Regulatory mechanism of Scutellaria baicalensis Georgi on bone cancer pain based on network pharmacology and experimental verification

Screening and target prediction of active ingredients of SBG

The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to screen the components of SBG. In this study, the range of oral bioavailability (OB) greater than or equal to 30% and drug-likeness (DL) greater than or equal to 0.18 were used to screen active ingredients (Dai, Sun & Zhong, 2020; Wu et al., 2020). Subsequently, TCMSP and the SwissTargetPrediction databases (http://www.swisstargetprediction.ch/) (Yang et al., 2021) were applied to predict targets. Further, the standardization of targets were performed using the UniProt platform (https://www.uniprot.org/). The complex relationships between active compounds and their targets were visualized based on Cytoscape software (version 3.7.2).

Collection of bone cancer pain targets

In order to reveal disease targets, the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/) (Yang et al., 2021) and GeneCards (https://www.genecards.org/) (Dai, Sun & Zhong, 2020; Zhang, Zhou & Ma, 2021; Zhan et al., 2021) were used to retrieve the disease targets through searching the keywords “bone cancer pain”, “cancer bone pain”, “cancer bone metastasis”, “bone cancer”, and “cancer pain”. According to the targets score, the median was applied to screen the disease targets, and the duplicate values were deleted to obtain the core bone-cancer-pain-related targets.

Intersection of active components and disease targets

To better investigate the complex relationship between components and diseases, the intersection targets of ingredients and diseases were obtained based on Venny 2.1.0 (http://bioinfogp.cnb.csic.es/tools/venny/). The STRING database (https://string-db.org/) was used to construct a protein-protein interaction network (PPI). Furthermore, R-3.6.3 software was used for the visualization of core targets, and Cytoscape 3.7.2 software was used to construct the component-target-disease (C-T-D) network diagram of the active ingredient and the intersection target.

Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses

In order to explore the biological information and signal pathways involved in the treatment of bone cancer pain by SBG (Wu et al., 2020), intersection targets were transformed into an “ensemble” form through the UniProt platform (http://www.UniProt.org/), and uploaded to the OmicShare platform (https://www.omicshare.com/) for GO and KEGG enrichment analyses and visualization.

Organ distribution of targets

According to the BioGPS database (http://biogps.org/), the mRNA expression data of the intersection targets in different tissues can be harvested. In order to investigate the potential relationship of the active ingredients between SBG and BCP at the organ level. In this study, a triple value of the median mRNA expression was used to determine the tissue distribution of the targets. Similarly, targets without corresponding mRNA expression data were also excluded from the analysis. Finally, a component-target-organ (C-T-O) network was constructed through Cytoscape to more intuitively reflect the distribution of targets in the organization.

Cells and culture conditions

BCP induces morphological and functional changes in brain structure, especially in hippocampal neuronal cellular plasticity (Wang et al., 2020). Currently, studies have found a decrease in the number of branches and dendritic length of CA3 pyramidal neurons in the hippocampus of mice with chronic sciatic nerve constriction injury (Tyrtyshnaia et al., 2019). Hippocampal neuron cell line (HT22) (CL-0595) was purchased from Wuhan Purcell Life Science Co., Ltd. (Wuhan, China). The HT22 cell line was cultured in Dulbecco’s modified Eagle’s medium (DMEM; Invitrogen, Camarillo, CA, USA) supplemented with 10% (v/v) fetal bovine serum (FBS; Gibco, Carlsbad, CA, USA), 100 µg/mL penicillin and 100 µg/mL streptomycin. Cells were grown at 37 °C in a humidified incubator with 5% carbon dioxide.

Drug treatment of HT22 cells

Baicalein (purity ≧ 98%) and wogonin (purity ≧ 98%) were purchased from MedChemExpress (New Jersey, USA). The cytotoxicity control test of the drug was detected using Cell Counting Kit-8 (CCK-8) (Beyotime, Hangzhou, China). HT22 cells were seeded in a 96-well plate at a density of 3 × 10⁵, and after culturing for 24 h, different concentrations of baicalein (0, 10, 20, 40, 80, 160, 320 µM) and wogonin (0, 5, 10, 20, 40, 80, 160 µM) were added respectively. After 24 h of incubation, cell viability was detected with CCK8, and the OD value (450 nm) of each well was measured with a SpectraMAX M3 microplate reader (Molecular Devices, Sunnyvale, CA, USA), which was used to screen out the safe concentration range of the drug.

QRT-PCR analysis

Total cellular RNA was extracted using Trizol reagent (Sigma, China) after 24 h incubation with various concentrations of baicalein (0, 20, 40, 80 µM) or wogonin (0, 10, 20, 40 µM). RNA purity and concentration were determined using a Nano Drop 2000 spectrophotometer (Thermo, USA). A reverse transcription kit (TaKaRa, Clontech) was applied to reverse transcribe RNA to cDNA. After reverse transcription, qRT-PCR was performed using the ABI 7500 Real-Time PCR System (Applied Biosystems, USA). The primer sequences were shown in Table 1, and the expression of VEGFA, IL-6, MAPK3, JUN and TNF target genes was detected. The samples were deformed at 95 °C for 30 s, then denatured at 95 °C for 40 cycles of 5 s and annealed at 60 °C for 34 s. Dissolution curve conditions were 95 °C for 15 s, 65 °C for 60 s, 95 °C for 30 s and 65 °C for 15 s. A system with a volume of 20 µL was used, 10 µL 2 × TB Green Premix Ex Taq II, 0.8 µL 10 µM forward primer and reverse primer, respectively, 0.4 µL 50 × ROX Reference Dye II, 2 µL cDNA, supplemented with double distilled water. GAPDH was used as the internal control, and the data were analyzed using the 2^−ΔΔCt method. The experiment was repeated three times.

Statistical analysis

The results of cell viability and target expression experiments were analyzed by one-way ANOVA. Data normalization was performed to control for the effect of changes in baseline parameters. GraphPad Prism 9.0 software (GraphPad Software, La Jolla, CA, USA) was used for analysis and visualization of study results. P < 0.05 was considered to be statistically significant.

Active ingredients and targets of SBG

According to the range of OB and DL, the 33 active components of SBG were obtained (Table 2), such as baicalein (MOL002714, OB = 33.52%, DL = 0.21), skullcapflavone II (MOL002927, OB = 69.51%, DL = 0.44), and moslosooflavone (MOL008206, OB = 44.09%, DL = 0.25). A total of 448 active components targets were obtained by combining the two databases. A network diagram (Figure S1) was generated and indicates that 33 active components and 448 drug targets had close relationships. In the component-target network diagram, the ellipses of different colors represent the targets of different components. Among them, baicalein (MOL002714) obtained more targets (Degree = 133) than other components, which were represented by a red ellipse.

Bone cancer pain targets and intersection targets

Based on the above results, 41 common targets were obtained through the intersection of SBG and BCP targets (Fig. 2A). The PPI diagram of 41 intersection targets was constructed (Fig. 2B), and degree value was counted (Fig. 2C). The targets in the center of the PPI diagram with degree value, and the closer connection with other potential targets, such as Vascular endothelial growth factor A (VEGFA), Interleukin-6 (IL6), MAP kinase ERK1 (MAPK3), Transcription factor AP-1 (JUN), and Tumor necrosis factor (TNF), which may be more essential in the treatment of BCP.

In the C-T-D network (Fig. 2D), targets with larger circle and closer to red may be more essential, such as VEGFA, MAPK3, and TNF. Meanwhile, the components with closer to the center of the circle and closer to yellow play an important role in the treatment of BCP, such as wogonin (MOL000173) and baicalein (MOL002714). In conclusion, the main components of SBG regulating BCP were baicalein and wogonin, and the core targets were VEGFA, IL6, MAPK3, JUN and TNF.

GO and KEGG enrichment results

The intersection targets were analyzed by GO and KEGG enrichment, (Figs. 3A, 3B and S2). Intersection targets mainly regulated biological processes and related pathways, such as the regulation of cell death (GO:0010941), the regulation of cell migration (GO:0030334), the pathways in cancer (ko05200), IL-17 signaling pathway (ko04657), and TNF signaling pathway (ko04668). Results indicated that the active compounds of SBG alleviate BCP by regulating core targets of angiogenesis-related targets VEGF, Transforming growth factor beta-1 (TGFB1), Apoptosis-related targets Caspase-9 (CASP9), TNF, JUN, Estrogen receptor (ESR1), and MAPK3.

Distribution of core targets in organs

The tissue distribution of 41 intersection targets has shown in Figs. 4A and 4B. The results demonstrated that there were five targets (ACE, CXCR4, ELANE, MMP9, and MPO) mainly distributed in the bone marrow to regulate the proliferation, death, and apoptosis of bone cancer cells, and three targets (CYCS, MAPK3, and P2RX7) mainly distributed in the brain tissue may be related to the pain through regulation. In addition, there were six targets (ABCB1, CASP9, JAK2, PTGS2, SIRT1, and TNFRSF1A) mainly distributed in the blood, which may act on target organs by regulating the targets of the apoptosis, proliferation, and migration of the related cells in the blood. Most of the remaining targets were distributed in metabolic organs, such as liver, heart, and kidney.

Cytotoxicity control test of drug

In order to more accurately examine the therapeutic effect of baicalein or wogonin on BCP, this study screened the safe concentration of baicalein or wogonin on the HT22 hippocampal neuron cell line (Fig. 5). The results demonstrated that baicalein did not affect the viability of HT22 cells in the concentration range of 0–160 μM, and P > 0.05. Similarly, wogonin did not affect the viability of HT22 cells in the concentration range of 0–80 μM (P > 0.05). Subsequent experiments will select 0–160 µM baicalein and 0–80 µM wogonin to explore the expression of core targets.

Validation of key target results by in vitro assays

The mRNA expression results of VEGFA, IL6, MAPK3, JUN and TNF target genes in HT22 cells after treatment of baicalein or wogonin were evidenced in Figs. 6 and 7. The results demonstrated that compared with the control group, baicalein obviously promoted the expression of VEGFA (40 and 80 µM), IL6 (80 µM), MAPK3 (40 and 80 µM), JUN (40 and 80 µM), and evidently reduced the expression of TNF (20, 40 and 80 µM). In addition, wogonin can significantly enhanced the expression of VEGFA (10, 20 and 40 µM), IL-6 (20 µM), JUN (40 µM), and obviously decreased the expression of TNF (20 and 40 µM), the difference has statistical reference value (P < 0.05). In general, the qRT-PCR results indicated that baicalein and wogonin were involved in hippocampal neurogenesis by regulating VEGFA, IL6, MAPK3, JUN and TNF target genes, thereby exerting anti-BCP effects.