mechanistic studies revealed that Saikosaponin-d impeded STAT3 phosphorylation and transcriptional activity, thereby counteracting its inhibitory effect on muscular tube atrophy. Consequently, the progression of cancer cachexia was inhibited (L. L. Chen et al., 2023). The aforementioned studies shed light on the mechanism of function and therapeutic impact of herbal compounds and monomer-based network pharmacology in cancer treatment. Thereby, this research further plumbs the specific function of the Saikosaponin-d monomer in the treatment of stomach carcinoma.

With the application of biomedical big data and the development of artificial intelligence, network pharmacology research and cellular experimental validation have combined traditional Chinese medicine with biosignature analysis to plumbs the effective ingredients and potential targets of drug therapy for tumor treatment. Traditional Chinese medicines and their monomers have unique advantages in treating tumors. They can induce apoptosis, inhibit tumor metastasis and angiogenesis, reverse multidrug resistance, regulate the body's immune system, and reduce the toxic side effects of radiotherapy (Yan et al., 2017). Gastric cancer has a complex etiology and pathogenesis, involving multiple factors, genes, and pathways. The multi-level, multi-target, and multi-pathway characteristics of traditional Chinese medicine in treating gastric cancer offer new approaches and references for research. In this research, we used network pharmacology and cellular experimental validation to predict the targets of Saikosaponin-d monomer against gastric cancer. We constructed a PPI network map and conducted GO and KEGG analyses. Furthermore, we identified core targets through molecular docking validation and clinical relevance analysis. Finally, by combining the results of cellular experiments, we elucidated the molecular mechanism of Saikosaponin-d's anti-tumor action. This research provides a theoretical foundation and scientific basis for future related studies. The flow chart is shown in Fig. 1.

2.1 Network pharmacology analysis

2.1.1 Prediction and acquisition of Saikosaponin-d monomeric targets

Saikosaponin-d was searched for its bioactivity data from the chemical module PubChem (https://pubchem.ncbi.nlm.nih.gov/) website. The 'Canonical SMILES' of Saikosaponin-d was then used to predict the corresponding targets in the Swiss Target Prediction (http://www.swisstargetprediction.ch/) repository. The retrieved targets of Saikosaponin-d were converted into corresponding gene names using UniProt (https://www.uniprot.org/).

2.1.2 Target prediction, acquisition and screening of DEGs for gastric cancer

The GEO repository (https://www.ncbi.nlm.nih.gov/) was searched for gene chips related to gastric cancer using the keyword 'gastric cancer'. The gene chip dataset GSE49051 was downloaded, and the GEO2R search site (https://www.ncbi.nlm.nih.gov/geo/geo2r/) was exerted to divide the gene microarray data set into a gastric cancer group and a normal group. Differentially expressed genes were identified by setting the conditions as Pvalue < 0.05 and |LogFC| > 1. Genes with LogFC > 1 were considered up-regulated genes, while genes with LogFC < -1 were considered down-regulated genes (Li et al., 2023). The up- and down-regulated genes were then intersected with the predicted gastric cancer target genes from Gene Cards (https://www.genecards.org/), respectively, to obtain the final set of differentially expressed genes. Differential gene volcanoes were generated using GraphPad Prism 9 software. The Venn chart revealing the intersection of differential genes from stomach carcinoma and Saikosaponin-d target genes was created using Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/). An equal-scale Venn chart manifesting the intersection of up- and down-regulated genes and Saikosaponin-d targets was plotted using the Microlife Letter online platform (http://www.bioinformatics.com.cn/). Heat maps and bar charts were generated to visualize the intersecting differential genes.

2.1.3 Construction of interaction networks between target proteins and screening of key targets

The intersecting objectives of monomeric Saikosaponin-d and gastric cancer were analyzed using the STRING 11.5 website (https://cn.string-db.org/) to create protein-protein interaction (PPI) networks and visualize the results. The downloaded files in tsv format were imported into CytoScape 3.9.1, and the CytoNCA instruction was used to filter the core targets based on their degree values and construct PPI network maps. The degree values of the core targets were represented as bars in the GraphPad Prism 9 software.

2.1.4 GO biofunctional analysis and KEGG pathway enrichment analysis

The intersecting objectives were analyzed using DAVID (https://david.ncifcrf.gov/tools.jsp) for GO and KEGG analysis. The top five biological processes (BP), molecular functions (MF), cellular composition (CC), and the first 15 KEGG signaling pathways were selected based on their P-values. The results of the analysis were presented in bubble plots using an online microbiology platform, following the screening criteria of enrichment, p-value, and count values. The identified KEGG signaling pathways and their enriched target genes were used to create a pathway-target network analysis map using Cytoscape 3.9.1 software.

2.1.5 Genomic enrichment analysis (GSEA)

The genes in the GSE49051 dataset file were conditioned to logFC values to sort the original genes for GSEA enrichment analysis using the Microlife Letter online platform.

2.1.6 Clinical relevance analysis

The core target genes were explored employing the GEPIA (http://GEPIA.cancer-pku.cn//) repository for gastric cancer and paracancer correlation analysis, grading and staging analysis, copy coefficient analysis, and survival prognosis analysis. The findings were displayed in the view.

2.1.7 Molecular docking

The mol2 files of the monomer Saikosaponin-d were copied through the TCMSP (https://tcmsp-e.com/) database. The 3D structure files of the final six core target proteins were obtained from the PDB (https://www.rcsb.org/) database. Water molecules and small-molecule ligands of the proteins were deleted applying Pymol. The files were then imported into Auto Duck Tools 1.5.7 software to hydrogenation, followed by molecular docking of the receptors and ligands. The findings were displayed applying Pymol.

2.2 Experimental verification

2.2.1 Cell culture

Gastric cancer cells AGS (CL-0022) and HGC-27 (CL-0107) were obtained from Wuhan Punosai Life Science and Technology Co., Ltd. (Wuhan, China). Gastric mucosal epithelial cells GES-1 (BNCC337969) was obtained from BeNa Culture Collection Biotechnology Co., Ltd. AGS cells were cultured in DMEM/F12 medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin mixture (R. Zhang et al., 2022). HGC-27 cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin mixture. GES-1 cells were cultured in DMEM medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin mixture. The cells were incubated in a 37℃ incubator with 5% CO2.

2.2.2 Cell viability assay

AGS cells, HGC-27 cells, and GES-1 cells in the logarithmic growth phase were digested using pancreatin and made into cell suspension. The cells were then inoculated in 96-well plates at a density of 5×10³ cells per well and cultured to an optimal state for pharmacological intervention. Different concentrations of Saikosaponin-d (6µM, 8µM, 10µM, 12µM, 14µM, 16µM, 18µM, 20µM, 22µM) were co-incubated with the cells for 12 h, 24 h, and 48 h in a 37℃ incubator, with each group having 3 replicate wells (Lin et al., 2021). After cultivation, 10 uL of CellCountingKit-8 per 100 uL of medium was co-incubated for 2 h. The OD value of each hole was read at 490 nm operating an instrument.

2.2.3 Cell apoptosis assay

Cells were vaccinated into culture plate. Once the cell grown to 80%, the supernatant was removed and replaced with a drug-containing medium for 24 h. The cells were then assimilated and collected in centrifuge tubes. The sediment was washed with pre-cooled PBS. Binding Buffer (500 uL), Annexin V-FITC (5 uL), and Propidium Iodide (5 uL) were inhaled, and the reaction was protected from light for 5–15 min. Finally, the analytes were detected and analyzed by flow cytometry within one hour (Pan et al., 2022). Each group had three replicates.

2.2.4 Wound healing assay

Cells were vaccinated into culture plate. Once the cell grown to 90%, a 10 uL spike was utilized to injure the cells, which were then washed three times to eliminate any cellular debris. The cells were then co-cultured with serum-free medium including different doses of Saikosaponin-d for 24 hours. The wound healing ability was observed and assessed by capturing images of the same scratch locations at 0 and 24 hours using a cell imager (Yang et al., 2023). Three replicates were performed for each group.

2.2.5 Transwell chamber assay

In the first step, 5×10⁵ cells were inhaled in serum-free medium with varying concentrations of Saikosaponin-d. Then, 200 uL of the cell suspension was added to the upper chamber (conventional chamber for migration trial and Matrigel-coated chamber for invasion trial) in the small chamber. In the bottom chamber, 600 uL of culture solution including 15% serum was mixed. The cultivation was carried out at 37℃ for 24 h. After that, the cells were fastened for 30 minutes and stained with 1% crystal violet. Images were captured using a cell imager (H. Wang et al., 2023). Each group was replicated three times.

2.2.6 qRT-PCR

Total RNA was abstractd from cells utilising the Trizol method. cDNA was then reverse transcribed from RNA to cDNA applying the PrimeScript RT kit. Internal reference was utilized as a control for normalization, following the 2^−ΔΔCT method (H. Chen et al., 2023). qRT-PCR was performed under the reaction conditions: 95℃ for 30 s, 1 cycle; 95℃ for 5 s, 60℃ for 30 s, 40 cycles. Table 1 shows all the primer sequences.

Table 1

Primers for Qrt-PCR
Gene	Forward	Reverse
MMP2	CCTACACCAAGAACTTCCGTCTG	GTGCCAAGGTCAATGTCAGGAG
MMP1	TTACACGCCAGATTTGCCAAGAG	TCAGAGGTGTGACATTACTCCAGAG
BCL2L1	AGCAGCCGAGAGCCGAAAG	AAGAGTGAGCCCAGCAGAACC
CASP3	ACAGACAGTGGTGTTGATGATGAC	ATGGCACAAAGCGACTGGATG
IL2	ACTCACCAGGATGCTCACATTTAAG	ATTTAGCACTTCCTCCAGAGGTTTG
VEGFA	GGAGGAGGAAGAAGAGAAGGAAGAG	GCGGCTGGAGCACTGTCTG
GAPDH	CAGGAGGCATTGCTGATGAT	GAAGGCTGGGGCTCATTT

2.2.7 Western blot

Proteins were abstracted using RIPA lysate. the proteins were splited on a SDS-PAGE gel and diverted to a PVDF membrane. The membranes were then incubated with 5% skimmed milk powder for 1 h and washed three times with TBST. Following this, the membranes were incubated overnight at 4°C with the corresponding antibodies. After washing the PVDF membrane three times, the corresponding secondary antibody was entered and incubated at ordinary temperature for 1 hour (X. X. Wang et al., 2023). Finally, the bands were visualized and statistically analyzed.

2.2.8 Statistical analysis

All data were statistically analyzed utilising GraphPad Prism 8 software. The results were denoted as mean ± standard deviation. Statistical differences were determined using one-way ANOVA and T-tests. The p-value of less than 0.05 was considered statistically significant.

3.1 Network pharmacology analysis

3.1.1 Prediction and acquisition of Saikosaponin-d monomeric targets and gastric cancer targets

The Canonical SMILES of Saikosaponin-d were obtained from the Pubchem website. A total of 107 targets were identified by removing null values and weighting the predicted targets in the Swiss Target Prediction database. To predict gastric cancer target genes, the GSE49051 GeneChip dataset from the GEO database will be used. The predicted gastric cancer target genes in the Gene Cards database will be intersected with the selected dataset to obtain 3535 differentially expressed genes. Among these, 3292 genes were up-regulated and 243 genes were down-regulated, as shown in Figure 2A.

3.1.2 Acquisition of Saikosaponin-d and gastric cancer intersection target genes

The 54 intersecting target genes were obtained by comparing the differential genes of gastric cancer with the target genes of Saikosaponin-d monomer. Fig.2B, 2C, and 2D show the Venn diagram, heat map, and volcano diagram, respectively. Among the intersecting target genes, 49 were up-regulated and 5 were down-regulated. Bar graphs were drawn using the first 10 and first 5 target genes, as shown in fig.2E and 2F, respectively.

3.1.3 Construction of interaction networks between target proteins and screening of core targets

The 54 intersecting target genes were analyzed in the STRING database, resulting in 54 nodes and 203 edges. Fig.3A illustrates this, including 4 free target genes. After removing the free target genes, Cytoscape software was used to obtain 50 associated target genes. By setting the filtering condition to a degree value > 8, 17 core target genes were identified, as shown in Fig.3B. These 17 core target genes were then visualized in a bar chart, considering degree information, similar to the example in Fig.3C.

3.1.4 GO biofunctional enrichment analysis

The 54 intersecting target genes were enriched with GO function using the DAVID database. A total of 205 BP items, 43 CC items, and 53 MF items were obtained. The top 5 entries with the lowest P-values were selected and plotted in bubble diagrams, as illustrated in Fig.4A. These entries were primarily related to cell migration, apoptosis, and protein hydrolysis.

3.1.5 KEGG pathway enrichment analysis and construction of pathway-target network interactions

The 54 intersecting target genes were analyzed for KEGG pathway enrichment using the DAVID database. A total of 101 relevant pathways were identified, and the top 15 pathways with the highest P-value values were selected to create a bubble map (Fig.4B). The KEGG pathway bubble diagram revealed that the pathway with the highest number of enriched cells was 'Pathways in Cancer.' This pathway was primarily associated with apoptosis, inhibition of tumor angiogenesis, and inhibition of cell migration and invasion. To further explore the interactions between the top 15 pathways and their enriched target genes, the data was imported into Cytoscape software, and a pathway-target network was constructed (Fig.4C).

3.1.6 Genome enrichment analysis (GSEA)

The genes in the GSE49051 dataset file underwent GSEA enrichment analysis, revealing that the target genes were primarily associated with gastric cancer through complement and coagulation as well as cytokine receptor interactions, as depicted in Fig.4D.

3.1.7 Clinical relevance analysis

The 17 core target genes were analyzed for clinical relevance using the GEPIA database. From this analysis, six genes were identified as having clinical relevance in the therapy of stomach carcinoma: VEGFA, IL2, CASP3, BCL2L1, MMP2, and MMP1. Among these genes, MMP2 showed high expression in stomach carcinoma progression. The overall survival curves of individuals with stomach carcinoma who had higher expression of VEGFA and MMP2 were found to be lower than those with lower expression, as depicted in Fig.5A-D.

3.1.8 Molecular docking

The molecular docking of Saikosaponin-d with six core target proteins (VEGFA, IL2, CASP3, BCL2L1, MMP2, and MMP1) was verified using Auto Duck Tools software. The docking findings were visualized using Pymol, as shown in Fig.5E-J. The corresponding binding energy values were represented using trilinear tables and heat maps, with low energies indicated by the blue region and high energies indicated by the red region, as depicted in Fig.5K and 5L.

3.2 Experimental results

3.2.1 Saikosaponin-d inhibits cell viability

AGS, HGC-27, and GES-1 cells were treated with different concentrations of Saikosaponin-d (6 μM, 8 μM, 10 μM, 12 μM, 14 μM, 16 μM, 18 μM, 20 μM, 22 μM) for 12 h, 24 h, and 48 hours. CCK8 datas revealed that Saikosaponin-d significantly reduced the cell viability of stomach carcinoma cells. The inhibitory effect of the drug increased gradually with higher concentrations and longer treatment times. GES-1 cells showed the highest cell viability, but their proliferation was significantly inhibited after 24 h of drug intervention in AGS and HGC-27 cells. Therefore, a 24 h time period was chosen for drug intervention, and the low, medium, and high concentrations of the drug for stomach carcinoma cells were defined as follows: AGS (11 μM, 13 μM, 15 μM) and HGC-27 (14 μM, 16 μM, 18 μM). (Fig.6-A, B and C)

3.2.2 Saikosaponin-d promotes cell apoptosis

The outcomes of apoptosis study displayed that Saikosaponin-d significantly increased the rate of apoptosis. After 24 hours of Saikosaponin-d intervention at low, medium, and high concentrations, the apoptosis rates of AGS cells were (3.98±2.42)%, (10.5±1.98)%, and (17.7±2.81)% (Fig.6-D, E), respectively. These rates were higher compared to the control group (1.63±1.10%) (P<0.01). Similarly, the apoptosis rates of HGC-27 cells after 24 hours at low, medium, and high concentrations were (1.21±6.18)%, (1.43±8.98)%, and (3.53±43.2)%, respectively, which were also more than those of the control (1.21±4.96)% (P<0.05) (Fig.6-G, F).

3.2.3 Saikosaponin-d inhibits cellular wound healing

The impact of Saikosaponin-d on the horizontal migratory capacity of cells was assessed utilising the wound scratch test. The findings revealed that the number of cells entering the 'wound zone' increased over time after 24 hours of culture, compared to the initial measurement at 0 hours. Saikosaponin-d intervention at low, medium, and high concentrations in stomach carcinoma cells prominently hindered the wound healing ability of the cells, as compared to the control (P<0.001) (Fig.7-A, B, C, and D).

3.2.4 Saikosaponin-d inhibits cell migration and invasion

The influence of Saikosaponin-d on gastric cancer cell migration and invasion were assessed by observing changes in cell number using Transwell chambers. The outcomes indicated that Saikosaponin-d markedly decreased the number of AGS and HGC-27 cells (P<0.001), indicating its interference with cell migration (Fig.8-A, B, C, and D). Furthermore, Saikosaponin-d was found to reduce the invasive ability of AGS and HGC-27 cells as its concentration increased (P<0.001) (Fig.8-E, F, G, and H). These findings suggest that Saikosaponin-d exhibits strong cytotoxic effects and potential anti-tumor properties on gastric cancer cells, effectively inhibiting tumor metastasis even at low concentrations.

3.2.5 qRT-PCR

The outcomes manifested that the mRNA levels of BCL2L1, VEGFA, MMP1, and MMP2 were prominently reduced (P<0.001) in Saikosaponin-d intervened AGS cells compared to the control. Additionally, the mRNA levels of CASP3 and IL-2 were noticeably increased (P<0.05) (Fig.9-A).

3.2.6 Western Blot

To define the influence of Saikosaponin-d on apoptosis and cell migration and invasion, a Western Blot assay was conducted. The outcomes indicated that Saikosaponin-d significantly decreased the protein expression of apoptosis pathway-related proteins Caspase 3, Cleaved-Caspase 3, and BAX (P<0.05), while increasing the protein expression of BCL-2 (P<0.05) in AGS cells after 24 hours of intervention. Moreover, Saikosaponin-d also significantly reduced the protein expression of migration- and invasion-related proteins MMP1 and MMP2 (P<0.01). Additionally, the network pharmacologically enriched core protein IL-2 expression increased, and VEGFA expression decreased (P<0.05) in response to Saikosaponin-d (Fig.9-B, C, D, and E).

This study aimed to predict the potential associations between Saikosaponin-d and gastric cancer using network pharmacology. A total of 54 intersecting targets and 101 related pathways of Saikosaponin-d with gastric cancer were collected. Through PPI network interactions, the core targets identified were AKT1, VEGFA, STAT3, IL2, CASP3, MMP2, and others. The GO function and KEGG analyses showed that the 'Pathways in cancer' signaling pathway had the highest number of differentiated genes, indicating its potential role in exerting anti-gastric cancer effects. By analyzing the clinical relevance of the pathway-enriched targets in gastric cancer, six final genes were identified: VEGFA, IL2, CASP3, BCL2L1, MMP2, and MMP1. These genes collectively functioned as anti-tumor agents. The 'Pathways in cancer' signaling pathway encompasses Caspase-mediated apoptosis and MMP-mediated cell migration and invasion. Therefore, it is speculated that Saikosaponin-d may exhibit anti-tumor activity by inducing apoptosis and inhibiting tumor cell migration and invasion.

Saikosaponin-d interfered with AGS and HGC-27 stomach carcinoma cells, inducing apoptosis and inhibiting wound healing, migration, and invasion in a dose-dependent manner. Haoliang Ke et al. researched the promotion of apoptosis in breast carcinoma cells by the monomeric compound Weimaining, which up-regulated the expression of caspase-3 (Ke et al., 2021). Hongxia Gong studied the promotion of apoptosis in lung carcinoma cells through increased concentrations of pinocembrin, which enhanced caspase-3 activity (Gong, 2021). Min Ji Kwon et al. revealed that Alisma canaliculatum extracts induced cell death in AGS cells by elevating BAX and reducing Bcl-2, activating caspase-3-mediated apoptosis (Kwon et al., 2021). Dmitry V. Sverchinsky et al. demonstrated that BT44 significantly increased caspase-3 levels, promoting apoptosis and enhancing anti-tumor activity (Sverchinsky et al., 2018). Yu-Ju Wu et al. showed that CCL5-regulated glioma migration and invasion were closely associated with MMP2 (Yu-Ju Wu et al., 2020). Gang Liu et al. explored the inhibitory effect of the proteoglycan SPOCK2 on prostate carcinoma cell migration and invasion by decreasing MMP2 expression and protein activation (Liu et al., 2019). Yue Liu et al. verified that the suppressive impact of quercetin on esophageal carcinoma cell invasion and angiogenesis was related to reduced MMP2 and VEGFA expression (Liu et al., 2021). Shengqun Luo et al. experimentally demonstrated that niflumic acid exerted anti-tumor effects by inhibiting MMP2 activity-mediated migration and invasion (Luo et al., 2015). In contrast, the present research employed PPI network interactions, GO and KEGG functional enrichment analysis, GSEA analysis, clinical relevance analysis, and molecular docking validation. These analyses indicated that the anti-tumor effect of Saikosaponin-d may be associated with Caspase-3-mediated apoptosis, as well as MMP1 and MMP2-mediated migration and invasion.

The results of related literature indicate that drugs exert their therapeutic effects on tumors through different mechanisms of action. Shibo Wu et al. demonstrated that Saikosaponin-d inhibits lung cancer cell proliferation and promotes apoptosis by suppressing the STAT3 pathway and activating the Caspase-3-mediated signaling pathway (Wu et al., 2020). Xiaojing Zhang et al. indicated that total saponins of Saikosaponin-d induce apoptosis in human colon carcinoma cells in a dose-dependent manner by modulating the PI3K/AKT axis and activating the Caspase-9/Caspase-3 cascade (X. Zhang et al., 2022). Jianran Hu et al. indicated that Saikosaponin-d increases caspase-3 to promote apoptosis and inhibits the NF-κB axis to suppress the migration and invasion of gastric cancer cells (Hu et al., 2021). Tong-Tong Tang et al. demonstrated that Saikosaponin-d mediates the MAPK axis to block endometrial cancer cell migration and invasion and activates the cascade of Bcl-2 and caspase families to promote apoptosis (Tang et al., 2023). The findings of the present research also reveal that the mechanism of Saikosaponin-d treatment in gastric cancer may be closely related to apoptosis mediated by Caspase-3 and migration and invasion mediated by MMP1 and MMP2.

This study employed network pharmacology and cellular experiments to predict and validate the potential targets and molecular mechanisms of Saikosaponin-d for treating gastric cancer. The next step involves conducting in vivo tumor formation experiments in nude mice to verify the efficacy of Saikosaponin-d and supply a more credible foundation for its therapy of gastric cancer through in vitro and in vivo double validation. Additionally, gene silencing of the core target will be performed using lentiviral transfection. Furthermore, high-throughput screening methods such as proteomics, genomics, and transcriptomics can be employed in conjunction with low-throughput methods to validate the obtained conclusions. Finally, a dual luciferase reporter system and chromatin immunoprecipitation methods were utilized to investigate whether the active ingredient can enter the nucleus and interfere with transcription factors, thereby participating in DNA transcription. These findings offer new opinion and contribute to a deeper understanding of the mechanism by which Saikosaponin-d treats gastric cancer. The graphical abstract is shown in Fig. 10.

Ethical approval: Not applicable.

Consent to participate: Not applicable.

Consent for publication: Not applicable.

Funding: This research was funded by Ningxia Key Research and Development Program (No.2023BEG02015), High-level Key Discipline Construction Project of State Administration of Traditional Chinese Medicine (No.2022-226) and Talent Development Projects of Young Qihuang of National Administration of Traditional Chinese Medicine (2020).

Data availability: Not applicable.

Author Contribution

All authors have contributed to the research of concepts and designs. N.N., Y.N., X.Y.L., S.C.H., and G.Q.C. contributed to the structure and ideas of the paper. N.N., Y.H.D., Q.G. and W.Q.L. carried out Document retrieval and sorting analysis. The manuscript was written by N.N. L.Y. and Y.N. revised the manu-script. All authors have read and agreed to the published version of the manuscript.

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Molecular mechanism of Saikosaponin-d in the treatment of gastric cancer based on network pharmacology and in vitro experimental verification

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Abstract

Aim

Methods

Results

Conclusion

Figures

1 Introduction

2 Materials and methods

2.1 Network pharmacology analysis

2.1.1 Prediction and acquisition of Saikosaponin-d monomeric targets

2.1.2 Target prediction, acquisition and screening of DEGs for gastric cancer

2.1.3 Construction of interaction networks between target proteins and screening of key targets

2.1.4 GO biofunctional analysis and KEGG pathway enrichment analysis

2.1.5 Genomic enrichment analysis (GSEA)

2.1.6 Clinical relevance analysis

2.1.7 Molecular docking

2.2 Experimental verification

2.2.1 Cell culture

2.2.2 Cell viability assay

2.2.3 Cell apoptosis assay

2.2.4 Wound healing assay

2.2.5 Transwell chamber assay

2.2.6 qRT-PCR

2.2.7 Western blot

2.2.8 Statistical analysis

3 Results

4 Discussion

Declarations

Author Contribution

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1