Abstract
To explore the anti-tumor effects of Scutellaria baicalensis on osteosarcoma and its mechanism. Network pharmacology and molecular docking techniques were applied to investigate the effect and mechanism of Scutellaria baicalensis on osteosarcoma (OS). We analyzed the protein-protein interaction (PPI) network for potential targets of Scutellaria baicalensis for treating osteosarcoma and identified hub targets. We used KM curves to screen for hub targets that could effectively prolong the survival time of OS patients. We systematically performed gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of the Scutellaria baicalensis potential targets and predicted the Scutellaria baicalensis molecular mechanism and function in treating osteosarcoma. Through molecular docking, the binding process between the hub targets, which could prolong the survival time of sarcoma patients, and Scutellaria baicalensis was simulated. PPI network analysis of potential therapeutic targets discriminated 12 hub targets. The KM curves of the hub targets showed that upregulation of RXRA, RELA, ESR1, TNF, IL6, IL1B, and RB1 expression, and downregulation of MAPK1, VEGFA, MAPK14, CDK1, and PPARG expression were effective in improving the 5-year survival rate of OS patients. GO and KEGG enrichment demonstrated that Scutellaria baicalensis regulated multiple signaling pathways of OS. Molecular docking results indicated that Scutellaria baicalensis could bind freely to the above hub target, which could prolong the survival time of sarcoma patients. Scutellaria baicalensis acted on osteosarcoma by regulating a signaling network formed by hub targets connecting multiple signaling pathways. Scutellaria baicalensis appears to have the potential to serve as a therapeutic drug for osteosarcoma and to prolong the survival of OS patients.
Keywords: mechanism, molecular docking, network pharmacology, osteosarcoma, Scutellaria baicalensis
1. Introduction
Osteosarcoma (OS) is one of the most common malignant tumors in children and adolescents,[1,2] which originates from primitive mesenchymal stem cells and occurs most often in the epiphysis of long stem bones,[3] such as the distal femur and the proximal tibia.[4] The current treatment strategy for OS is neoadjuvant chemotherapy, radical resection, adjuvant chemotherapy and radiotherapy.[4,5] With the addition of chemotherapy, the 5-year overall survival rate for patients with OS has reached 60–70%, however, the overall 5-year survival rate for patients with overt metastasis and recurrence is still <15%.[6,7] Although new treatments such as immunotherapy and targeted gene therapy have been tried, the 5-year survival rate for OS has not changed in the past few decades.[5,8] Clearly, new treatment approaches are still urgently needed.
Scutellaria baicalensis is a traditional Chinese medicine,[9] and various active components of Scutellaria baicalensis have reportedly been demonstrated to have inhibitory effects on the biological behavior of OS such as migration, infiltration, and proliferation.[9–11]10–12 Nevertheless, there are no reports regarding the effects of Scutellaria baicalensis on osteosarcoma or its mechanisms.
By integrating systems biology, bioinformatics, and computer science, network pharmacology can efficiently and cost-effectively explore the relationship between diseases and drugs, evolving from a “one-target, one-drug” mode to a “network-target, multiple-component-therapeutics” mode.[13–15] This study used network pharmacology to systematically investigate the effect and mechanism of Scutellaria baicalensis on OS.
2. Methods and materials
The flow chart of the study design is shown in Figure 1
Figure 1.
Network pharmacological study of Scutellaria baicalensis for the treatment of osteosarcoma schematic diagram.
2.1. Collection of Scutellaria baicalensis ingredients
Search for the active ingredients of Scutellaria baicalensis was undertaken in the TCMSP database (https://old.tcmsp-e.com/tcmsp.php) with the keyword “Scutellaria Baicalensis.”
Screening criteria for oral bioavailability (OB) > 30% and drug-likeness > 0.08 were used to determine active ingredients of Scutellaria baicalensis, and further review identified effective active ingredients of Scutellaria baicalensis for subsequent data processing.
2.2. Collection of Scutellaria baicalensis-related targets
Based on the effective active ingredients of Scutellaria baicalensis, we screened for Scutellaria baicalensis-related targets in the TCMSP database, the ETCM database (http://www.tcmip.cn/ETCM/), and the Symmap database (http://www.symmap.org/). Results from the above 3 databases were integrated and de-duplicated, and a final list of baicalin-related targets was compiled.
2.3. Collection of OS-related targets
To identify OS-related targets, the DisGeNET database (https://www.disgenet.org/) and the Genecard database (https://www.genecards.org/) were searched using the keywords “OS” and “Homo sapiens” as filtering criteria. The Genecard database is a search platform that retrieves genes associated with human diseases from 150 + web sources. Genes were searched on the 2 platforms using the keyword “Osteosarcoma” which provided data including information about OS, such as the names and gene IDs. Again, the above 2 databases were integrated and de-duplicated to get “Homo sapiens OS” related genes.
2.4. Constructing protein-protein interaction (PPI) networks
We imported Scutellaria baicalensis-related targets and OS-related targets into the Venn diagram online platform (https://bioinfogp.cnb.csic.es/tools/venny/) to plot Venn diagrams. The intersection of the 2 potential targets of Scutellaria baicalensis for the treatment of OS was then imported into the String database (https://cn.string-db.org/), set the species as “Homo sapiens,” and the confidence level as “0.9” to construct the PPI network.
2.5. Acquisition of hub targets
We also imported the PPI network into Cytoscape 3.8.0 to further analyze the coefficients of the targets, such as Degree, Betweenness Centrality and Closeness Centrality, etc. Then, the targets obtained were screened twice with degree-≥degree median, Betweenness Centrality ≥ Betweenness Centrality median, and Closeness Centrality ≥ Closeness Centrality median as the screening criteria for screening hub targets.
2.6. Plotting Kaplan–Meier (KM) curves
We imported the hub targets into the Kaplan–Meier plotter online platform (https://kmplot.com/analysis/) to plot KM curves. Because there was no separate database for OS in this platform, we plotted KM curves based on the sarcoma database, and the P value < .05 was significant. The division between high and low groups was chosen as the median for gene expression levels. The Kaplan–Meier plotter database was constructed based on gene microarray and RNA-seq data from public databases such as GEO, EGA, and TCGA. It was used to integrate gene expression information and clinical prognostic values for meta-analysis and the study, discovery, and validation of survival-related molecular markers.
2.7. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis
To analyze the potential functions and pathways of Scutellaria baicalensis for OS, we used R software to perform GO and KEGG enrichment analysis of the potential targets of Scutellaria baicalensis for OS. GO enrichment was described in terms of biological processes (BP), cellular components (CC), and molecular functions (MF). R-package-Bioconductor Cluster Profiler is an R package (R x64 4.0.3) widely used for gene bioinformatics analysis.
2.8. Molecular docking
Autodock software is a software program used for molecular docking. In this study, Autodock 4 and Autodock Vina were used for molecular docking, and Pymol software was used for molecular processing and visualization of docking results. Targets whose differential expression had an impact on the survival of sarcoma patients were molecularly docked with the corresponding active ingredients. The 3D structures of the targets were obtained from the PDB database (https://www.rcsb.org/), and the 3D structures of the active ingredients were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The target proteins were pre-processed using Autodock 4 and Pymol software, and Autodock Vina software was used as a bulk molecular docking.
2.9. Statistical analysis
For correlation analysis, spearman analysis was employed. The survival difference in different groups was compared using a log-rank test and illustrated in Kaplan–Meier (KM) survival plots. All statistical P values were 2-sided, and a P value < .05 indicated statistical significance. Bioinformatics data analysis was performed with R x64 4.0.3, Cytoscape 3.8.0, the Kaplan–Meier Plotter online platform, and Microbiology mapping online tool.
3. Results
3.1. The active ingredients of Scutellaria baicalensis
The active ingredients of Scutellaria baicalensis were searched in the TCMSP database with the keyword Scutellaria baicalensis, and the active ingredients were screened with OB > 30% and drug likeness > 0.08 as the screening criteria. Finally, we identified 36 active ingredients of Scutellaria baicalensis (Table 1).
Table 1.
Characteristics of the active ingredients.
| Mol ID1 | Molecule name | OB2 (%) | DL3 | MW4 |
|---|---|---|---|---|
| MOL001689 | acacetin | 34.97 | 0.24 | 284.28 |
| MOL000173 | wogonin | 30.68 | 0.23 | 284.28 |
| MOL000228 | (2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one | 55.23 | 0.2 | 270.3 |
| MOL002714 | baicalein | 33.52 | 0.21 | 270.25 |
| MOL002908 | 5,8,2’-Trihydroxy-7-methoxyflavone | 37.01 | 0.27 | 300.28 |
| MOL002909 | 5,7,2,5-tetrahydroxy-8,6-dimethoxyflavone | 33.82 | 0.45 | 376.34 |
| MOL002910 | Carthamidin | 41.15 | 0.24 | 288.27 |
| MOL002911 | 2,6,2’,4’-tetrahydroxy-6’-methoxychaleone | 69.04 | 0.22 | 302.3 |
| MOL002913 | Dihydrobaicalin | 40.04 | 0.21 | 272.27 |
| MOL002914 | Eriodyctiol (flavanone) | 41.35 | 0.24 | 288.27 |
| MOL002915 | Salvigenin | 49.07 | 0.33 | 328.34 |
| MOL002917 | 5,2’,6’-Trihydroxy-7,8-dimethoxyflavone | 45.05 | 0.33 | 330.31 |
| MOL002925 | 5,7,2’,6’-Tetrahydroxyflavone | 37.01 | 0.24 | 286.25 |
| MOL002926 | dihydrooroxylin A | 38.72 | 0.23 | 286.3 |
| MOL002927 | Skullcapflavone II | 69.51 | 0.44 | 374.37 |
| MOL002928 | oroxylin a | 41.37 | 0.23 | 284.28 |
| MOL002932 | Panicolin | 76.26 | 0.29 | 314.31 |
| MOL002933 | 5,7,4’-Trihydroxy-8-methoxyflavone | 36.56 | 0.27 | 300.28 |
| MOL002934 | NEOBAICALEIN | 104.34 | 0.44 | 374.37 |
| MOL002937 | DIHYDROOROXYLIN | 66.06 | 0.23 | 286.3 |
| MOL000358 | beta-sitosterol | 36.91 | 0.75 | 414.79 |
| MOL000359 | sitosterol | 36.91 | 0.75 | 414.79 |
| MOL000525 | Norwogonin | 39.4 | 0.21 | 270.25 |
| MOL000552 | 5,2’-Dihydroxy-6,7,8-trimethoxyflavone | 31.71 | 0.35 | 344.34 |
| MOL000073 | ent-Epicatechin | 48.96 | 0.24 | 290.29 |
| MOL000449 | Stigmasterol | 43.83 | 0.76 | 412.77 |
| MOL001458 | coptisine | 30.67 | 0.86 | 320.34 |
| MOL001490 | bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate | 43.59 | 0.35 | 390.62 |
| MOL001506 | Supraene | 33.55 | 0.42 | 410.8 |
| MOL002879 | Diop | 43.59 | 0.39 | 390.62 |
| MOL002897 | epiberberine | 43.09 | 0.78 | 336.39 |
| MOL008206 | Moslosooflavone | 44.09 | 0.25 | 298.31 |
| MOL010415 | 11,13-Eicosadienoic acid, methyl ester | 39.28 | 0.23 | 322.59 |
| MOL012245 | 5,7,4’-trihydroxy-6-methoxyflavanone | 36.63 | 0.27 | 302.3 |
| MOL012246 | 5,7,4’-trihydroxy-8-methoxyflavanone | 74.24 | 0.26 | 302.3 |
| MOL012266 | rivularin | 37.94 | 0.37 | 344.34 |
Annotation: 1. ID: IDentity; 2. OB: oral bioavailability; 3. DL: drug likeness; 4. MW: Molecule Weight;
3.2. Scutellaria-related targets
The 122 Scutellaria baicalensis-related targets were obtained from the following databases: TCMSP, 122; ETCM, 227; Symmap, 232; and 466 targets were obtained by integrating and de-duplicating the above targets for further processing.
3.3. OS-related targets
We searched the Genecard and Disgenet databases for OS-related targets using the keyword “osteosarcoma” and obtained 5347 and 2283 OS-related targets, respectively; ultimately, 5681 OS-related targets were identified after integrating and de-duplicating the above targets, which were used for subsequent data processing.
3.4. Constructing PPI network
We constructed a Venn diagram with Scutellaria baicalensis-related targets and OS-related targets (Fig. 2), the intersection of which provided 232 potential targets of Scutellaria baicalensis for OS. These potential targets were imported into the String database to construct a PPI network (Fig. 3).
Figure 2.
The veen diagram about the target of Scutellaria baicalensis and the target of osteosarcoma. The blue circle represents the target of Scutellaria baicalensis, and the yellow circle represents the target of the intersection of the 2 circles represents the target of Scutellaria baicalensis for osteosarcoma.
Figure 3.
PPI network of Scutellaria baicalensis in the treatment of osteosarcoma. The nodes represent potential therapeutic targets of Scutellaria baicalensis against osteosarcoma. The larger the node, the higher the corresponding target degree and the more connections to other nodes. PPI = protein-protein interaction.
3.5. Acquisition of hub targets
To further process and analyze the PPI network, we imported the PPI network obtained from String website into Cytoscape software. We obtained the degree median, Betweenness Centrality median, and Closeness Centrality median of the PPI network, which were 33, 0.0104754965928755, and 0.404921700223713, respectively; the degree ≥ 33, Betweenness Centrality median ≥ 0.0104754965928755, and Closeness Centrality median ≥ 0.404921700223713. Then, the targets obtained were screened with degree-≥degree median, Betweenness Centrality ≥ Betweenness Centrality median, and Closeness Centrality ≥ Closeness Centrality median as the screening criteria for screening hub targets, and 12 hub targets were obtained (Table 2).
Table 2.
Characteristics of the 12 hub gene.
| Gene symbol | Degree | Closeness centrality | Betweenness centrality |
|---|---|---|---|
| RXRA | 50 | 0.435096154 | 0.063614378 |
| RELA | 86 | 0.47382199 | 0.04165325 |
| ESR1 | 52 | 0.454773869 | 0.041436641 |
| MAPK1 | 68 | 0.47382199 | 0.037174618 |
| TNF | 64 | 0.443627451 | 0.032712348 |
| VEGFA | 34 | 0.430952381 | 0.032367073 |
| MAPK14 | 64 | 0.466494845 | 0.032106226 |
| IL6 | 52 | 0.409502262 | 0.023162672 |
| CDK1 | 38 | 0.408577878 | 0.01860225 |
| IL1B | 38 | 0.405829596 | 0.017668934 |
| RB1 | 54 | 0.451371571 | 0.013343046 |
| PPARG | 38 | 0.420930233 | 0.010699693 |
CDK1 = cyclin-dependent kinase 1, ESR1 = estrogen receptor, IL1B = Interleukin-1 beta, IL6 = Interleukin-6, MAPK1 = mitogen-activated protein kinase, MAPK14 = Mitogen-activated protein kinase 14, RB1 = retinoblastoma-associated protein, RELA = Transcription factor p65, RXRA = RXR-alpha, TNF = tumor necrosis factor, VEGFA = vascular endothelial growth factor A.
3.6. Plotting KM curve
The hub targets were individually imported into The Kaplan–Meier Plotter online platform to plot KM curves, and the differential expression of 12 hub genes had an impact on the overall survival of sarcoma patients (KM curves for 12 hub genes displayed in Fig. 4).
Figure 4.
The Kaplan–Meier curves. A (RXRA), B (RELA), C (ESR1), D (TNF), E (IL6), F (IL1B), and G (RB1) are KM curves for genes whose upregulation prolongs median survival in patients with sarcoma. H (MAPK1), L (VEGFA), M (MAPK14), N (CDK1), and O (PPARG) were KM curves for genes whose downregulation could prolong the median survival of osteosarcoma patients. ESR1 = estrogen receptor, IL1B = Interleukin-1 beta, IL6 = Interleukin-6, KM = Kaplan–Meier, MAPK1 = mitogen-activated protein kinase, MAPK14 = Mitogen-activated protein kinase 14, RB1 = retinoblastoma-associated protein, RXRA = RXR-alpha.
3.7. GO and KEGG enrichment analysis
To investigate the potential function of Scutellaria baicalensis for the treatment of OS, we subjected the potential targets of Scutellaria baicalensis for the treatment of OS to GO enrichment analysis, and the results of GO enrichment analysis are shown in terms of BP, CC, and MF. P values were arranged from smallest to largest, and the top 10 BP, CC, and MF (shown in Supplementary Table 1, http://links.lww.com/MD/K625) are shown in Figures 5A and B, 6A and B and 7A and B. Figures 5B, 6B, and 7B highlight the genes and relationship between functions. The top 10 BP were response to xenobiotic stimulus, cellular response to peptide, epithelial cell apoptotic process, response to nutrient levels, response to oxidative stress, response to lipopolysaccharide, response to peptide hormone, response to reactive oxygen species, response to molecule of bacterial origin and regulation of neuron death.
Figure 5.
Top 10 significant biological process (BP) entries. (A): GO enrichment analysis of therapeutic targets for biological process. (B): Relationship between the therapeutic targets and biological process. GO = gene ontology.
Figure 6.
Top 10 significant cell component (CC) entries. (A): GO enrichment analysis of therapeutic targets for cell components. (B): Relationship between the therapeutic targets and cell component. GO = gene ontology.
Figure 7.
Top 10 significant molecular function (MF) entries. (A): GO enrichment analysis of therapeutic targets for molecular function. (B): Relationship between the therapeutic targets and molecular function. GO = gene ontology.
Next, the potential targets of Scutellaria baicalensis for the treatment of OS were subjected to KEGG enrichment analysis, with the P values arranged from smallest to largest, and the top 30 results of the enrichment results are displayed in Figure 8.
Figure 8.
KEGG enrichment analysis for therapeutic targets. KEGG = Kyoto encyclopedia of genes and genomes.
3.8. Molecular docking
To simulate the process of mutual binding between the hub target, whose differential expression had an impact on the survival of sarcoma patients, and the corresponding active ingredient of Scutellaria baicalensis, we undertook a molecular docking, which, with the free energy of release < −7 Kcal/mol, indicated that the corresponding active ingredient and the target bound effectively in the natural state. Based on the docking results, we plotted a heat map (Fig. 9) that visually demonstrated the results for the 20 dockings that released the most free energy (Fig 10; Table 3).
Figure 9.
Heatmaps of the docking scores of hub targets combined with corresponding bioactive compound of Scutellaria baicalensi. The darker the blue, the more free energy the bioactive ingredient has to bind to the hub targets.
Figure 10.
The top 20 significant Molecular Docking. A (PPARG, DIHYDROOROXYLI, −9.1kcal/mol); B (PPARG, Eriodyctiol, −9.1kcal/mol); C (PPARG, baicalein, −9kcal/mol); D (PPARG, Dihydrobaicalin, −9kcal/mol); E (PPARG, Panicolin, −8.9kcal/mol); F (PPARG, wogonin, −8.9kcal/mol), G (VEGFA, Dihydrobaicalin, −8.9kcal/mol); H (PPARG, acacetin, −8.8kcal/mol), I (PPARG, Hydroxywogonin, −8.8kcal/mol); J (CDK1, coptisine, −8.6kcal/mol), K (CDK1, Dihydrobaicalin, −8.6kcal/mol); L (MAPK14, coptisine, −8.6kcal/mol), M (PPARG, 5,7,2,6-Tetrahydroxyflavone, −8.6kcal/mol); N (PPARG, ent-Epicatechin, −8.6kcal/mol); O (MAPK14, Dihydrobaicalin, −8.4kcal/mol); P (MAPK14, SCHEMBL11128429, −8.3kcal/mol); Q (PPARG, Norwogonin, −8.3kcal/mol); R (CDK1, baicalein, −8.2kcal/mol); S (CDK1, Carthamidin, −8.2kcal/mol); T (CDK1, Hydroxywogonin, −8.2kcal/mol).
Table 3.
Information on the docking results of the top 20 significant molecules.
| Receptor | Ligands | Free Energy(kcal/mol) | Corresponding serial numbers in Figure 10 |
|---|---|---|---|
| PPARG | DIHYDROOROXYLIN | −9.1 | A |
| PPARG | Eriodyctiol | −9.1 | B |
| PPARG | baicalein | −9 | C |
| PPARG | Dihydrobaicalin | −9 | D |
| PPARG | Panicolin | −8.9 | E |
| PPARG | wogonin | −8.9 | F |
| VEGFA | Dihydrobaicalin | −8.9 | G |
| PPARG | acacetin | −8.8 | H |
| PPARG | Hydroxywogonin | −8.8 | I |
| CDK1 | coptisine | −8.6 | J |
| CDK1 | Dihydrobaicalin | −8.6 | K |
| MAPK14 | coptisine | −8.6 | L |
| PPARG | 5,7,2,6-Tetrahydroxyflavone | −8.6 | M |
| PPARG | ent-Epicatechin | −8.6 | N |
| MAPK14 | Dihydrobaicalin | −8.4 | O |
| MAPK14 | SCHEMBL11128429 | −8.3 | P |
| PPARG | Norwogonin | −8.3 | Q |
| CDK1 | baicalein | −8.2 | R |
| CDK1 | Carthamidin | −8.2 | S |
| CDK1 | Hydroxywogonin | −8.2 | T |
CDK1 = cyclin-dependent kinase 1, MAPK14 = Mitogen-activated protein kinase 14, PPARG = Peroxisome proliferator-activated receptor gamma, VEGFA = vascular endothelial growth factor A.
4. Discussion
Although osteosarcoma is a rare malignancy, at least 50% of such cases occur during childhood and adolescents younger than 20 years old. OS originates from skeletal progenitor cells but the precise molecular alterations responsible for its development remain undefined.[1,12] The current clinical treatment strategy for OS is either neoadjuvant chemotherapy followed by complete surgical resection or the same treatment in reverse order.[13] Previously, recognizing that a high percentage of OS patients had occult micrometastatic disease, chemotherapy was routinely undertaken with dramatic improvement in the overall 5-year survival of approximately 60% to 70%. Unfortunately, once patients develop overt distant metastases, the 5-year survival rate drops to <15%.[6,7,14] Scutellaria baicalensis is one of the traditional Chinese medicines, and several of its active ingredients have been shown to have anti-tumor effects.[15,16] However, the literature has not reported its effects on OS and its mechanism. The present study represents the first to investigate the effect of Scutellaria baicalensis on OS and its mechanism.
By intersecting Scutellaria baicalensis-related targets and OS-related targets obtained from several databases, potential therapeutic targets were identified for Scutellaria baicalensis in treating OS. Additionally, hub targets were defined for Scutellaria baicalensis acting on osteosarcoma by analyzing the PPI network. Then, to explore the effect of hub targets on the prognosis of sarcoma patients, we constructed KM curves. The hub targets that had an impact on the 5-year survival of sarcoma patients were selected for molecular docking with the corresponding active ingredients. Finally, to investigate the potential function and the possible pathways of Scutellaria baicalensis on OS, we subjected the potential targets of Scutellaria baicalensis for the treatment of OS to GO and KEGG enrichment analysis.
The results showed that 12 pivotal targets were obtained, which were Retinoic acid receptor RXR-alpha (RXRA), Transcription factor p65 (RELA), Estrogen receptor (ESR1), mitogen-activated protein kinase (MAPK1), Tumor necrosis factor (TNF), Vascular endothelial growth factor A (VEGFA), Mitogen-activated protein kinase 14 (MAPK14), Interleukin-6 (IL6), Cyclin-dependent kinase 1 (CDK1), Interleukin-1 beta (IL1B), Retinoblastoma-associated protein (RB1), and Peroxisome proliferator-activated receptor gamma (PPARG). The expression of RXRA, RELA, ESR1, TNF, IL6, IL1B, and RB1 was upregulation, and the expression of MAPK1, VEGFA, MAPK14, CDK1, and PPARG was downregulation. The 5-year survival rate of patients with OS was effectively improved. Moreover, KEGG enrichment results showed that the potential targets of Scutellaria baicalensis for OS were enriched in various signaling pathways, such as IL-17 signaling pathway, small cell lung cancer, PI3K-Akt signaling pathway, apoptosis, and TNF signaling pathway. In addition, we found that the hub targets were involved in the transduction of multiple signaling pathways. Among the docking results, Dihydrobaicalin is effectively bound to the most hub targets.
By analyzing the PPI network, we confirmed that potential therapeutic targets interacted with each other, in which the pivotal target was at the hub of the network when the drug acted on the hub targets, it could effectively regulate the whole protein network. The KEGG results showed that this single hub target was often involved in the transduction of multiple signaling pathways. For example, RELA was involved in the signaling of the IL-17 signaling pathway, small cell lung cancer, PI3K-Akt signaling pathway, apoptosis, and TNF signaling pathway. MAPK1 was involved in signaling pathways such as the IL-17 signaling pathway, small cell lung cancer, PI3K-Akt signaling pathway, apoptosis, and TNF signaling pathway. TNF was involved in the IL-17 signaling pathway, apoptosis, and TNF signaling pathway. There is considerable literature that has demonstrated that multiple signaling pathways have been enriched in regulating the multiple biological behaviors of OS cells.[9–11,17–21] In the present study, the hub target connected multiple signaling pathways to form an interactive signaling network, through which Scutellaria baicalensis affected the biological behavior of OS. When the PI3K-Akt signaling pathway was inhibited, the biological behavior of tumor cells, such as proliferation, migration, and apoptosis, was regulated.[18,20–22] Already known, when TNF is activated, proliferation and migration of tumor cells are inhibited, and apoptosis of OS cells is promoted.[23,24] Thus, with the hub target as the connecting point, multiple signaling pathways formed a signaling network of signal interoperability that regulated the biological behavior of osteosarcoma cells which influenced the survival of osteosarcoma patients. The molecular docking results indicated that several active components of Scutellaria baicalensis could bind freely to the corresponding hub targets in the natural state. Therefore, we could speculate that the active ingredients of Scutellaria baicalensis could affect the biological behaviors of osteosarcoma cells, such as proliferation, apoptosis, migration, and infiltration, by acting on the hub targets, thereby regulating the signaling network formed by connecting multiple signaling pathways in the hub targets, ultimately affecting the survival of osteosarcoma patients. Additionally, the hub targets and corresponding OS active ingredients were molecularly docked, and the ligand and receptor were able to bind freely in their natural state when the free energy released by docking was −7kcal/mol, and the docking results were confirmed with the highest free energy release. The docking results showed that Dihydrobaicalin effectively bound to the most hub targets and was the most promising active ingredient to become a therapeutic drug, while among the hub targets, PPARG and CDK1 effectively docked with the most active ingredients and were the most promising therapeutic targets for Scutellaria baicalensis when acting on OS.
Despite the robust nature of network pharmacology to investigate drug mechanisms, the present study still has some limitations. First in vitro and in vivo experiments on the effect of Scutellaria baicalensis on OS were not conducted in this study. Secondly, each database used had a different focus, and because of these differences, there were potential risks in joint analysis of multiple databases. Therefore, the discovery of new targets and pathways still needs to be conducted through basic laboratory experiments.
5. Conclusion
Through the use of network pharmacology, the present study investigated the potential role of Scutellaria baicalensis in regulating biological processes such as proliferation, apoptosis, migration, and invasion of OS cells. The results indicated that the regulatory mechanism was not a single pathway but a signaling network. Each signaling pathway could influence another through target crosstalk, with the final result affecting OS cells. Moreover, the 12 hub targets including RXRA, RELA, ESR1, MAPK1, TNF, VEGFA, MAPK14, IL6, CDK1, IL1B, RB1, and PPARG were shown to have prolonged OS survival time. Dihydrobaicalin was the most likely principal component of Scutellaria baicalensis in treating OS.
Acknowledgments
We are very grateful for the contributions of the TCMSP database, ETCMdatabase, symMap database, the DisGeNET database and the Genecard database that provided information on cancer research, as well as all colleagues involved in the study. The authors would like to express their gratitude to EditSprings (https://www.editsprings.cn) for the expert linguistic services provided.
Author contributions
Conceptualization: Jingbo Wang.
Data curation: Jingbo Wang.
Formal analysis: Jingbo Wang.
Funding acquisition: Lijuan Zhang.
Investigation: Lijuan Zhang.
Methodology: Lijuan Zhang, Shuangjiao Deng.
Project administration: Heng Fan.
Resources: Shuangjiao Deng, Heng Fan.
Software: Yushi Tian, Shuangjiao Deng.
Validation: Yushi Tian.
Visualization: Yushi Tian.
Writing – original draft: Lijuan Zhang.
Writing – review & editing: Yushi Tian.
Supplementary Material
Abbreviations:
- BP
- biological processes
- CC
- cellular components
- CDK1
- cyclin-dependent kinase 1
- ESR1
- estrogen receptor
- GO
- gene ontology
- IL1B
- Interleukin-1 beta
- IL6
- Interleukin-6
- KEGG
- Kyoto encyclopedia of genes and genomes
- KM
- Kaplan–Meier
- MAPK1
- mitogen-activated protein kinase
- MAPK14
- mitogen-activated protein kinase 14
- MF
- molecular functions
- OB
- oral bioavailability
- PPARG
- peroxisome proliferator-activated receptor gamma
- PPI
- protein-protein interaction
- RB1
- retinoblastoma-associated protein
- RELA
- Transcription factor p65
- RXRA
- RXR-alpha
- TNF
- tumor necrosis factor
- VEGFA
- vascular endothelial growth factor A
Supplemental Digital Content is available for this article.
The authors have no conflicts of interest to disclose.
Because we use public and anonymous data, according to the ethics guidelines, neither informed consent nor approval of the ethics committee is required.
All data generated or analyzed during this study are included in this published article [and its supplementary information files].
This work was supported by Youth Fund Project of China, Grant No. 81503433. This work was also supported by the provincial finance allocated to the seventh batch of National Old Chinese Medicine Experts’ Academic Experience Inheritance Project (2022), Grant No. 08.01.22028.
How to cite this article: Zhang L, Tian Y, Wang J, Deng S, Fan H. Network pharmacology-based research on the effect of Scutellaria baicalensis on osteosarcoma and the underlying mechanism. Medicine 2023;102:46(e35957).
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Lijuan Zhang, Email: 614365168@qq.com.
Yushi Tian, Email: 1141208128@qq.com.
Jingbo Wang, Email: 317491688@qq.com.
Shuangjiao Deng, Email: 670738774@qq.com.
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