A Network Pharmacology Perspective Investigation of the Pharmacological Mechanisms of the Herbal Drug FDY003 in Gastric Cancer

Abstract

Gastric cancer (GC) is one of the most common and deadly malignant tumors worldwide. While the application of herbal drugs for GC treatment is increasing, the multicompound–multitarget pharmacological mechanisms involved are yet to be elucidated. By adopting a network pharmacology strategy, we investigated the properties of the anticancer herbal drug FDY003 against GC. We found that FDY003 reduced the viability of human GC cells and enhanced their chemosensitivity. We also identified 8 active phytochemical compounds in FDY003 that target 70 GC-associated genes and proteins. Gene ontology (GO) enrichment analysis suggested that the targets of FDY003 are involved in various cellular processes, such as cellular proliferation, survival, and death. We further identified various major FDY003 target GC-associated pathways, including PIK3-Akt, MAPK, Ras, HIF-1, ErbB, and p53 pathways. Taken together, the overall analysis presents insight at the systems level into the pharmacological activity of FDY003 against GC.

Keywords

herbal drugs network pharmacology gastric cancer molecular mechanisms anticancer agents

Introduction

Gastric cancer (GC) is the fourth most frequent cause of cancer mortality, at a global rate of 0.77 million deaths per year¹. The current standard treatment options for GC are chemotherapy, targeted therapy, and immunotherapy²; however, the development of resistance and occurrence of side effects limit their therapeutic success^3,4. Herbal drugs are also being widely applied as GC treatment as they exert potent anticancer activity, improve the therapeutic efficacy of standard cancer therapeutics, and ameliorate adverse events^5-8. Additionally, they can prolong survival, increase cure rate, improve prognosis, alleviate symptoms, and enhance recovery in patients with GC^5-8.

FDY003 is an anticancer herbal drug composed of Cordyceps militaris (Cm), Artemisia capillaris Thunberg (AcT), and Lonicera japonica Thunberg (LjT)^9-11. It suppresses growth and promotes apoptosis in various cancers by targeting crucial genes and proteins associated with cancerous behaviors^9-11. However, the anticancer effects of FDY003 on GC and the mechanisms involved need to be elucidated.

Network pharmacology has become one of the most efficient methodologies used in herbal drug-associated studies owing to its ability to effectively elucidate the complex multicompound–multitarget mechanisms of herbal drugs using related comprehensive pharmacological data^12-14. The polypharmacological effects of herbal drugs are coordinated through complex interactions between the chemical compounds and their diverse therapeutic targets ^12-14. Thus, network pharmacology investigates the active pharmacological compounds in herbal drugs and their disease-related therapeutic targets^12-14. This is done by analyzing the topological and functional features of diverse types of herbal drug-associated networks that consist of the interactions between their associated pharmacological components and targets^12-14. By adopting a network pharmacology strategy, we investigated the properties of FDY003 as a treatment for GC.

Materials and Methods

Cell culture

AGS human GC cells were purchased from the Korean Cell Line Bank (Seoul, Korea) and cultured in Dulbecco's modified Eagle's medium (WELGENE Inc.) supplemented with 10% fetal bovine serum (WELGENE Inc.), 100 μg/mL streptomycin, and 100 U/mL penicillin (Life Technologies Corp.) at 37°C in a humidified atmosphere containing 5% CO₂.

Preparation of FDY003

Dried Cm (6.25 g), AcT (6.25 g), and LjT (4.16 g), which were purchased from Hanpure Pharmaceuticals (Pocheon, Korea), were ground, mixed, and placed in 70% ethanol (500 mL). The herbal extract was obtained by refluxing the mixture at 80°C for 3 h. Next, the extract was filtered, purified with 80% and 90% ethanol consecutively, lyophilized at 80°C, and stored at − 20 °C. The lyophilized samples were dissolved in distilled water prior to performing the experiments.

Cell viability analysis

Cell viability was measured using a water-soluble tetrazolium salt (WST-1) assay. In total, 1.0 × 10⁴ cells were seeded in a 96-well plate and incubated at 37°C for 72 h with FDY003 and/or 5-fluorouracil (5-FU; Sigma-Aldrich). Afterward, the cells were incubated with WST-1 solution (Daeil Lab Service Co., Ltd,) for 2 h at 37°C and 5% CO₂. Absorbances at 450 nm were measured using an xMark microplate absorbance spectrophotometer (Bio-Rad).

Bioactive compounds screening

Detailed data on the chemical compounds present in FDY003 were retrieved from the Traditional Chinese Medicine Systems Pharmacology¹⁵, Anticancer Herbs Database of Systems Pharmacology¹⁶, and Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine¹⁷. Then, the pharmacologically bioactive compounds were identified based on their Caco-2 permeabilities, druglikenesses, and oral bioavailability parameters, which are widely used in network pharmacological analysis for compound screening^14,15,18. Caco-2 permeability determines whether a compound has pharmacologically suitable intestinal permeability^15,19-21, in which a value ≥ −0.4 is generally used as an indicator for strong in vivo intestinal permeability^22,23. Druglikeness is an indicator that assesses the potential of a compound to be a drug considering its molecular, physicochemical, and structural characteristics^15,24, in which a score ≥ 0.18 (the average score of all approved drugs) indicates good potential^15,24. Oral bioavailability evaluates whether an orally administered compound can successfully enter the target tissues and organs^15,25, in which a value ≥ 30% indicates effective absorption and distribution in the body^15,25. Thus, a compound was determined to be active if it satisfied the following criteria: Caco-2 permeability ≥ −0.4, druglikeness ≥ 0.18, and oral bioavailability ≥ 30%^14,15,18.

Target investigation

The simplified molecular-input line-entry system (SMILES) notations of the active compounds of FDY003 were collected from the PubChem database²⁶. Then, they were imported to SwissTargetPrediction²⁷, Search Tool for Interactions of Chemicals 5²⁸, Similarity Ensemble Approach²⁹, and PharmMapper³⁰ to identify the human targets of each compound. Information on GC-associated genes and proteins was investigated using the search keyword “gastric cancer” on the following databases: DrugBank²², Therapeutic Target Database²³, DisGeNET³¹, Comparative Toxicogenomics Database³², Online Mendelian Inheritance in Man³³, Human Genome Epidemiology Navigator³⁴, GeneCards³⁵, and Pharmacogenomics Knowledgebase³⁶.

Herbal medicine-related networks

A network is composed of nodes (eg, herbal medicines, compounds, targets, and pathways) and edges (or links) describing their pharmacological interactions and associations³⁷, in which the No. of edges of a given node is called the degree³⁷. The herbal medicine-compound-target (H-C-T) network depicts the association between the FDY003-constituting herbal medicines, their active phytochemical compounds, and the GC-associated genes and proteins targeted by the compounds. The H-C-T-pathway (H-C-T-P) network depicts the association between the targets of the H-C-T network and their related GC-associated pathways. The protein–protein interaction (PPI) network depicts the interactions between GC-associated targets using the STRING database³⁸. The networks were built, drawn, and analyzed using Cytoscape³⁹.

Survival analysis

The prognostic associations between the expression of FDY003 targets and the survival of patients with GC were computed using a Kaplan–Meier Plotter⁴⁰.

Investigation of functional enrichment for the FDY003 targets

Gene ontology (GO) and pathway enrichment for the FDY003 targets were investigated using g:Profiler⁴¹.

Analysis of molecular docking interaction

Information on the molecular structures of the active compounds of FDY003 and their targets was obtained from RCSB Protein Data Bank⁴² and PubChem²⁶. Then, the binding affinities between the compounds and their targets were evaluated based on the molecular docking scores obtained by importing their structural information into Autodock Vina⁴³. Docking scores of ≤ −5.0 indicated strong binding affinities between compound–target pairs^44,45.

Results

Pharmacological effects of FDY003 for gastric cancer

To examine the pharmacological activity of FDY003 in GS, the viabilities of AGS cells were measured after treatment with FDY003 and/or 5-FU, a chemotherapeutic agent used in clinics for GC treatment⁴⁶. FDY003 reduced the viability and increased the 5-FU sensitivity of AGS cells (Supplementary Figure S1A and B), suggesting that FDY003 exhibits anti-GC effects.

The active phytochemical compounds and targets of FDY003

The active phytochemical compounds of FDY003 were determined based on their pharmacokinetic criteria (Supplementary Table S1)^11,14,15,18. Moreover, some of them were determined to be active based on their reported potent pharmacological effects, although they did not meet the aforementioned criteria. In total, 18 FDY003 compounds were considered active (Supplementary Table S2). The targets of the active compounds were investigated using their structural information and, in total, 270 targets for FDY003 were identified, of which 70 were GC-associated targets (Supplementary Table S3).

Network-perspective pharmacological properties of FDY003 against gastric cancer

To investigate the pharmacological properties of FDY003 at the network level, we generated an H-C-T network using comprehensive FDY003-related data (Figure 1). The generated network was composed of 81 nodes (3 herbal medicines, 8 active phytochemical compounds, and 70 GC-associated targets) and 121 links (Figure 1 and Supplementary Table S3). The compounds quercetin, luteolin, and kaempferol had relatively many targets compared to other compounds (Figure 2 and Supplementary Table S3), demonstrating their potential importance in the anti-GC activities of FDY003. In addition, 78.6% of the FDY003 targets (55 of 70 targets) interacted with two or more compounds (Figure 1), demonstrating the multicompound–multitarget characteristics of the herbal drug.

Figure 1.

The herbal medicine-active phytochemical compound-target (H-C-T) network of FDY003. Green hexagons, herbal medicines; red round rectangles, active phytochemical compound; blue ellipses, gastric cancer (GC)-associated targets.

Figure 2.

The protein–protein interaction (PPI) network for gastric cancer (GC)-associated targets of FDY003. Pink ellipses, hub GC-associated targets.

As the pharmacological effects of drugs are conferred through interactions with disease-related target genes and proteins^47-49, a PPI network (60 nodes and 272 links) consisting of FDY003 targets was built, in which the large-degree hub nodes with topological significance and potential as effective therapeutic targets were investigated^50,51. Following previous studies, the hubs were considered as nodes with degrees equal to or greater than twice the average degrees of all the nodes in a network^52,53. AKT1, MAPK1, MAPK3, MAPK9, PIK3CA, STAT3, and TP53 were identified as hubs in the PPI network (Figure 2), indicating that they are key targets in the anti-GC mechanism of FDY003. The survival analysis further suggested that the targets function as determinants for survival outcome in patients with GC (Figure 3), demonstrating their clinical significance.

Figure 3.

Survival analysis of gastric cancer (GC)-associated targets of FDY003. Kaplan–Meier curves for the overall survival of patients with GC with respect to the expression status of the indicated FDY003 targets.

Functional analysis of the FDY003 mechanisms

To gain insight into the pharmacological mechanisms involved in the anti-GC activities of FDY003, GO and pathway enrichment analyses were performed on the FDY003 targets. The targets were found to potentially be involved in regulating various cellular processes, such as cellular proliferation, survival, and death (Supplementary Figure S2). Furthermore, the FDY003 targets were found to be important constituents of the following diverse GC-associated signaling pathways: “Chemokine signaling pathway”, “Epithelial cell signaling in Helicobacter pylori infection”, “Epstein-Barr virus infection”, “Erythroblastic leukemia viral oncogene homolog (ErbB) signaling pathway”, “Estrogen signaling pathway”, “Gastric cancer”, “Hypoxia inducible factor-1 (HIF-1) signaling pathway”, “Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway”, “Mitogen-activated protein kinase (MAPK) signaling pathway”, “Mammalian target of rapamycin (mTOR) signaling pathway”, “Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kappa B) signaling pathway”, “p53 signaling pathway”, “Pathways in cancer”, “Programmed death ligand 1 (PD-L1) expression and programmed death-ligand 1 (PD-1) checkpoint pathway in cancer”, “Phosphoinositide 3-kinase (PI3K)-Akt signaling pathway”, “Ras signaling pathway”, “Tumor necrosis factor (TNF) signaling pathway”, and “Vascular endothelial growth factor (VEGF) signaling pathway” (Figure 4 and Supplementary Figure S2). These findings demonstrated the molecular- and signaling-perspective mechanistic properties of FDY003 in GC treatment.

Figure 4.

The herbal medicine-active phytochemical compound-target-pathway (H-C-T-P) network of FDY003. Green hexagons, herbal medicines; red round rectangles, active phytochemical compound; blue ellipses, gastric cancer (GC)-associated targets; orange nodes, GC-associated signaling pathways.

Molecular docking analysis for the FDY003 components

To verify the binding potential of the FDY003 components, the binding affinities between the active compounds of FDY003 and their targets were measured based on molecular docking analysis. The active compounds and their direct hub targets of the herbal drug [ie, AKT1-isorhamnetin (score = −6.4), AKT1-kaempferol (score = −6.6), AKT1-luteolin (score = −7.1), AKT1-quercetin (score = −6.9), MAPK1-kaempferol (score = −7.4), MAPK1-luteolin (score = −8.1), MAPK1-quercetin (score = −7.8), MAPK3-kaempferol (score = −8.4), MAPK3-luteolin (score = −8.8), MAPK3-quercetin (score = −9.1), MAPK8-isorhamnetin (score = −8.6), MAPK8-luteolin (score = −8.2), MAPK8-quercetin (score = −8.0), PIK3CA-quercetin (score = −8.5), STAT3-kaempferol (score = −7.7), STAT3-luteolin (score = −7.5), TP53-kaempferol (score = −8.7), TP53-luteolin (score = −9.0), and TP53-quercetin (score = −9.2)] had docking scores of ≤ −5.0 (Figure 5A-S), suggesting their binding activities.

Figure 5.

Molecular docking analysis for the active phytochemical compounds of FDY003 and the gastric cancer (GC)-associated targets. (A) AKT1-isorhamnetin (score = −6.4). (B) AKT1-kaempferol (score = −6.6). (C) AKT1-luteolin (score = −7.1). (D) AKT1-quercetin (score = −6.9). (E) MAPK1-kaempferol (score = −7.4). (F) MAPK1-luteolin (score = −8.1). (G) MAPK1-quercetin (score = −7.8). (H) MAPK3-kaempferol (score = −8.4). (I) MAPK3-luteolin (score = −8.8). (J) MAPK3-quercetin (score = −9.1). (K) MAPK8-isorhamnetin (score = −8.6). (L) MAPK8-luteolin (score = −8.2). (M) MAPK8-quercetin (score = −8.0). (N) PIK3CA-quercetin (score = −8.5). (O) STAT3-kaempferol (score = −7.7). (P) STAT3-luteolin (score = −7.5). (Q) TP53-kaempferol (score = −8.7). (R) TP53-luteolin (score = −9.0). (S) TP53-quercetin (score = −9.2).

Discussion

Although the application of herbal drugs for GC treatment has gained increasing attention^5-8, their complex multicomponent–multitarget mechanistic properties have yet to be investigated. In this study, we dissected the anti-GC properties of the herbal drug FDY003 through a systems perspective^9-11. We found that FDY003 suppressed the viability and enhanced the chemosensitivity of human GC cells. Based on the network pharmacological investigation, we identified 8 active phytochemical compounds in FDY003 and 70 GC-associated therapeutic targets that may confer the pharmacological effects of the herbal drug. The GO enrichment analysis suggested that the FDY003 targets are involved in various cellular processes, such as cellular proliferation, survival, and death. We further identified various GC-associated pathways that are major pharmacological targets of FDY003, such as the PIK3-Akt, MAPK, Ras, HIF-1, ErbB, and p53 pathways. These results demonstrate the comprehensive polypharmacological anti-GC mechanisms of the herbal drugs.

The molecular docking analysis revealed that the active compounds of FDY003 and their direct hub targets show molecular docking scores of ≤ −5.0 (Figure 5), suggesting that the targets have strong binding affinities with their interacting active compounds and that these interactions confer the treatment effects of FDY003 against LC. The major targets of FDY003 were found to be genes and proteins associated with GC mechanisms and relevant therapeutic targets for GC treatment. The protein kinases AKT1 (encoded by AKT1) and extracellular signal-regulated kinase 2 (ERK2; encoded by MAPK1) are involved in the induction of various cancerous behaviors, including migration, invasion, proliferation, metastasis, angiogenesis, survival, epithelial-to-mesenchymal transition (EMT), cancer stemness, and chemoresistance in GC cells, which can be suppressed by inhibiting the expression and activity of the abovementioned enzymes^54-66. Genetic alteration, expression, and activity of AKT1, MAPK1, and MAPK3 can also affect the risk of developing GC, clinical outcomes, recurrence, and survival rates in patients^67-69. Additionally, c-Jun NH2-terminal kinase 1 (JNK1; encoded by MAPK8), a kinase that coordinates cell migration, apoptosis, survival, and proliferation, plays an important role in the carcinogenesis and development of GC^70-73. Furthermore, genetic mutations in PI3KCA is a determinant of the chemosensitivity of GC⁷⁴. The chronic hyperactivation of STAT3 (encoded by STAT3) was also found to contribute to the tumorigenesis and development of GC by promoting uncontrolled proliferation, survival, angiogenesis, metastasis, inflammation, chemoresistance, and stem-like features in GC cells, and it has also been found to be correlated with poor survival rates in patients with GC^75-77. Lastly, a loss-of-function mutation in TP53 causes it to malfunction, leading to its crucial role in GC carcinogenesis; its expression levels and the presence of genetic alterations in this gene may act as predictors of survival, clinicopathological characteristics, and tumor recurrence in patients with GC^78-81.

The target pathways of FDY003 are also involved in the key signaling mechanisms of GC pathology. The chemokine signaling pathway plays a role in the regulation of the immune system and tumor microenvironment, and it is further involved in GC tumorigenesis by modulating tumor transformation, growth, survival, metastasis, invasion, and angiogenesis⁸². The MAPK, HIF-1, ErbB, PI3K-Akt, mTOR, NF-kappa B, and Ras pathways are key modulators of GC carcinogenesis and progression through the regulation of malignant cellular behaviors such as proliferation, stem-like characteristics, migration, invasion, survival, angiogenesis, metastasis, apoptosis, chemoresistance, and EMT of GC cells^46,83-87. These oncogenic pathways have gained much attention as potential drug targets for targeted GC therapies^46,83-87. The estrogen signaling pathway controls proliferation, apoptosis, and invasion of GC cells, and it may act as a prognostic factor for the progression and prognosis of GC^88-91. The dysregulation of JAK-STAT, TNF, and NF-kappa B signaling also leads to uncontrolled proliferation, survival, EMT, and protumorigenic inflammation in GC cells^92,93. Moreover, functional deficiency of the p53 pathway may induce stemness and EMT in gastric epithelial cells, which ultimately leads to the formation, development, and metastasis of malignant GC tumors, and the genetic alterations in the components of this pathway are further associated with increased risk of GC^78-81,94. Additionally, the PD-1/PD-L1 pathway regulates the promotion and persistence of immune responses in the tumor microenvironment, making it a potential target for immune checkpoint blockade therapy in treating GC^95-97. The expression levels and activity of the components of this pathway may also serve as prognostic indicators for GC^95-97. Furthermore, the VEGF pathway is important for tumor metastasis and growth by promoting angiogenesis, with its activity being associated with decreased survival in patients with GC^98,99. Lastly, infection with Helicobacter pylori or Epstein–Barr virus may cause abnormalities in cell proliferation, survival, apoptosis, migration, invasion, and immune system, contributing to GC development^100,101.

The phytochemicals of FDY003 have also been reported to function as anti-GC compounds. AcT and Cm exhibit anti-proliferative and pro-apoptotic effects on GC cells^102,103. Cordycepin induces apoptosis and cell cycle arrest as well as inhibits proliferation, survival, and migration in human GC cells by targeting PI3K/Akt, caspase, and CLEC2 signaling^104-106. Isorhamnetin exhibits anti-migratory, anti-invasive, anti-proliferative, pro-apoptotic, and chemosensitizing effects by regulating peroxisome proliferator-activated receptor γ (PPAR-γ) and NF-kappa B cascades^107,108. Kaempferol regulates the activities of Akt, cyclooxygenase-2 (COX-2), ERK, caspase, and endoplasmic reticulum (ER) stress signaling, leading to anti-proliferation and autophagic cell death in GC cells^109,110. Luteolin inhibits proliferation, cell cycle progression, survival, migration, invasion, EMT, and angiogenesis as well as enhances the efficacy of chemotherapy and radiotherapy on GC cells by altering PI3K-Akt-mTOR, MAPK, STAT3, HIF-1, NF-kappa B, p53, and NOTCH1 signaling^111-117. Quercetin reduces the survival, proliferation, stemness, metastasis, migration, invasion, and chemoresistance activities of GC cells by modulating PI3K-Akt-mTOR, HIF-1, Wnt, VEGF receptor (VEGFR), MAPK, caspase, protein kinase C (PKC), NF-kappa B, adenosine monophosphate (AMP)-activated protein kinase (AMPK), matrix metalloproteinases (MMP), uPAR, p53, and Hippo pathways^118-125. It has also been reported that a higher intake of quercetin is associated with a decreased risk of developing GC¹¹⁸. β-Sitosterol suppresses the proliferation and survival capacity of GC cells through the modulation of AMPK, PTEN, heat shock protein 90 (HSP90), and caspase signaling¹²⁶.

In summary, the network pharmacology analysis presented system-level insights into the pharmacological mechanisms underlying the anti-GC activity of FDY003. We identified the active phytochemical compounds and therapeutic targets responsible for the multicompound–multitarget pharmacological effects of FDY003. Through the functional analysis of FDY003 components, we identified the molecular- and signaling-level mechanisms of the herbal drug from a comprehensive systems perspective. Future studies are needed to elucidate the anticancer effects of FDY003 on various cancerous cellular behaviors such as migration, invasion, stemness, and angiogenesis, which may broaden the therapeutic usage of herbal drugs for cancer treatment.

Supplemental Material

sj-docx-1-npx-10.1177_1934578X211073030 - Supplemental material for A Network Pharmacology Perspective Investigation of the Pharmacological Mechanisms of the Herbal Drug FDY003 in Gastric Cancer

Supplemental material, sj-docx-1-npx-10.1177_1934578X211073030 for A Network Pharmacology Perspective Investigation of the Pharmacological Mechanisms of the Herbal Drug FDY003 in Gastric Cancer by Ho-Sung Lee, In-Hee Lee, Kyungrae Kang, Sang-In Park, Minho Jung, Seung Gu Yang, Tae-Wook Kwon and Dae-Yeon Lee in Natural Product Communications

Footnotes

Acknowledgments

Not applicable.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (grant No. 2021R1F1A1049472).

Data Availability

All data generated or analyzed during this study are included in this published article and its Supplemental material file.

Author Contributions

Conceptualization: Ho-Sung Lee, In-Hee Lee, and Dae-Yeon Lee.

Methodology: Ho-Sung Lee, In-Hee Lee, and Dae-Yeon Lee.

Data collection: Ho-Sung Lee, In-Hee Lee, Kyungrae Kang, Sang-In Park, Minho Jung, Seung Gu Yang, and Tae-Wook Kwon.

Data analysis and investigation: Ho-Sung Lee, In-Hee Lee, and Dae-Yeon Lee.

Writing: Ho-Sung Lee, In-Hee Lee, and Dae-Yeon Lee.

All authors read and approved the final manuscript.

Ethical Approval

Ethical Approval is not applicable for this article.

Statement of Human and Animal Rights

This article does not contain any studies with human or animal subjects.

Statement of Informed Consent

There are no human subjects in this article and informed consent is not applicable.

Trial Registration

Not applicable, because this article does not contain any clinical trials.

Supplemental Material

Supplemental material for this article is available online.

ORCID iD

Dae-Yeon Lee

References

Sung

Ferlay

Siegel

, et al. Global cancer statistics 2020: gLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249.

Joshi

Badgwell

. Current treatment and recent progress in gastric cancer. CA Cancer J Clin. 2021;71(3):264-279.

Marin

Al-Abdulla

Lozano

, et al. Mechanisms of resistance to chemotherapy in gastric cancer. Anticancer Agents Med Chem. 2016;16(3):318-334.

Woll

Devries

Eisterer

, et al. Chemotherapy in gastric cancer. Anticancer Res. 2008;28(2B):1213-1219.

Hung

Hsu

Chiang

, et al. Complementary Chinese herbal medicine therapy improves survival of patients with gastric cancer in Taiwan: a nationwide retrospective matched-cohort study. J Ethnopharmacol. 2017;199(6):168-174.

Lee

Bae

Yoo

Cho

. Benefit of adjuvant traditional herbal medicine With chemotherapy for resectable gastric cancer. Integr Cancer Ther. 2018;17(3):619-627.

Takiguchi

Hiura

Takahashi

, et al. Effect of rikkunshito, a Japanese herbal medicine, on gastrointestinal symptoms and ghrelin levels in gastric cancer patients after gastrectomy. Gastric Cancer. 2013;16(2):167-174.

Yoshikawa

Shimada

Wakabayashi

, et al. Effect of daikenchuto, a traditional Japanese herbal medicine, after total gastrectomy for gastric cancer: a multicenter, randomized, double-blind, placebo-controlled, phase II trial. J Am Coll Surg. 2015;221(2):571-578.

Lee

I-H

Lee

D-YJPM

. FDY003 Inhibits colon cancer in a Colo205 xenograft mouse model by decreasing oxidative stress. Pharmacogn Mag. 2019;15(65):675-681.

10.

Lee

H-S

Lee

I-H

Kang

, et al. Systems pharmacology study of the anticervical cancer mechanisms of FDY003. Nat Prod Commun. 2020;15(12):1-15.

11.

Lee

Kang

, et al. A network pharmacology study on the molecular mechanisms of FDY003 for breast cancer treatment. Evid Based Complement Alternat Med. 2021;2021(3919143):1-18.

12.

Poornima

Kumar

Zhao

Blunder

Efferth

. Network pharmacology of cancer: from understanding of complex interactomes to the design of multi-target specific therapeutics from nature. Pharmacol Res. 2016;111:290-302.

13.

Lee

Kim

. The methodological trends of traditional herbal medicine employing network pharmacology. Biomolecules. 2019;9(362):1-15.

14.

Lee

Park

Lee

. Network pharmacology-based investigation of the system-level molecular mechanisms of the hematopoietic activity of samul-tang, a traditional Korean herbal formula. Evid Based Complement Alternat Med. 2020;2020(9048089):1-17.

15.

Wang

, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6(13):1-6.

16.

Tao

Gao

, et al. CancerHSP: anticancer herbs database of systems pharmacology. Sci Rep. 2015;5(11481):1-6.

17.

Liu

Guo

Wang

, et al. BATMAN-TCM: a bioinformatics analysis tool for molecular mechANism of traditional Chinese medicine. Sci Rep. 2016;6(21146):1-11.

18.

Yue

Xin

Fan

, et al. Herb pair danggui-honghua: mechanisms underlying blood stasis syndrome by system pharmacology approach. Sci Rep. 2017;7(40318):1-15.

19.

Kono

Iwasaki

Matsuoka

Fujita

. Effect of mechanical agitation on cationic liposome transport across an unstirred water layer in caco-2 cells. Biol Pharm Bull. 2016;39(8):1293-1299.

20.

Volpe

. Variability in caco-2 and MDCK cell-based intestinal permeability assays. J Pharm Sci. 2008;97(2):712-725.

21.

Garcia

Flowers

Cook

. The caco-2 cell culture system can be used as a model to study food iron availability. J Nutr. 1996;126(1):251-258.

22.

Wishart

Feunang

Guo

, et al. Drugbank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074-D1082.

23.

Zhu

Han

Kumar

, et al. Update of TTD: therapeutic target database. Nucleic Acids Res. 2010;38(Database issue):D787-D791.

24.

Lee

Park

Kang

Cha

Chun

. Network pharmacology-based prediction of active compounds and molecular targets in yijin-tang acting on hyperlipidaemia and atherosclerosis. J Ethnopharmacol. 2018;221(15):151-159.

25.

Wang

Craik

. Cyclic peptide oral bioavailability: lessons from the past. Biopolymers. 2016;106(6):901-909.

26.

Kim

Chen

Cheng

, et al. Pubchem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019;47(D1):D1102-D1109.

27.

Daina

Michielin

Zoete

. Swisstargetprediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47(W1):W357-W364.

28.

Szklarczyk

Santos

von Mering

Jensen

Bork

Kuhn

. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016;44(D1):D380-D384.

29.

Keiser

Roth

Armbruster

Ernsberger

Irwin

Shoichet

. Relating protein pharmacology by ligand chemistry. Nat Biotechnol. 2007;25(2):197-206.

30.

Wang

Shen

Wang

, et al. Pharmmapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res. 2017;45(W1):W356-W360.

31.

Pinero

Bravo

Queralt-Rosinach

, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2017;45(D1):D833-D839.

32.

Davis

Grondin

Johnson

, et al. The comparative toxicogenomics database: update 2019. Nucleic Acids Res. 2019;47(D1):D948-D954.

33.

Amberger

Bocchini

Schiettecatte

Scott

Hamosh

. OMIM.Org: online Mendelian inheritance in Man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2015;43(Database issue):D789-D798.

34.

Gwinn

Clyne

Yesupriya

Khoury

. A navigator for human genome epidemiology. Nat Genet. 2008;40(2):124-125.

35.

Safran

Dalah

Alexander

, et al. Genecards version 3: the human gene integrator. Database. 2010;2010(baq020):1-16.

36.

Whirl-Carrillo

McDonagh

Hebert

, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012;92(4):414-417.

37.

Barabasi

Oltvai

. Network biology: understanding the cell's Functional organization. Nat Rev Genet. 2004;5(2):101-113.

38.

Szklarczyk

Gable

Lyon

, et al. STRING V11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607-D613.

39.

Shannon

Markiel

Ozier

, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-2504.

40.

Nagy

Lanczky

Menyhart

Gyorffy

. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep. 2018;8(9227):1-9.

41.

Raudvere

Kolberg

Kuzmin

, et al. G:profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019;47(W1):W191-W198.

42.

Burley

Berman

Bhikadiya

, et al. RCSB Protein data bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res. 2019;47(D1):D464-D474.

43.

Trott

Olson

. Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455-461.

44.

Zhuang

Wen

Zhang

, et al.

Can network pharmacology identify the anti-virus and anti- inflammatory activities of shuanghuanglian oral liquid used in Chinese medicine for respiratory tract infection?

Eur J Integr Med. 2020;37:1-12.

45.

Zhang

Yuan

Zhou

, et al. Network pharmacology analysis of chaihu lizhong tang treating non-alcoholic fatty liver disease. Comput Biol Chem. 2020;86:1-9.

46.

Wadhwa

Song

Lee

Yao

Wei

Ajani

. Gastric cancer-molecular and clinical dimensions. Nat Rev Clin Oncol. 2013;10(11):643-655.

47.

Athanasios

Charalampos

Vasileios

Ashraf

. Protein-Protein interaction (PPI) network: recent advances in drug discovery. Curr Drug Metab. 2017;18(1):5-10.

48.

Huang

Niu

Green

Yang

Mei

Han

. Systematic prediction of pharmacodynamic drug-drug interactions through protein-protein-interaction network. PLoS Comput Biol. 2013;9(3):1-9.

49.

Zhu

Gao

, et al. The analysis of the drug-targets based on the topological properties in the human protein-protein interaction network. J Drug Target. 2009;17(7):524-532.

50.

Cho

Kim

Przytycka

. Chapter 5: network biology approach to complex diseases. PLoS Comput Biol. 2012;8(12):1-11.

51.

Jeong

Mason

Barabasi

Oltvai

. Lethality and centrality in protein networks. Nature. 2001;411(6833):41-42.

52.

Zhu

Zhang

Pan

Zhong

Huang

. Systems pharmacology-based approach to comparatively study the independent and synergistic mechanisms of danhong injection and naoxintong capsule in ischemic stroke treatment. Evid Based Complement Alternat Med. 2019;2019(1056708):1-18.

53.

Zhong

Liu

Zhou

. Synergic anti-pruritus mechanisms of action for the radix Sophorae flavescentis and Fructus cnidii herbal pair. Molecules. 2017;22(1465):1-13.

54.

Wang

Zhang

Cui

. Downregulation of long noncoding RNA LINC01419 inhibits cell migration, invasion, and tumor growth and promotes autophagy via inactivation of the PI3 K/Akt1/mTOR pathway in gastric cancer. Ther Adv Med Oncol. 2019;11:1-16.

55.

Zhou

Zhang

, et al. The AKT1/NF-kappaB/Notch1/PTEN axis has an important role in chemoresistance of gastric cancer cells. Cell Death Dis. 2013;4(e847):1-11.

56.

Guo

Shi

Lin

, et al. De-Ubiquitinating enzymes USP21 regulate MAPK1 expression by binding to transcription factor GATA3 to regulate tumor growth and cell stemness of gastric cancer. Front Cell Dev Biol. 2021;9(641981):1-13.

57.

Husain

Szabo

Pai

Soreghan

Jones

Tarnawski

. MAPK (ERK2) kinase–a key target for NSAIDs-induced inhibition of gastric cancer cell proliferation and growth. Life Sci. 2001;69(25–26):3045-3054.

58.

Shen

Dong

Zhou

. IL-33 Promotes gastric cancer cell invasion and migration Via ST2-ERK1/2 pathway. Dig Dis Sci. 2015;60(5):1265-1272.

59.

Bang

Kim

, et al. Lymphatic metastasis-related TBL1XR1 enhances stemness and metastasis in gastric cancer stem-like cells by activating ERK1/2-SOX2 signaling. Oncogene. 2021;40(5):922-936.

60.

Lin

Yoon

, et al. CDK5RAP3 As tumour suppressor negatively regulates self-renewal and invasion and is regulated by ERK1/2 signalling in human gastric cancer. Br J Cancer. 2020;123(7):1131-1144.

61.

Chen

, et al. Knockdown of TMPRSS3 inhibits gastric cancer cell proliferation, invasion and EMT via regulation of the ERK1/2 and PI3 K/Akt pathways. Biomed Pharmacother. 2018;107:841-848.

62.

Wang

Tang

Wang

, et al. EPCR Promotes MGC803 human gastric cancer cell tumor angiogenesis in vitro through activating ERK1/2 and AKT in a PAR1-dependent manner. Oncol Lett. 2018;16(2):1565-1570.

63.

Luo

Wang

Zhao

Chen

. The expression of TMPRSS4 and Erk1 correlates with metastasis and poor prognosis in Chinese patients with gastric cancer. PLoS One. 2013;8(7):1-17.

64.

Dong

Liu

Zhou

, et al. GLI1 Activation by non-classical pathway integrin alphavbeta3/ERK1/2 maintains stem cell-like phenotype of multicellular aggregates in gastric cancer peritoneal metastasis. Cell Death Dis. 2019;10(574):1-13.

65.

Liu

Qin

, et al. Ginkgo biloba extract enhances chemotherapy sensitivity and reverses chemoresistance through suppression of the KSR1-mediated ERK1/2 pathway in gastric cancer cells. Oncol Rep. 2015;33(6):2871-2882.

66.

Yang

Zhang

. C23 protein meditates bone morphogenetic protein-2-mediated EMT via up-regulation of Erk1/2 and Akt in gastric cancer. Med Oncol. 2015;32(76):1-8.

67.

Wang

Zhu

, et al. A functional polymorphism (rs2494752) in the AKT1 promoter region and gastric adenocarcinoma risk in an eastern Chinese population. Sci Rep. 2016;6(20008):1-8.

68.

Wang

Lin

Lan

, et al. A GG allele of 3'-side AKT1 SNP is associated with decreased AKT1 activation and better prognosis of gastric cancer. J Cancer Res Clin Oncol. 2014;140(8):1399-1411.

69.

Kim

Lee

Chae

, et al. Association between phosphorylated AMP-activated protein kinase and MAPK3/1 expression and prognosis for patients with gastric cancer. Oncology. 2013;85(2):78-85.

70.

Mishra

Senthivinayagam

Rangasamy

Sondarva

Rana

. Mixed lineage kinase-3/JNK1 axis promotes migration of human gastric cancer cells following gastrin stimulation. Mol Endocrinol. 2010;24(3):598-607.

71.

Shibata

Maeda

Hikiba

, et al. c-Jun NH2-terminal kinase 1 is a critical regulator for the development of gastric cancer in mice. Cancer Res. 2008;68(13):5031-5039.

72.

Zhang

Shi

, et al. Dihydroartemisinin induces apoptosis in human gastric cancer cell line BGC-823 through activation of JNK1/2 and p38 MAPK signaling pathways. J Recept Signal Transduct Res. 2017;37(2):174-180.

73.

Zhang

Zhou

Cheng

, et al. PRKAA1 Promotes proliferation and inhibits apoptosis of gastric cancer cells through activating JNK1 and Akt pathways. Oncol Res. 2020;28(3):213-223.

74.

Davies

Han

, et al. The AKT inhibitor AZD5363 is selectively active in PI3KCA mutant gastric cancer, and sensitizes a patient-derived gastric cancer xenograft model with PTEN loss to taxotere. J Transl Med. 2013;11(241):1-10.

75.

Ashrafizadeh

Zarrabi

Orouei

, et al. STAT3 Pathway in gastric cancer: signaling, therapeutic targeting and future prospects. Biology (Basel). 2020;9(126):1-36.

76.

Giraud

Menheniott

Judd

. Targeting STAT3 in gastric cancer. Expert Opin Ther Targets. 2012;16(9):889-901.

77.

Zhang

Huang

Deng

, et al. Co-ordinated overexpression of SIRT1 and STAT3 is associated with poor survival outcome in gastric cancer patients. Oncotarget. 2017;8(12):18848-18860.

78.

Busuttil

Zapparoli

Haupt

, et al. Role of p53 in the progression of gastric cancer. Oncotarget. 2014;5(23):12016-12026.

79.

Fenoglio-Preiser

Wang

Stemmermann

Noffsinger

. TP53 And gastric carcinoma: a review. Hum Mutat. 2003;21(3):258-270.

80.

Fondevila

Metges

Fuster

, et al. P53 and VEGF expression are independent predictors of tumour recurrence and survival following curative resection of gastric cancer. Br J Cancer. 2004;90(1):206-215.

81.

Yildirim

Kaya

Demirpence

Gunduz

Bozcuk

. Prognostic significance of p53 in gastric cancer: a meta- analysis. Asian Pac J Cancer Prev. 2015;16(1):327-332.

82.

Lee

Song

Yun

Kim

. CXC Chemokines and chemokine receptors in gastric cancer: from basic findings towards therapeutic targeting. World J Gastroenterol. 2014;20(7):1681-1693.

83.

Riquelme

Saavedra

Espinoza

, et al. Molecular classification of gastric cancer: towards a pathway-driven targeted therapy. Oncotarget. 2015;6(28):24750-24779.

84.

Yang

Raufi

Klempner

. Targeted therapy for gastric cancer: molecular pathways and ongoing investigations. Biochim Biophys Acta. 2014;1846(1):232-237.

85.

Farran

Muller

Montenegro

. Gastric cancer management: kinases as a target therapy. Clin Exp Pharmacol Physiol. 2017;44(6):613-622.

86.

Kitajima

Miyazaki

. The critical impact of HIF-1a on gastric cancer biology. Cancers (Basel). 2013;5(1):15-26.

87.

Sokolova

Naumann

. NF-kappaB Signaling in gastric cancer. Toxins (Basel). 2017;9(4):1-22.

88.

Yan

Tian

Huang

. Prognostic value of estrogen receptor alpha and estrogen receptor beta in gastric cancer based on a meta-analysis and The cancer genome atlas (TCGA) datasets. Int J Surg. 2018;53:24-31.

89.

Qin

Liu

Ding

, et al. The direct effect of estrogen on cell viability and apoptosis in human gastric cancer cells. Mol Cell Biochem. 2014;395::99-107.

90.

Rahman MS

Cao

. Estrogen receptors in gastric cancer: advances and perspectives. World J Gastroenterol. 2016;22(8):2475-2482.

91.

Zhou

Teng

, et al. Overexpression of ERalpha inhibits proliferation and invasion of MKN28 gastric cancer cells by suppressing beta-catenin. Oncol Rep. 2013;30(4):1622-1630.

92.

Khanna

Chua

Bay

Baeg

. The JAK/STAT signaling cascade in gastric carcinoma (review). Int J Oncol. 2015;47(5):1617-1626.

93.

Oguma

Oshima

. Inflammation, tumor necrosis factor and Wnt promotion in gastric cancer development. Future Oncol. 2010;6(4):515-526.

94.

Ohtsuka

Oshima

Ezawa

Abe

Oshima

Ohki

. Functional loss of p53 cooperates with the in vivo microenvironment to promote malignant progression of gastric cancers. Sci Rep. 2018;8(2291):1-15.

95.

Boger

Behrens

Mathiak

Kruger

Kalthoff

Rocken

. PD-L1 is an independent prognostic predictor in gastric cancer of western patients. Oncotarget. 2016;7(17):24269-24283.

96.

Tran

Sarkissian

Chao

Klempner

. PD-1 and PD-L1 as emerging therapeutic targets in gastric cancer: current evidence. Gastrointest Cancer. 2017;7:1-11.

97.

Cao

, et al. PD-1 and PD-L1 co-expression predicts favorable prognosis in gastric cancer. Oncotarget. 2017;8(38):64066-64082.

98.

Wang

Chen

Fang

Yang

. Overexpression of both VEGF-A and VEGF-C in gastric cancer correlates with prognosis, and silencing of both is effective to inhibit cancer growth. Int J Clin Exp Pathol. 2013;6(4):586-597.

99.

Zhou

, et al. Clinicopathological significance of E-cadherin, VEGF, and MMPs in gastric cancer. Tumour Biol. 2010;31(6):549-558.

100.

Graham

. Helicobacter pylori update: gastric cancer, reliable therapy, and possible benefits. Gastroenterology. 2015;148(4):719-731.

101.

Shinozaki-Ushiku

Kunita

Fukayama

. Update on epstein-barr virus and gastric cancer (review). Int J Oncol. 2015;46(4):1421-1434.

102.

Lim

H-W

Kwon

Y-M

Cho

S-M

, et al. Antitumor activity of cordyceps militaris on human cancer cell line. Korean J Pharmacogn. 2004;35(4):364-367.

103.

Kim

J-H

Kim

D-H

You

J-H

, et al. Comparison of cytotoxin and immune activities between natural and tissue cultured plant in Artemisia capillaris thunb. Korean J Med Crop Sci. 2005;13(4):154-160.

104.

Lee

Jeong

J-W

Choi

. Induction of apoptotic cell death by cordycepin, an active component of the fungus cordyceps militaris, in AGS human gastric cancer cells. J Life Sci. 2016;26(7):847-854.

105.

Lee

Jeong

J-W

Choi

. Nutrition. Inhibition of PI3 K/AKT signaling pathway enhances cordycepin-induced apoptosis in human gastric cancer cells. J Korean Soc Food Sci Nutr. 2016;45(6):835-842.

106.

Wang

Liu

, et al. Cordycepin suppresses cell proliferation and migration by targeting CLEC2 in human gastric cancer cells via Akt signaling pathway. Life Sci. 2019;223:110-119.

107.

Manu

Shanmugam

Ramachandran

, et al. Isorhamnetin augments the anti-tumor effect of capecitabine through the negative regulation of NF-kappaB signaling cascade in gastric cancer. Cancer Lett. 2015;363(1):28-36.

108.

Ramachandran

Manu

Shanmugam

, et al. Isorhamnetin inhibits proliferation and invasion and induces apoptosis through the modulation of peroxisome proliferator-activated receptor gamma activation pathway in gastric cancer. J Biol Chem. 2012;287(45):38028-38040.

109.

Kim

Lee

Kim

Cheon

. Kaempferol induces autophagic cell death via IRE1-JNK-CHOP pathway and inhibition of G9a in gastric cancer cells. Cell Death Dis. 2018;9(875):1-14.

110.

Song

Bao

Wei

, et al. Kaempferol inhibits gastric cancer tumor growth: an in vitro and in vivo study. Oncol Rep. 2015;33(2):868-874.

111.

Zhang

Wang

, et al. Luteolin exerts an anticancer effect on gastric cancer cells through multiple signaling pathways and regulating miRNAs. J Cancer. 2018;9(20):3669-3675.

112.

Radziejewska

Borzym-Kluczyk

Leszczynska

. Luteolin alters MUC1 extracellular domain, sT antigen, ADAM-17, IL-8, IL-10 and NF-kappaB expression in Helicobacter pylori-infected gastric cancer CRL-1739 cells: a preliminary study. Biomed Rep. 2021;14(19):1-9.

113.

Ren

Zhang

. Luteolin suppresses the proliferation of gastric cancer cells and acts in synergy with oxaliplatin. Biomed Res Int. 2020;2020(9396512):1-9.

114.

Zhang

Shen

Zhu

. Anti-proliferative and chemosensitizing effects of luteolin on human gastric cancer AGS cell line. Mol Cell Biochem. 2008;313(1–2):125-132.

115.

Zang

Zhang

, et al. Luteolin suppresses angiogenesis and vasculogenic mimicry formation through inhibiting Notch1-VEGF signaling in gastric cancer. Biochem Biophys Res Commun. 2017;490(3):913-919.

116.

Zang

Fan

, et al. Luteolin suppresses gastric cancer progression by reversing epithelial-mesenchymal transition via suppression of the notch signaling pathway. J Transl Med. 2017;15(52):1-11.

117.

Zhang

Wan

Guo

, et al. Radiosensitization effect of luteolin on human gastric cancer SGC-7901 cells. J Biol Regul Homeost Agents. 2009;23(2):71-78.

118.

Ekstrom

Serafini

Nyren

Wolk

Bosetti

Bellocco

. Dietary quercetin intake and risk of gastric cancer: results from a population-based study in Sweden. Ann Oncol. 2011;22(2):438-443.

119.

Lee

Shin

Cho

Kang

Cho

. Anti-Cancer effect of quercetin in Xenograft models with EBV-associated human gastric carcinoma. Molecules. 2016;21(10):1-10.

120.

Lei

Hou

Pai

Lin

Yeh

. Effects of quercetin combined with anticancer drugs on metastasis-associated factors of gastric cancer cells: in vitro and in vivo studies. J Nutr Biochem. 2018;51:105-113.

121.

Chen

. Quercetin Has antimetastatic effects on gastric cancer cells via the interruption of uPA/uPAR function by modulating NF-kappab, PKC-delta, ERK1/2, and AMPKalpha. Integr Cancer Ther. 2018;17(2):511-523.

122.

Shang

Lee

, et al. Quercetin induced cell apoptosis and altered gene expression in AGS human gastric cancer cells. Environ Toxicol. 2018;33(11):1168-1181.

123.

Shen

Wang

Guo

Zhang

. Quercetin inhibits the growth of human gastric cancer stem cells by inducing mitochondrial-dependent apoptosis through the inhibition of PI3 K/Akt signaling. Int J Mol Med. 2016;38(2):619-626.

124.

Wang

Liu

, et al. Quercetin induces protective autophagy in gastric cancer cells: involvement of Akt-mTOR- and hypoxia-induced factor 1alpha-mediated signaling. Autophagy. 2011;7(9):966-978.

125.

Han

Cao

Wang

, et al. Quercetin suppresses proliferation and motility through modulating hippo pathway via upregulating miR-146a-5p in gastric cancer. J Biomater Tissue Eng. 2019;9(1):82-88.

126.

Shin

Choi

Sung

, et al. Anti-tumour effects of beta-sitosterol are mediated by AMPK/PTEN/HSP90 axis in AGS human gastric adenocarcinoma cells and xenograft mouse models. Biochem Pharmacol. 2018;152:60-70.

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