Network pharmacology and experimental verification based research into the effect and mechanism of Aucklandiae Radix–Amomi Fructus against gastric cancer

To investigate the mechanism of the Aucklandiae Radix–Amomi Fructus (AR–AF) herb pair in treating gastric cancer (GC) by using network pharmacology and experimental verification. Using the traditional Chinese medicine system pharmacology database and analysis platform (TCMSP), the major active components and their corresponding targets were estimated and screened out. Using Cytoscape 3.7.2 software, a visual network was established using the active components of AR–AF and the targets of GC. Based on STRING online database, the protein interaction network of vital targets was built and analyzed. With the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server, the gene ontology (GO) biological processes and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways of the target enrichment were performed. AutoDock Vina was used to perform molecular docking and calculate the binding affinity. The mRNA and protein expression levels of the hub targets were analyzed by the Oncomine, GEPIA, HPA databases and TIMER online tool, and the predicted targets were verified by qRT–PCR in vitro. Eremanthin, cynaropicrin, and aceteugenol were identified as vital active compounds, and AKT1, MAPK3, IL6, MAPK1, as well as EGFR were considered as the major targets. These targets exerted therapeutic effects on GC by regulating the cAMP signaling pathway, and PI3K-Akt signaling pathway. Molecular docking revealed that these active compounds and targets showed good binding interactions. The validation in different databases showed that most of the results were consistent with this paper. The experimental results confirmed that eremanthin could inhibit the proliferation of AGS by reducing the mRNA expression of hub targets. As predicted by network pharmacology and validated by the experimental results, AR–AF exerts antitumor effects through multiple components, targets, and pathways, thereby providing novel ideas and clues for the development of preparations and the treatment of GC.


Molecular Docking
www.nature.com/scientificreports/ closeness centrality (CC) were taken as reference standards, the top ten targets were selected as hub targets. The DAVID database (https:// david. ncifc rf. gov/) 27 was adopted to conduct GO and KEGG enrichment analyses.

Validation of hub targets in different databases.
Oncomine (https:// www. Oncom ine. org) 28 was used to compare the differential expression of hub targets in GC tissues and normal gastric tissues. The genetic information of the correlation between mRNA expression and DNA methylation was conducted by cBioPortal tool (http:// www. cbiop ortal. org/) 29 . The hub targets were input into the online tool GEPIA (http:// gepia. cancer-pku. cn/ index. html) 30 to verify their mRNA expression level, pathological stages and overall survival (OS) in TCGA-STAD. Immunohistochemistry (IHC) was performed in the HPA (https:// www. prote inatl as. org/) 31 .
Molecular docking. The PDB database 32 (https:// www. rcsb. org/) was used to search the PDB files of the hub target proteins. The resolution of the crystal structures to achieve and carry out molecular modeling is 2.5-3.0 A. The PDB IDs corresponding to the hub target proteins selected in this study are AKT1 (PDB ID: 1UNQ 33 39 ). The SDF files of the main compounds with high degree were downloaded from the PubChem database (https:// www. ncbi. nlm. nih. gov/ pccom pound/) as candidate docking medicinal ingredients, and the docking simulation calculation of molecules and target proteins was carried out by the molecular docking software AutoDock Vina 1.5.6 40 . The protein was processed using AutoDock Vina 1.5.6 software by separating the protein, adding nonpolar hydrogen, calculating the Gasteiger charge, assigning the AD4 type, and setting all the flexible bonds of the small molecule ligands to be rotatable. The receptor proteins were set as rigid docking, and a genetic algorithm was selected with the maximum eval number as the medium. Before docking, ChemBioDraw 3D was used to generate 3D chemical structures and minimize energy for all selected ligands. Grid box parameters can be seen in Supplementary Table S3. The docking results were obtained by running autogrid4 and autodock4, revealing the binding energy. The relatively stable results of molecular docking were selected to draw a 3D map with PyMol software 41 . The best docking poses were identified as those showing the smallest root mean square deviation (RMSD) between the predicted conformation and the observed X-ray crystallographic conformation. Models with an RMSD ≤ 4 Å were considered reliable and those with an RMSD ≤ 2 Å were considered accurate 42 . In this paper, models with an RMSD ≤ 2 Å were considered.
Immune cell infiltrates of hub targets. The efficacy of immune checkpoint inhibitors is affected by many factors. To illuminate the underlying mechanism of the immune microenvironment of GC, we used the TIMER online tool (https:// cistr ome. shiny apps. io/ timer/) 43 to investigate the effect of somatic cell copy number (CNA) of the hub targets on different kinds of immune cell infiltration and the association between hub targets and the immune infiltrate level.

Experimental verification in vitro.
Cell cultures and cell viability measurements. AGS cells were cultured in F12K medium (Gibco, MD, USA) supplemented with 5% fetal bovine serum (FBS) (Gibco, MD, USA) and 100 U of penicillin G with 100 μg of streptomycin per ml. Cells were incubated at 37 °C under 5% CO2 in a humidified atmosphere. AGS cells were inoculated into 96-well plates at a cell density of 5 × 10 4 cells/well and cultured for 24 h. Different concentrations of eremanthin (0, 5,10,20,30,40,50,60, 80 μmol/L) were added for 24 h. The inhibition rate of the cells was determined using the CCK8 kit to explore whether eremanthin had a toxic effect on the cells. Finally, optical density (OD) measurements were performed at 560 nm with microplate reader.
Quantitative real-time polymerase chain reaction (qRT-PCR). According to the molecular docking results, eremanthin with a good docking effect was selected as the main compound for experimental verification. On the basis of network pharmacology, we selected the top five targets, AKT1, MAPK3, IL6, MAPK1, and EGFR to explore the mechanism. Total RNA was extracted from cultured cells using TRIzol according to the manufacturer's instructions and then reverse transcribed using the RevertAid RT Reverse Transcription Kit (Invitrogen, K1691). The primer sequences used for qRT-PCR are listed in Table 1. qRT-PCR was performed using a Ste-pOne Real-Time PCR system (Applied Biosystems).
Statistical analysis. The experimental results were statistically analyzed by GraphPad Prism software. A singlefactor ANOVA was used to compare the differences in experimental data among groups, and P < 0.05 revealed significant difference. www.nature.com/scientificreports/ pound targets as well as the potential pharmacological effects of AR-AF were visually illustrated. We identified 12,202, 3168 and 472 GC-related targets from GeneCards, DisGeNET and OMIM, respectively, which resulted in 12,844 targets after merging and removing duplicate targets (Fig. 3A). A total of 482 target genes were identified to be affected by GC and regulated by AR-AF, which are more critical to treat GC (Fig. 3B). To clarify the function of the estimated protein targets, GO and KEGG enrichment analyses were conducted. The top 20 noticeably enriched GO and KEGG terms are listed in Fig. 5. As suggested from the results, the targets of AR-AF displayed tight relations to the major biological process, which included signal transduction, protein phosphorylation, positive regulation of transcription from the RNA polymerase II promoter, and the G-protein coupled receptor signaling pathway. Through KEGG enrichment analysis, AR-AF probably exerted therapeutic effects on GC by regulating signaling pathways, which included the cAMP signaling pathway, and PI3K-Akt signaling pathway. To illustrate the relationship between the AR-AF herb pair and their corresponding compound targets and GC targets, a compound-target-pathway network was built (Fig. 6).

Molecular docking.
Molecular docking is mainly used to investigate the molecular recognition between small molecules and target proteins and to identify the binding of the molecules with the active sites of the target 44 . If the binding energy of docking is negative, it indicates that the small molecule can effectively and  It is generally believed that the lower the energy when the conformation of ligand-receptor binding is stable, the more likely the effect will be 45 . A binding energy < − 5 kcal mol −1 indicates good binding activity, and a binding energy < − 7 kcal mol −1 indicates strong binding activity. The results of molecular docking were plotted as a heatmap, as shown in Fig. 7. It can be seen from the figure that cynaropicrin, 2-[(2r, 4ar, 8as)-4a-methyl-8-methyl-decalin-2-yl] acrylic acid, and eremanthin with IL6, TNF, and APP all had good activities, and the binding affinity is shown in Supplementary Table S2. The docking results of eremanthin with good activity were selected and visualized by PyMol software. In general, the calculated interactions of experimental structures of ligand-protein complexes have two different regions of high probability density. The first identifiable geometry is described by the relatively close distance between the centers of the aromatic rings (3.5-4.5 Å) between the ring plane normal vector, with a range of parameters corresponding to parallel aromatic hydrocarbon accumulation. The second dense region described by the longer distance and the near right angle of 5-6 Å corresponds to the vertical aromatic interaction 46 . In this paper, the interaction between ligands and proteins is aromatic accumulation. Eremanthin formed π-π interactions with ASN-53 , and THR-87 (2.5 Å) to form π-π interactions that resulted in binding to AKT1 (1UNQ) (Fig. 8A). Similarly, eremanthin was predicted to pair into the binding pocket of MAPK3 (6GES) by hydrogen bonding to LYS-224 (4.0 Å) (Fig. 8B). Eremanthin was also predicted to dock in the binding pocket of IL6 (4O9H) by 2 hydrogen bonds to TYR-34 (2.3 Å) and SER-53 (2.1 Å) (Fig. 8C). Eremanthin formed a π-π interaction with ASP-106 (3.3 Å) through hydrogen bonding with TRP-108 (2.8 Å) in the binding pocket of MAPK1 (6OPH) (Fig. 8D). Eremanthin was in the binding pocket of EGFR (7JXP) by multiple hydrogen bonds with PHE-723 (3.4 Å), ARG-748 (3.0 Å), and GLU-749 (2.7 Å) (Fig. 8E). Eremanthin bonded to SRC (6E6E) through

Validation of hub targets in different databases.
Oncomine showed that IL6, MAPK1, CXCL8, and APP were all expressed statistically significantly in GC. Although AKT1, MAPK3, SRC, and CASP3 were not expressed in GC, the analysis showed that they were expressed in other tumors (Fig. 9).
The cBioPortal tool showed that 124 of 434 patients with gastric adenocarcinoma (29%) had genetic mutations in these targets (Fig. 10A). An overview of the genetic variation of hub targets was also analyzed (Fig. 10B). Figure 10C shows the correlation between the mRNAs and proteins of the hub targets.
The mRNA levels of MAPK1, CXCL8, and CASP3 were significantly higher in GC tissues (Fig. 11A). The levels of TNF changed significantly with pathological stage and increased significantly in stage II (Fig. 11B). The Kaplan-Meier survival curve (Fig. 11C) showed that the prognostic value of IL6 was significantly different (P < 0.05).
The protein expression of IL-6 and TNF between gastric cancer tissue and normal gastric tissue was the same, indicating that there was no difference. In contrast, the other eight hub targets were expressed to different degrees between normal gastric tissue and gastric cancer tissue, which means that there were significant differences. Compared with normal gastric tissue, the expression levels of MAPK3, MAPK1, EGFR, and SRC were increased in GC tissue, while the expression levels of AKT1, CXCL8, CASP3, and APP were decreased in GC tissue (Fig. 12).
Immune cell infiltrates of hub targets. The CNA of the hub target signature significantly affected the fraction of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells (DCs) in GC (Fig. 13). The association between hub target expression and the immune infiltrate is shown in Fig. 14.
There is no information about CXCL8 in TIMER. Among them, the expression level of IL6 was significantly negatively correlated with B cells (cor = − 0.256, P < 0.0001) and significantly positively correlated with neutrophils (cor = 0.265, P < 0.0001). SRC was significantly negatively correlated with macrophages (cor = − 0.208, P < 0.0001). TNF was significantly negatively correlated with purity (cor = − 0.281, P < 0.0001) and was significantly positively correlated with neutrophils and dendritic cells (cor = 0.357, P < 0.0001; cor = 0.248, P < 0.0001). The above results indicated that the hub targets play a critical role in regulating the tumor immune microenvironment of GC patients.

Experimental verification in vitro. Eremanthin inhibited the proliferation of AGS cells. Ereman-
thin was observed to inhibit AGS cell proliferation in a concentration-dependent manner. The 24 h IC50 was 25.07 μmol/L, revealing that eremanthin for the AGS cell line showed a significant reduction in IC50 values over time. The cell viability curves are shown in Fig. 15. We chose this concentration for the follow-up experiment.   Fig. 16. AKT1 is a key target of the PI3K-Akt signaling pathway. IL6 is one of the most important cytokine families in tumor occurrence and metastasis. The MAPK signaling pathway is a key pathway involved in GC. Target inhibition of EGFR expression can induce tumor cell death. The above experimental results proved that the main compound eremanthin was involved in the occurrence and development of GC.

Discussion
It has been verified that TCM has the characteristics of "multiple components, multiple targets, and multiple pathways" in the treatment of GC 6 . According to the results achieved here, 106 compounds in AR-AF with 12,844 compound targets were identified, suggesting that AR-AF exerted its pharmacological effects on treating GC via multiple targets. Eremanthin, cynaropicrin, and aceteugenol were identified as the vital main compounds with the highest degrees. Eremanthin exerts anticancer activity on human cervical cancer cells by inhibiting the PI3K/ AKT signaling pathway 47 . Studies have reported that cynaropicrin has a good antitumor effect and has a certain inhibitory effect on colorectal cancer 48 , melanoma 49 , and thyroid cancer 50 . Aceteugenol has a good antioxidant effect 51 . On the whole, it was speculated that AR-AF is a multicomponent herb pair with multitarget therapeutic effects. Associations between these active compounds and GC should be supposed to be deeply investigated.  www.nature.com/scientificreports/ GO and KEGG enrichment analyses showed that AR-AF regulates the proliferation, apoptosis, migration and angiogenesis of tumor cells by regulating signaling pathways, including the cAMP signaling pathway and PI3K-Akt signaling pathway, thereby playing a role in treating GC.
In this paper, AKT1, MAPK3, IL6, MAPK1, EGFR, SRC, TNF, CXCL8, CASP3, and APP were identified as hub targets. AKT is a serine/threonine kinase located downstream of PI3K. Activated AKT1 can induces the proliferation and activation of gastric cancer cells 52,53 . The positive rate of AKT1 expression in GC tissue is significantly higher than that in adjacent tissues, and it participates in the occurrence and development of GC 54,55 . MAPK3 is a serine/threonine protein kinase that can respond to a variety of extracellular stimuli. MAPK3 is related to tumor cell development, differentiation, apoptosis, angiogenesis, invasion and metastasis 56 . Mitogenactivated protein kinase 1 (MAPK1) has been confirmed as an essential oncogene in the progression of GC, and its level is elevated in GC tissues and cells, which can promote the proliferation, migration, and invasion of GC cells 57 . MAPK3 is a member of the MAPK family. MAPK is an important signal transmitter in cells that can participate in a variety of biological processes, such as cell proliferation, differentiation, and immune defense, by phosphorylating nuclear transcription factors and related enzymes 58 . IL6 is a pleomorphic cytokine involved in various biological processes, such as inflammation and tumor development 59,60 . Elevated IL-6 can stimulate the excessive activation of STAT3, including angiogenesis and tumor metastasis 61 . Activation of IL-6/STAT3 inhibits the transcription of miR-520f-3p, which amplifies the excessive activation of STAT3 by targeting GP130. Therefore, targeting this pathway may have the potential to treat GC 62 . EGFR is associated with gastric mucosal hyperplasia and poor prognosis of GC 63 . SRC is a serine/threonine kinase that plays an important role in the development of many solid tumors 64 . SRC is highly expressed in GC 65,66 . Activated SRC can promote tumor cell proliferation 67 . TNF-α is mainly secreted by macrophages and participates in inflammation and the immune response. Its effect on tumors is bidirectional; high concentrations of TNF-α can selectively destroy the tumor vascular system and produce specific T cells that have an antitumor effect, while low concentrations of TNF-α bind to the receptor, activate the NF-κB signaling pathway, promote cell proliferation, inhibit cell apoptosis, and promote tumor progression 68,69 . CXCL and CXCR are endogenous ligands of chemokines or members of the receptor family and are closely related to a variety of cancers 70 . Helicobacter pylori (HP) induces macrophages to release CXCL8, thereby promoting the occurrence and development of GC 71 . CASP3 is an apoptotic gene, and miR-524-5p participates in the development of GC by regulating CASP3, which may provide new prospects for the diagnosis and treatment of GC 72 . APP is the main target that we screened for the treatment of GC, but there  www.nature.com/scientificreports/ is no clear antitumor effect report, which is worthy of further study. Molecular docking validated that vital active compounds and major targets showed good binding interactions. It is suggested that the AR-AF herb pair may act on the major targets to play a key role in the treatment of GC.
The results of immune infiltrates showed that the CNA of the hub target signature significantly affected the fraction of B cells, T cells, neutrophils, macrophages and DCs in GC, which suggested that the hub targets can be used as a potential predictor of gastric cancer immunotherapy.
The experimental results in this study confirmed that the active compounds of AR-AF inhibited cell proliferation and induced apoptosis of AGS cells, thus delaying the progression of GC, which will provide a preliminary basis for future clinical trials.

Conclusion
In this study, eremanthin, cynaropicrin, and aceteugenol were identified as the vital active compounds, and AKT1, MAPK3, IL6, MAPK1, as well as EGFR were considered as the major targets. Molecular docking revealed that these active compounds and major targets showed good binding interactions. AR-AF regulates the proliferation, apoptosis, migration and angiogenesis of tumor cells by regulating THE cAMP signaling pathway, and PI3K-Akt signaling pathway. Experimental studies provided evidence that AR-AF showed therapeutic effects on GC by regulating related target proteins, inhibiting cell proliferation, and increasing the cell apoptosis rate. This study demonstrated potential pharmacological mechanisms of AR-AF acting on GC, and it can be referenced for the clinical application of AR-AF.