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Abstract

Glioblastoma (GBM) is the most common type of primary malignant tumor that develops in the brain, with 0.21 million new cases per year globally and a median survival period of less than 2 years after diagnosis. Traditional Korean medicines have been increasingly suggested as effective and safe therapeutic strategies for GBM. However, their pharmacological effects and mechanistic characteristics remain to be studied. In this study, we employed a computational network pharmacological approach to determine the effects and mechanisms of the traditional Korean medicinal formula FDY003 on GBM. We found that FDY003 treatment decreased the viability of human GBM cells and increased their response to chemotherapeutics. We identified 10 potential active pharmacological compounds of FDY003 and 67 potential GBM-related target genes and proteins. The GBM-related targets of FDY003 were signaling components of various crucial GBM-associated pathways, such as PI3K-Akt, focal adhesion, MAPK, HIF-1, FoxO, Ras, and TNF. These pathways are functional regulators for the determination of cell growth and proliferation, survival and death, and cell division cycle of GBM cells. Together, the overall analyses contribute to the pharmacological basis for the anti-GBM roles of FDY003 and its systematic mechanisms.

Keywords

Herbal drugs anticancer agents glioblastoma network pharmacology molecular mechanisms

Introduction

Glioblastoma (GBM) is the most common type of primary malignant tumor that develops in the brain, with 0.21 million new cases per year worldwide and a median survival period of less than 2 years after diagnosis.¹ The current standard therapies for GBM include surgery, chemotherapy, radiotherapy, and immune checkpoint inhibition.² Unfortunately, these therapies have limited efficacy in improving the survival of patients with GBM.² Herbal drugs have been increasingly suggested as effective and safe therapeutic strategies for GBM since they can effectively suppress the malignant activities of GBM cells and can elevate the treatment efficacies of standard anti-GBM therapies.³

FDY003 is a traditional Korean medicinal formula composed of Cordyceps militaris (Cm), Lonicera japonica Thunberg (LjT), and Artemisia capillaris Thunberg (AcT). It induces growth arrest and death in cancer cells by targeting various oncogenes and tumor suppressors that are implicated in cancerous and tumorigenic mechanisms.^4-7 The drug has the potential to elevate the drug sensitivity of cancer cells.^4-7 However, the pharmacological mechanisms of FDY003 in GBM have not been elucidated from a systems approach.

Numerous previous studies have proven that computational network pharmacology is an efficient approach for understanding drug mechanisms of action.^8-10 The pharmacological effects of herbal drugs are determined by multiple interactions between their active pharmacological compounds and therapeutic targets.^8-10 Hence, network pharmacology elucidates the mechanisms of herbal drugs by analyzing the characteristics of their relevant networks, which are the integration of comprehensive compound–target interactions.^8-10 In this study, we employed a computational network pharmacology approach to determine the mechanisms underlying the role of FDY003 as a potential anti-GBM therapy.

Results and Discussion

Examination of FDY003 Activity in GBM

To examine FDY003 activity in GBM, we treated U-87 MG human GBM cells with either FDY003, temozolomide (a chemotherapy drug for GBM treatment²), or their combination. FDY003 significantly decreased the viability of GBM cells with an IC₅₀ value of 74.25 μg/mL (Figure 1A) and further enhanced the pharmacological effects along with temozolomide (Figure 1B), suggesting that FDY003 may possess anticancer properties in GBM.

Figure 1.

Effects of FDY003 treatment on the viability of human glioblastoma cells. (A) U-87 MG human glioblastoma cells were treated with indicated doses of FDY003 and their viability was measured. (B) U-87 MG cells were treated with indicated doses of FDY003 and/or temozolomide (TMZ) for 72 h, and their viability was measured. Data represent the mean ± SEM of five replicates. **P < 0.01; ***P < 0.001; two-tailed student's t-test versus the untreated control cells. ^###P < 0.001; two-tailed student's t-test versus the TMZ-treated cells.

Active Pharmacological Compounds and Targets of FDY003

To identify active pharmacological compounds, we screened the FDY003 compounds with Caco-2 permeability ≥ −0.4, drug-likeness ≥ 0.18, and oral bioavailability ≥ 30% to determine their activity (Supplementary Tables S1 and S2).^10,11 In addition, some compounds known to possess potent anticancer effects were selected as active compounds even if their pharmacokinetic parameters did not meet the inclusion criteria (Supplementary Tables S2 and S3). Then, we identified the therapeutic targets of the active pharmacological compounds using databases and programs used for the investigation of the molecular binding between chemicals and genes/proteins, as well as tools containing large-scale information regarding cancer biology and clinical oncology (Supplementary Table S3). In the identified FDY003 targets, those that are closely involved in the pro-tumorigenic processes of GBM were determined for the further network pharmacological analyses (Figure 2 and Supplementary Table S3).

Figure 2.

A venn diagram of FDY003 targets (green circle) and GBM-associated genes (yellow circle).

Network Pharmacological Dissection of Anti-GBM Mechanisms of FDY003

For the network pharmacological dissection of the anti-GBM mechanism of FDY003, its H-A-T network was generated by integrating comprehensive data (Figure 3 and Supplementary Table S3). In the network, 76.12% (51 of 67 targets) of targets belonged to quercetin (Figure 3 and Supplementary Table S3), which suggests that it may have the most important pharmacological role. Additionally, 89.55% (60 of 67 targets) belonged to ≥ 2 pharmacological compounds (Figure 3), which indicates that FDY003 shows multiple compound-multiple target characteristics.

Figure 3.

The herbal medicine–active pharmacological compound–target network for FDY003. Green node, herbal medicine; red node, active pharmacological compound; blue node, glioblastoma-related target.

Analyzing the interaction mechanisms of the druggable targets may facilitate the investigation of the pharmacological effects and regulatory mechanisms of therapeutic agents.^12,13 Hence, we integrated the interaction data between the GBM-related targets of FDY003 into a protein–protein interaction (PPI) network and investigated its topological characteristics to search for hub nodes within the network (Figure 4). Of note, hub nodes are those whose degrees greatly exceed the average node degree and are effective for mediating the pharmacological effects of drugs in cells and tissues.^14,15 In the analysis, nodes were defined as hubs if their degree was ≥2x average node degree.^4-7 The hub targets identified were AKT1, PIK3CA, PIK3R1, SRC, STAT3, TP53, and VEGFA (Figure 4), suggesting their function as key mediators for conferring FDY003 activity for GBM treatment.

Figure 4.

The protein–protein interaction network for glioblastoma-related targets of FDY003. Purple node, hub; blue node, non-hub.

We examined the functional enrichment of the targets to explore the potential regulatory mechanisms of FDY003 for the anti-GBM activity of the identified targets. The FDY003 targets were modulators for the cell cycle process, proliferation, and survival of GBM cells (Supplementary Figure S1), which could potentially explain its therapeutic activity for GBM treatment. In addition, FDY003 pharmacologically interacted with various pathways that are implicated in the carcinogenesis and progression of GBM (Figure 5 and Supplementary Figure S1), suggesting the involvement of multiple pathways in the action mechanism of FDY003. Moreover, we found that FDY003 may affect the gene expression levels of the crucial components of those targeted pathways in human GBM cells (Supplementary Figure S2).

Figure 5.

The herbal medicine–active pharmacological compound–target pathway network for FDY003. Green node, herbal medicine; red node, active pharmacological compound; blue node, glioblastoma-related target; orange node, glioblastoma-related pathway.

Examination of the Molecular Binding Interaction Between the Pharmacological Compounds and the Targets of FDY003

To examine the molecular binding interaction between the pharmacological compounds and the identified targets of FDY003, we investigated their binding affinities by calculating the molecular docking scores. The binding energies of the pharmacological compounds and their targets were ≤ −5.0 kcal/mol (Figure 6 and Supplementary Figure S3), which suggests their significant binding capacity.

Figure 6.

Evaluation of the binding activities between the active pharmacological compounds of FDY003 and their targets. (A) Isorhamnetin-AKT1 (binding energy = −6.4 kcal/mol). (B) Kaempferol-AKT1 (binding energy = −6.7 kcal/mol). (C) Kaempferol-Src (binding energy = −8.5 kcal/mol). (D) Kaempferol-STAT3 (binding energy = −9.0 kcal/mol). (E) Kaempferol-TP53 (binding energy = −8.8 kcal/mol). (F) Luteolin-AKT1 (binding energy = −6.6 kcal/mol). (G) Luteolin-STAT3 (binding energy = −7.7 kcal/mol). (H) Luteolin-TP53 (binding energy = −9.1 kcal/mol). (I) Luteolin-VEGFA (binding energy = −8.4 kcal/mol). (J) Quercetin-AKT (binding energy = −6.9 kcal/mol). (K) Quercetin-PIK3CA (binding energy = −8.2 kcal/mol). (L) Quercetin-PIK3R1 (binding energy = −9.7 kcal/mol).

In this study, we employed a network pharmacological approach to elucidate the mechanisms underlying the anti-GBM characteristics of the traditional Korean medicinal formula FDY003. The overall network pharmacological analyses determined active pharmacological compounds and targets of FDY003 for its anti-GBM activity and further investigated its molecular and signaling mechanisms. The results may contribute to the understanding of the pharmacological basis of the anti-GBM properties of FDY003, along with its systematic mechanisms.

The key GBM-associated targets of FDY003 may participate in the pathological processes of GBM, and their targeting has been shown to exhibit substantial anticancer effects. AKT1 is an oncogene that augments the capacity of GBM cells to survive, migrate, proliferate, and resist chemotherapies.^16-19 Notably, this effect can be overcome by AKT1 inhibition.^16-19 Aberrant activities and genetic alterations of PIK3CA contribute to the enhanced carcinogenesis, proliferation, invasion, and migration of GBM cells, which are associated with earlier recurrence and decreased survival of patients with GBM.^20-23 PIK3R1 is overexpressed and mutated in GBM cells and tissues, and its suppression can decrease their migration, proliferation, growth, motility, and invasion.²³ Src and STAT3 activities are strongly enhanced in GBM cells and tumors, and they modulate their inflammation, tumor microenvironment remodeling, migration proliferation, metastasis, invasion, growth, motility, angiogenesis, adhesion, therapeutic sensitivity, progression, epithelial–mesenchymal transition (EMT), and cancer stemness.^24-29 Much effort has been devoted to designing and developing effective therapeutics targeting them for GBM treatment.^24-29 The deregulation of TP53 is one of the most commonly observed events in GBM cells and tumors, leading to aberrant invasion, proliferation, cancer stemness, growth, and apoptosis evasion.³⁰ Its genetic mutation and expression status act as prognostic indicators for aggressiveness, treatment responsiveness, and patient survival in GBM, and its targeting is recognized as a potential anti-GBM therapy.³⁰ VEGFA functions as the most important driver for the induction of angiogenesis, metastasis, migration, invasion, and growth of GBM cells.^31-35 It also serves as a prognosticator for tumor recurrence and patient survival in GBM. Additionally, VEGFA is a primary target for anti-angiogenic treatment strategies.^31-35

The pathways involving the FDY003 targets are important signaling mechanisms for the pathological processes underlying GBM. The calcium signaling pathway modulates the growth, mobility, tumor microenvironment organization, cancer stemness, and cancer-promoting inflammation of GBM cells.³⁶ The chemokine pathway plays a key role in tumor microenvironment remodeling to induce glioblastomagenesis, metastasis, tumor progression, neovascularization, angiogenesis, microglia and macrophage polarization, invasion, and migration of GBM cells and tumors.³⁷ This pathway serves as a potential target site for anti-angiogenic and immunotherapeutic strategies for GBM treatment.³⁷ The erythroblastic leukemia viral oncogene homolog (ErbB), mitogen-activated protein kinase (MAPK), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), phosphoinositide 3-kinase (PI3K)-Akt, and Ras pathways are involved in tumorigenicity, migration, proliferation, stemness, survival, cell cycle control, therapy sensitivity, angiogenesis, invasiveness, and inflammation of GBM cells and tumors.^38-46 The expression and activity of the signaling components of these pathways may predict the prognosis of patients with GBM and may function as relevant action sites for anti-GBM therapeutic interventions.^38-46 Targeting the focal adhesion and fork head box O (FOXO) pathways can suppress proliferation, migration, stemness, survival, angiogenesis, motility, and invasion of GBM cells and tumors while elevating the treatment outcomes.^47-53 Their expression profiles have been related to the clinical prognosis of patients with GBM.^47-53 Various cancerous and tumorigenic mechanisms, including cell immortalization, invasion, metastasis, angiogenesis, migration, metabolic reorganization, proliferation, and stemness, are linked to the hypoxia-inducible factor (HIF)-1 pathway in the pathological processes of GBM.^54,55 The janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway participates in the carcinogenesis and progression of GBM by inducing migration, invasion, EMT, cancer stem-like abilities, inflammation, immunosuppression, angiogenesis, proliferation, and therapeutic resistance.⁵⁶ This activity of the pathway correlates with the survival and prognosis of patients with GBM and is a potential target for cytotoxic and immune checkpoint therapies.⁵⁶ Dysregulation of the p53 pathway, the most frequent event in GBM pathology, causes metastasis, growth, self-renewal potential, and anti-apoptosis of GBM cells and tumors, and its activity status is a predictor of treatment response and prognosis of patients with GBM.³⁰ The programmed death-ligand 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) pathway is an immunoregulatory cascade whose deregulation can cause the escape from the immune surveillance of GBM cells and tumors and targeting the pathway can restore and enhance immunity.^57-59 The tumor necrosis factor (TNF) pathway controls not only the cancerous inflammation but also the radiosensitivity, chemosensitivity, migration, invasiveness, and growth of GBM cells and tumors.^60-64 The TNF pathway also functions as a predictor for prognosis of survival rate and disease progression in patients with GBM.^60-64 The vascular endothelial growth factor (VEGF) pathway is a primary angiogenic driver of GBM cells and tumors, and it coordinates their immune responses, migration, proliferation, and invasion.^65-67 This pathway may also predict the survival, prognosis, metastasis, and recurrence of GBM patients, and it is the main druggable site for anti-angiogenic treatment strategies against GBM.^65-67

The active pharmacological compounds identified in FDY003 act as therapeutic components for GBM treatment. Cm induces apoptosis and autophagy in GBM cells and arrests their growth and cell cycle process by targeting caspase, B-cell lymphoma 2 (Bcl-2), autophagy-related (Atg) 5, microtubule-associated protein 1A/1B-light chain 3 (LC3), p53, p21, extracellular signal-regulated kinase (ERK), and PI3K/Akt cascades.⁶⁸ Cordycepin represses migration, proliferation, and survival of GBM cells through the pharmacological modulation of focal adhesion kinase (FAK), paxillin, integrin, Akt, p38, caspase, Bcl-2, Fas, Fas ligand (FasL), Bcl2-antagonist/killer (Bak), and poly (adenosine diphosphate-ribose) polymerase (PARP) signaling.^69,70 Eriodyctiol intervenes in the activities of PI3K/Akt, glycogen synthase kinase (GSK)-3β, NF-kappa B, cadherin, p38, and zinc finger E-box binding homeobox 1 (ZEB1) pathways in GBM cells, which leads to the inhibition of their migration, invasion, EMT, metastasis, survival, and proliferation.⁷¹ Kaempferol promotes oxidative stress, autophagy, pyroptosis, and death of GBM cells while repressing their drug resistance, growth, and migration through the regulation of reactive oxygen species (ROS), multidrug resistance-associated protein 1 (MRP1), superoxide dismutase 1 (SOD-1), thioredoxin, Bcl-2, caspase, PARP, interleukins (ILs), monocyte chemoattractant protein 1 (MCP-1), and regulated on activation, normal T cell expressed and secreted (RANTES) cascades.^{72, 73} Luteolin decreased the ability of GBM cells to induce cell viability, cell adhesion, invasion, migration, angiogenesis, cancer stemness, colony formation, chemoresistance and radioresistance, cell cycle progression, EMT, and inflammation. Therapeutic modulation of signaling cascades, including the musashi RNA binding protein 1 (MSI1), epidermal growth factor receptor (EGFR), insulin-like growth factor 1 receptor (IGF1R), MAPK, PI3K/Akt, caspase, PARP, matrix metalloproteinases (MMPs), tissue inhibitor of metalloproteinases (TIMPs), ILs, cyclooxygenase-2 (COX-2), NF-κB, survivin, Bcl-2, induced myeloid leukemia cell differentiation protein (Mcl-1), cell division control protein 42 homolog (CDC42), protein kinase C (PKC) α, X-linked inhibitor of apoptosis protein (XIAP), inducible nitric oxide synthase (iNOS), and cyclin pathways are the key signaling mechanisms underlying the anti-GBM activities of luteolin.^74-80 Quercetin acts on various signaling pathways, such as p53, mouse double minute 2 homolog (MDM2), Bcl-2, Bcl-2-associated X protein (Bax), PARP, caspase, cyclin, ERK, p38, c-Jun N-terminal kinase (JNK), PI3K/Akt, VEGF-A, MMPs, fibronectin, Axl, NF-κB, phospholipase D1 (PLD1), IL-6/STAT3, COX-2, Beclin-1, LC3, Atg7, heat shock proteins (HSPs), endoplasmic reticulum (ER) stress, and ROS pathways, to demonstrate its anti-growth, anti-survival, anti-migratory, anti-invasive, anti-angiogenic, chemosensitizing, and radiosensitizing effects in GBM cells.⁸¹

In summary, we utilized a network pharmacological approach to evaluate the effects and mechanisms of FDY003 on GBM. Further extensive studies investigating the mechanisms of FDY003 targeting various pathological behaviors of cancer cells are important. Understanding the cancer responses of various traditional Korean medicinal formula also can help expand the pharmacological role and usage of traditional Korean medicines as anti-tumor therapies.

Materials and Methods

A list of abbreviations used in the study are provided in Supplementary Table S4.

Cell Culture

The U-87 MG human GBM cell line was purchased from the Korean Cell Line Bank (Seoul, Korea) and was incubated in Dulbecco's Modified Eagle Medium (WELGENE Inc., Gyeongsang, Korea) with 10% fetal bovine serum (WELGENE Inc.) and penicillin-streptomycin (Thermo Fisher Scientific, Inc., Waltham, MA, USA) at 37 °C-5% CO₂.

Preparation of Drugs

First, we obtained all the dried raw medicines that comprise FDY003 (ie, Act, Cm, and LjT) from Hanpure Pharmaceuticals (Pocheon, Korea). Subsequently, we mixed and ground AcT (6.25 g), LjT (4.16 g), and Cm (6.25 g), placed them in 70% ethanol (500 mL), and performed an 80 °C reflux extraction process for 3 h. The extract was filtered using a 1-μm pore size-filter paper (Hyundai Micro, Seoul, South Korea). After concentration to dryness by rotary evaporation, 80% ethanol (500 mL) was added to the dried residue and stirred for 1 h. After filtration using a 1-μm pore size-filter paper (Hyundai Micro, Seoul, South Korea) and concentration by rotary evaporation, 90% ethanol (500 mL) was added to the dried material and further stirred for 1 h. This mixture was subsequently concentrated by rotary evaporation. After adding distilled water (500 mL) to the dried material, the solvent was sequentially filtered through 1-μm and 0.6-μm pore size-filter papers (Hyundai Micro, Seoul, South Korea), and cryodesiccated at − 80 °C. The extract was stored at − 20 °C and dissolved in distilled water immediately before use.

Determination of the Cell Viability of Drug-Treated Cells

The effects of drug treatment on cell viability were investigated using a water-soluble tetrazolium salt (WST-1) assay. In brief, we seeded 1.0 × 104 cells in the 96-well plates and treated them with either FDY003, temozolomide (Sigma-Aldrich, St. Louis, MO, USA), or both for 72 h. Subsequently, we incubated the drug-treated cells for 2 h with WST-1 solution (Daeil Lab Service Co. Ltd, Seoul, Korea) at 37 °C-5% CO₂. We determined cell viability by examining the absorbance at 450 nm using an xMark microplate absorbance spectrophotometer (Bio-Rad, Hercules, CA, USA).

Quantitative Real-Time PCR

RNA isolation from the harvested cells was performed using an easy-Blue^TM RNA Extraction Kit (iNtRON Biotech, Seongnam, Korea), according to the manufacturer's protocols. Then, the complementary DNA (cDNA) synthesis of the samples (total RNA 2.5 μg) was performed using a cDNA Synthesis Kit RT (iNtRON Biotech, Seongnam, Korea), according to the manufacturer's protocols. Quantitative real-time polymerase chain reaction (PCR) was performed with primers (Supplementary Table S5) through a StepOnePlus PCR system instrument (Applied Biosystems, Waltham, MA, USA) by using SYBR Green Master Mix (Applied Biosystems, Waltham, MA, USA). The transcription levels of the genes of interest were normalized to those of GAPDH in each reaction.

Screening of the Active Pharmacological Compounds and Targets of FDY003

To screen the active pharmacological compounds of FDY003, we searched the following databases: the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine,⁸² Traditional Chinese Medicine Systems Pharmacology,¹¹ and Anticancer Herbs Database of Systems Pharmacology.⁸³ We obtained a list of the pharmacological compounds in FDY003 and retrieved detailed information on their pharmacokinetic properties. Among the investigated compounds, we screened the compounds that are active on the basis of their drug-likeness, oral bioavailability, and Caco-2 permeability, which are the most useful pharmacokinetic parameters for active compound screening.^10,11 Drug-likeness examines the potential of chemicals to be designed and developed as drugs based on their biophysical characteristics.¹¹ Chemicals whose drug-likeness is ≥ 0.18 possess a strong potential to act as drugs.¹¹ Oral bioavailability examines the effectiveness of chemical delivery to the drug action sites after oral administration.¹¹ Chemicals whose oral bioavailability is ≥ 30% are generally effectively delivered to the targeted tissues and organs.¹¹ Caco-2 permeability examines the ability of chemicals to permeate and be absorbed in the intestines.¹¹ Chemicals whose Caco-2 permeability is ≥ −0.4 can permeate effectively and become absorbed in intestines.¹¹ Based on these reports, compounds with drug-likeness ≥ 0.18, oral bioavailability ≥ 30%, and Caco-2 permeability ≥ −0.4 were included in the list of active pharmacological compounds of FDY003.^{10, 11}

After the active pharmacological compound screening, we entered the simplified molecular-input line-entry system notation (investigated from PubChem⁸⁴) of the active pharmacological compounds of FDY003 to the following databases: the Search Tool for Interactions of Chemicals 5,⁸⁵ PharmMapper,⁸⁶ SwissTargetPrediction,⁸⁷ and Similarity Ensemble Approach⁸⁸ to obtain their targets. The GBM-associated genes and proteins that participate in their pathological mechanisms were obtained from the following databases: Comparative Toxicogenomics Database,⁸⁹ DrugBank,⁹⁰ Pharmacogenomics Knowledgebase,⁹¹ Therapeutic Target Database,⁹² Online Mendelian Inheritance in Man,⁹³ GeneCards,⁹⁴ Human Genome Epidemiology Navigator,⁹⁵ and DisGeNET.⁹⁶

FDY003-Related Network Construction

Biological networks, including FDY003-related networks, contain nodes and links describing their molecular and functional interactions.⁹⁷ In the herbal medicine–active pharmacological compound–target (H-A-T) network for FDY003, its medicinal materials, pharmacological compounds, and their targets act as nodes, and the interaction relationships among these nodes act as links. The H-A-T-Pathway (H-A-T-P) network of FDY003 was built by adding the connections between the FDY003 targets and their GBM-associated signals. In the PPI network for FDY003, its targets act as nodes, and their interactions (with a confidence score ≥ 0.9 investigated using STRING⁹⁸) act as links. The number of links that a node has is called a degree.⁹⁷ Network production, drawing, and analysis were performed using Cytoscape.⁹⁹

Functional Analysis of FDY003 Targets for Their Enrichments in Gene Ontology and Pathways

We used Database for Annotation, Visualization and Integrated Discovery (DAVID)¹⁰⁰ and Kyoto Encyclopedia of Genes and Genomes (KEGG)¹⁰¹ for the functional analysis of FDY003 targets for their enrichments in gene ontology and pathways, respectively. The results with p-values of less than 0.05 were considered to have statistical significance.

Evaluating the Molecular Interaction Affinities of Pharmacological Compounds and Targets of FDY003

To examine the binding interaction between the pharmacological compounds and the targets of FDY003, we investigated the molecular docking scores of compound–target pairs. In brief, we entered the structural data of FDY003 compounds (obtained from RCSB Protein Data Bank¹⁰²) and their targets (obtained from PubChem⁸⁴) to Autodock Vina¹⁰³ to calculate their scores for molecular docking. Following previous studies,^4-7 binding energy ≤ −5.0 kcal/mol was indicative of strong molecular interaction affinities for the compounds and their targets.

Supplemental Material

sj-docx-1-npx-10.1177_1934578X221126311 - Supplemental material for Unveiling the Mechanism of the Traditional Korean Medicinal Formula FDY003 on Glioblastoma Through a Computational Network Pharmacology Approach

Supplemental material, sj-docx-1-npx-10.1177_1934578X221126311 for Unveiling the Mechanism of the Traditional Korean Medicinal Formula FDY003 on Glioblastoma Through a Computational Network Pharmacology Approach by Ho-Sung Lee, In-Hee Lee, Sang-In Park, Minho Jung, Seung Gu Yang, Tae-Wook Kwon and Dae-Yeon Lee in Natural Product Communications

Footnotes

Author Contributions

Conceptualization: Ho-Sung Lee, In-Hee Lee, Dae-Yeon Lee. Methodology: Ho-Sung Lee, In-Hee Lee, Dae-Yeon Lee. Data collection: Ho-Sung Lee, In-Hee Lee, Sang-In Park, Minho Jung, Seung Gu Yang, Tae-Wook Kwon. Data analysis and investigation: Ho-Sung Lee, In-Hee Lee, Dae-Yeon Lee. Writing: Ho-Sung Lee, In-Hee Lee, Dae-Yeon Lee. All authors read and approved the final manuscript.

Data Statement

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

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.

Ethical Approval

Ethical Approval is not applicable for this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1049472).

ORCID iD

Dae-Yeon Lee

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.

Supplemental Material

Supplemental material for this article is available online.

References

Tan

Ashley

Lopez

, et al. Management of glioblastoma: state of the art and future directions. CA Cancer J Clin. 2020;70(4):299-312.

Oberheim Bush

Hervey-Jumper

Berger

. Management of glioblastoma, present and future. World Neurosurg. 2019;131:328-338.

Wang

, et al. A review of traditional Chinese medicine for treatment of glioblastoma. Biosci Trends. 2020;13(6):476-487.

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.

Lee

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

Lee

Kang

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

Lee

Kang

, et al. Network pharmacology-based dissection of the comprehensive molecular mechanisms of the herbal prescription FDY003 against estrogen receptor-positive breast cancer. Nat Prod Commun. 2021;16(9):1-15.

Poornima

Kumar

Zhao

, et al. 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.

Lee

Kim

, et al. The methodological trends of traditional herbal medicine employing network pharmacology. Biomolecules. 2019;9(8):1-15.

10.

Lee

Park

, et al. 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.

11.

Wang

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

12.

Athanasios

Charalampos

Vasileios

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

13.

Yildirim

Goh

Cusick

, et al. Drug-target network. Nat Biotechnol. 2007;25(10):1119-1126.

14.

Cho

Kim

Przytycka

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

15.

Jeong

Mason

Barabasi

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

16.

Gorgisen

Yaren

. Insulin receptor substrate 1 overexpression promotes survival of glioblastoma cells through AKT1 activation. Folia Neuropathol. 2020;58(1):38-44.

17.

Liu

, et al. UBE2S, a novel substrate of Akt1, associates with Ku70 and regulates DNA repair and glioblastoma multiforme resistance to chemotherapy. Oncogene. 2017;36(8):1145-1156.

18.

Mete

Unsal

Aydemir

, et al. Punicic acid inhibits glioblastoma migration and proliferation via the PI3K/AKT1/mTOR signaling pathway. Anticancer Agents Med Chem. 2019;19(9):1120-1131.

19.

Vehlow

Klapproth

Jin

, et al. Interaction of discoidin domain receptor 1 with a 14-3-3-beclin-1-Akt1 complex modulates glioblastoma therapy sensitivity. Cell Rep. 2019;26(13):3672-3683.

20.

Liang

Wang

Jin

, et al. MiR-422a acts as a tumor suppressor in glioblastoma by targeting PIK3CA. Am J Cancer Res. 2016;6(8):1695-1707.

21.

McNeill

Stroobant

Smithberger

, et al. PIK3CA missense mutations promote glioblastoma pathogenesis, but do not enhance targeted PI3K inhibition. PLoS One. 2018;13(7):1-21.

22.

Tanaka

Batchelor

Iafrate

, et al. PIK3CA activating mutations are associated with more disseminated disease at presentation and earlier recurrence in glioblastoma. Acta Neuropathol Commun. 2019;7(66):1-8.

23.

Weber

Parat

Binder

, et al. Abrogation of PIK3CA or PIK3R1 reduces proliferation, migration, and invasion in glioblastoma multiforme cells. Oncotarget. 2011;2(11):833-849.

24.

Cirotti

Contadini

Barila

. SRC kinase in glioblastoma. News from an old acquaintance. Cancers (Basel). 2020;12(6):1-21.

25.

Ahluwalia

de Groot

Liu

, et al. Targeting SRC in glioblastoma tumors and brain metastases: rationale and preclinical studies. Cancer Lett. 2010;298(2):139-149.

26.

de Groot

Milano

. Improving the prognosis for patients with glioblastoma: the rationale for targeting Src. J Neurooncol. 2009;95(2):151-163.

27.

Kim

Patel

Ruzevick

, et al. STAT3 activation in glioblastoma: biochemical and therapeutic implications. Cancers (Basel). 2014;6(1):376-395.

28.

Luwor

Stylli

Kaye

. The role of Stat3 in glioblastoma multiforme. J Clin Neurosci. 2013;20(7):907-911.

29.

West

Tsui

Stylli

, et al. The role of interleukin-6-STAT3 signalling in glioblastoma. Oncol Lett. 2018;16(4):4095-4104.

30.

Zhang

Dube

Gibert

Jr. , et al. The p53 pathway in glioblastoma. Cancers (Basel). 2018;10(9):1-18.

31.

Chang

Cheng

Tsai

, et al. PSMB8 inhibition decreases tumor angiogenesis in glioblastoma through vascular endothelial growth factor A reduction. Cancer Sci. 2020;111(11):4142-4153.

32.

Liu

Zhang

. LINGO1, C7orf31 and VEGFA are prognostic genes of primary glioblastoma: analysis of gene expression microarray. Neoplasma. 2018;65(4):532-541.

33.

Luo

Zhong

, et al. High gene expression levels of VEGFA and CXCL8 in the peritumoral brain zone are associated with the recurrence of glioblastoma: a bioinformatics analysis. Oncol Lett. 2019;18(6):6171-6179.

34.

Mastrella

Hou

, et al. Targeting APLN/APLNR improves antiangiogenic efficiency and blunts proinvasive side effects of VEGFA/VEGFR2 blockade in glioblastoma. Cancer Res. 2019;79(9):2298-2313.

35.

Serrill

Wan

Hau

, et al. Coibamide A, a natural lariat depsipeptide, inhibits VEGFA/VEGFR2 expression and suppresses tumor growth in glioblastoma xenografts. Invest New Drugs. 2016;34(1):24-40.

36.

Leclerc

Haeich

Aulestia

, et al. Calcium signaling orchestrates glioblastoma development: facts and conjunctures. Biochim Biophys Acta. 2016;1863(6 Pt B):1447-1459.

37.

Urbantat

Vajkoczy

Brandenburg

. Advances in chemokine signaling pathways as therapeutic targets in glioblastoma. Cancers (Basel). 2021;13(12):1-25.

38.

Krishna

Dubey

Singhvi

, et al. MAPK pathway: potential role in glioblastoma multiforme. Interdiscip Neurosurg. 2021;23(100901):1-2.

39.

Eskilsson

Rosland

Solecki

, et al. EGFR heterogeneity and implications for therapeutic intervention in glioblastoma. Neuro Oncol. 2018;20(6):743-752.

40.

Chen

, et al. PI3K/Akt/mTOR signaling pathway and targeted therapy for glioblastoma. Oncotarget. 2016;7(22):33440-33450.

41.

Westphal

Maire

Lamszus

. EGFR as a target for glioblastoma treatment: an unfulfilled promise. CNS Drugs. 2017;31(9):723-735.

42.

Zhao

Wang

Shao

, et al. Recent advances in the use of PI3K inhibitors for glioblastoma multiforme: current preclinical and clinical development. Mol Cancer. 2017;16(100):1-16.

43.

Gray

McFarland

Nozell

, et al. NF-kappaB and STAT3 in glioblastoma: therapeutic targets coming of age. Expert Rev Neurother. 2014;14(11):1293-1306.

44.

Soubannier

Stifani

. NF-kappaB signalling in glioblastoma. Biomedicines. 2017;5(2):1-15.

45.

Soukhtanloo

Mohtashami

Maghrouni

, et al. Natural products as promising targets in glioblastoma multiforme: a focus on NF-kappaB signaling pathway. Pharmacol Rep. 2020;72(2):285-295.

46.

Yamini

. NF-kappaB, mesenchymal differentiation and glioblastoma. Cells. 2018;7(9):1-18.

47.

Firat

Niedermann

. Foxo proteins or loss of functional p53 maintain stemness of glioblastoma stem cells and survival after ionizing radiation plus PI3K/mTOR inhibition. Oncotarget. 2016;7(34):54883-54896.

48.

Robertson

Perry

Doherty

, et al. Transcriptomic profiling of forkhead box transcription factors in adult glioblastoma multiforme. Cancer Genomics Proteomics. 2015;12(3):103-112.

49.

Wang

Zhang

, et al. MicroRNA8765p inhibits the progression of glioblastoma multiforme by directly targeting forkhead box M1. Oncol Rep. 2019;41(1):702-710.

50.

Dasari

Kaur

Velpula

, et al. Downregulation of focal adhesion kinase (FAK) by cord blood stem cells inhibits angiogenesis in glioblastoma. Aging (Albany NY). 2010;2(11):791-803.

51.

Shi

Lin

Lai

, et al. Upregulation of CPNE3 suppresses invasion, migration and proliferation of glioblastoma cells through FAK pathway inactivation. J Mol Histol. 2021;52(3):589-596.

52.

Storch

Sagerer

Cordes

. Cytotoxic and radiosensitizing effects of FAK targeting in human glioblastoma cells in vitro. Oncol Rep. 2015;33(4):2009-2016.

53.

Natarajan

Hecker

Gladson

. FAK signaling in anaplastic astro cytoma and glioblastoma tumors. Cancer J. 2003;9(2):126-133.

54.

Gabriely

Wheeler

Takenaka

, et al. Role of AHR and HIF-1alpha in glioblastoma metabolism. Trends Endocrinol Metab. 2017;28(6):428-436.

55.

Wang

, et al. Advances in the targeting of HIF-1alpha and future therapeutic strategies for glioblastoma multiforme (review). Oncol Rep. 2017;37(2):657-670.

56.

Ott

Fang

, et al. The role and therapeutic targeting of JAK/STAT signaling in glioblastoma. Cancers (Basel). 2021;13(3):1-25.

57.

Caccese

Indraccolo

Zagonel

, et al. PD-1/PD-L1 immune-checkpoint inhibitors in glioblastoma: a concise review. Crit Rev Oncol Hematol. 2019;135:128-134.

58.

Hao

Chen

Zhao

, et al. PD-L1 expression in glioblastoma, the clinical and prognostic significance: a systematic literature review and meta-analysis. Front Oncol. 2020;10(1015):1-10.

59.

Wang

Guo

Guan

, et al. Challenges and potential of PD-1/PD-L1 checkpoint blockade immunotherapy for glioblastoma. J Exp Clin Cancer Res. 2019;38(87):1-13.

60.

Ahluwalia

Bou-Anak

Burgett

, et al. Correlation of higher levels of soluble TNF-R1 with a shorter survival, independent of age, in recurrent glioblastoma. J Neurooncol. 2017;131(3):449-458.

61.

Ellingson

Harris

Woodworth

, et al. Baseline pretreatment contrast enhancing tumor volume including central necrosis is a prognostic factor in recurrent glioblastoma: evidence from single and multicenter trials. Neuro Oncol. 2017;19(1):89-98.

62.

Guo

Gong

Ali

, et al. A TNF-JNK-Axl-ERK signaling axis mediates primary resistance to EGFR inhibition in glioblastoma. Nat Neurosci. 2017;20(8):1074-1084.

63.

Iwasaki

Rogers

Barnett

, et al. Effect of recombinant tumor necrosis factor-alpha on three-dimensional growth, morphology, and invasiveness of human glioblastoma cells in vitro. J Neurosurg. 1993;78(6):952-958.

64.

Lamy

Ben Saad

Zgheib

, et al. Olive oil compounds inhibit the paracrine regulation of TNF-alpha-induced endothelial cell migration through reduced glioblastoma cell cyclooxygenase-2 expression. J Nutr Biochem. 2016;27:136-145.

65.

McCarty

. Glioblastoma resistance to anti-VEGF therapy: has the challenge been MET? Clin Cancer Res. 2013;19(7):1631-1633.

66.

Reardon

Turner

Peters

, et al. A review of VEGF/VEGFR-targeted therapeutics for recurrent glioblastoma. J Natl Compr Canc Netw. 2011;9(4):414-427.

67.

Turkowski

Brandenburg

Mueller

, et al. VEGF as a modulator of the innate immune response in glioblastoma. Glia. 2018;66(1):161-174.

68.

Yang

Kao

Huang

, et al. Cordyceps militaris and mycelial fermentation induced apoptosis and autophagy of human glioblastoma cells. Cell Death Dis. 2012;3(e431):1-9.

69.

Hueng

Hsieh

Cheng

, et al. Cordycepin inhibits migration of human glioblastoma cells by affecting lysosomal degradation and protein phosphatase activation. J Nutr Biochem. 2017;41:109-116.

70.

Baik

Mun

Kim

, et al. Apoptotic effects of cordycepin through the extrinsic pathway and p38 MAPK activation in human glioblastoma U87MG cells. J Microbiol Biotechnol. 2016;26(2):309-314.

71.

, et al. Eriodictyol inhibits glioblastoma migration and invasion by reversing EMT via downregulation of the P38 MAPK/GSK-3beta/ZEB1 pathway. Eur J Pharmacol. 2021;900(174069):1-9.

72.

Chen

Yang

, et al. Anti-glioblastoma activity of kaempferol via programmed cell death induction: involvement of autophagy and pyroptosis. Front Bioeng Biotechnol. 2020;8(614419):1-10.

73.

Nakatsuma

Fukami

Suzuki

, et al. Effects of kaempferol on the mechanisms of drug resistance in the human glioblastoma cell line T98G. Pharmazie. 2010;65(5):379-383.

74.

Anson

Wilcox

Huseman

, et al. Luteolin decreases epidermal growth factor receptor-mediated cell proliferation and induces apoptosis in glioblastoma cell lines. Basic Clin Pharmacol Toxicol. 2018;123(6):678-686.

75.

Chakrabarti

Ray

. Synergistic anti-tumor actions of luteolin and silibinin prevented cell migration and invasion and induced apoptosis in glioblastoma SNB19 cells and glioblastoma stem cells. Brain Res. 2015;1629:85-93.

76.

Cheng

Chiao

Liang

, et al. Luteolin inhibits migration of human glioblastoma U-87 MG and T98G cells through downregulation of Cdc42 expression and PI3K/AKT activity. Mol Biol Rep. 2013;40(9):5315-5326.

77.

Gueder D

Maatouk

Kalboussi

, et al. Heat processing effect of luteolin on anti-metastasis activity of human glioblastoma cells U87. Environ Sci Pollut Res Int. 2018;25(36):36545-36554.

78.

Wang

Jia

, et al. Luteolin reduces migration of human glioblastoma cell lines via inhibition of the p-IGF-1R/PI3K/AKT/mTOR signaling pathway. Oncol Lett. 2017;14(3):3545-3551.

79.

Ivanov

, et al. Luteolin inhibits Musashi1 binding to RNA and disrupts cancer phenotypes in glioblastoma cells. RNA Biol. 2018;15(11):1420-1432.

80.

Tsai

Chen

Hsu

, et al. Luteolin inhibits proliferation of human glioblastoma cells via induction of cell cycle arrest and apoptosis. J Taiwan Inst Chem Eng. 2013;44(6):837-845.

81.

Tavana

Mollazadeh

Mohtashami

, et al. Quercetin: a promising phytochemical for the treatment of glioblastoma multiforme. Biofactors. 2020;46(3):356-366.

82.

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.

83.

Tao

Gao

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

84.

Kim

Chen

Cheng

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

85.

Szklarczyk

Santos

von Mering

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

86.

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.

87.

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.

88.

Keiser

Roth

Armbruster

Ernsberger

Irwin

Shoichet

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

89.

Davis

Grondin

Johnson

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

90.

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.

91.

Whirl-Carrillo

McDonagh

Hebert

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

92.

Zhu

Han

Kumar

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

93.

Amberger

Bocchini

Schiettecatte

, et al. 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.

94.

Safran

Dalah

Alexander

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

95.

Gwinn

Clyne

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

96.

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.

97.

Barabasi

Oltvai

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

98.

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.

99.

Shannon

Markiel

Ozier

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

100.

Huang da

Sherman

Lempicki

. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44-57.

101.

Kanehisa

Furumichi

Sato

, et al. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021;49(D1):D545-D551.

102.

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.

103.

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.

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