Exploration of the effect and mechanism of Scutellaria barbata D. Don in the treatment of ovarian cancer based on network pharmacology and in vitro experimental verification

Abstract

The mortality rate of ovarian cancer is the highest among gynecological cancers, posing a serious threat to women health and life. Scutellaria barbata D. Don (SBD) can effectively treat ovarian cancer. However, its mechanism of action is unclear. The aim of this study was to elucidate the mechanism of SBD in the treatment of ovarian cancer using network pharmacology, and to verify the experimental results using human ovarian cancer SKOV3 cells. The Herb and Disease Gene databases were searched to identify common targets of SBD and ovarian cancer. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Protein-Protein Interaction (PPI) network analyses were performed to identify the potential molecular mechanisms behind SBD. Finally, the molecular docking and main possible pathways were verified by experimental studies. Cell proliferation, the mRNA expression level of key genes and signaling pathway were all investigated and evaluated in vitro. A total of 29 bioactive ingredients and 137 common targets in SBD were found to inhibit ovarian cancer development. The active ingredients identified include quercetin, luteolin, and wogonin. Analysis of the PPI network showed that AKT1, VEGFA, JUN, TNF, and Caspase-3 shared centrality among all target genes. The results of the KEGG pathway analysis indicated that the cancer pathway, PI3K-Akt signaling pathway, and MAPK signaling pathways mediated the effects of SBD against ovarian cancer progression. Cell experiments showed that quercetin, luteolin, and wogonin inhibited the proliferation and clone formation of SKOV3 cells and regulated mRNA expression of 5 key genes by inhibiting PI3K/Akt signaling pathway. Our results demonstrate that SBD exerted anti-ovarian cancer effects through its key components quercetin, luteolin and wogonin. Mechanistically, its anti-cancer effects were mediated by inhibition of the PI3K/Akt signaling pathways. Therefore, SBD might be a candidate drug for ovarian cancer treatment.

1. Introduction

Ovarian cancer (OC) is a lethal and common gynecological malignancy, with 80% of patients diagnosed at an advanced stage of disease.^[1] Fewer than half of OC patients survive beyond 5 years after diagnosis due to the prevalence of aggressive high-grade serous carcinomas and the lack of accurate early detection methods.^[2] Despite many improvements in ovarian cancer diagnosis and treatment, conventional chemotherapy and new biological drugs cannot cure the disease, and the overall prognosis of patients remains poor.^[3] Thus, traditional Chinese medicine (TCM), a complementary and alternative approach, is considered to be an effective alternative treatment for ovarian cancer. Some Chinese medicine preparations are widely used to treat cancer due to their properties of clearing away heat and toxic materials, and improving immunity thereby enhancing the quality of life of patients.^[4]

The use of TCM in treating cancer has been recorded in Chinese medical texts and publications for more than 2 millennia. Scutellaria barbata D. Don (SBD) is derived from the dried Labiate plant and has been widely used to treat patients with multiple cancers.^[5] SBD, belonging to Labiatae, has a sour taste and is cold in nature.^[6] They have the functions of clearing away heat and detoxification, promoting blood circulation, removing blood stasis, anti-inflammatory and analgesic effects, and can be used as an adjuvant treatment for various cancers, including colorectal cancer, breast cancer, bladder cancer, lung cancer, liver cancer, gastric cancer, ovarian cancer, and other malignant tumors.^[7,8] Studies have also shown that SBD can inhibit proliferation and induce apoptosis of ovarian cancer cell lines^[9] and downregulate MMP-2/9 expression to inhibit the migration of ovarian cancer cells.^[10] However, there are limited research related to SBD in the field of ovarian cancer, and most of them focus on a single target or a single pathway, which cannot comprehensively explain the antitumor effects of SBD.SBD contain multiple components and regulates several target genes with synergistic effects among various members. Therefore, the detailed effects and mechanism of SBD in the treatment of ovarian cancer need to be explored.

Network pharmacology combines bioinformatics, system biology, and pharmacology to explain the interaction between TCM and diseases and conforms to the systematic and holistic view of TCM.^[11,12] Network pharmacology updates the “one target, one drug” model to the “multicomponent, multitarget” model. In addition, network pharmacology clarifies the complex interactions between genes, proteins, and metabolites related to diseases and drugs, allowing systematic analysis of the relationship between TCM and diseases.^[13,14] This study combined network pharmacology with experimental verification to clarify the characteristics and potential molecular mechanism of SBD in treating ovarian cancer, opening up a new avenue for investigation. This research on SBD molecular mechanisms in ovarian cancer treatment not only lays a foundation for its broader use but also provides a reference for developing TCM and monomers, facilitating its integration with modern medicine to benefit patients, as shown in Figure 1, the working flowchart.

Figure 1.

Flow diagram of the research.

2. Materials and methods

2.1. Network pharmacology analysis

2.1.1. Identification of active ingredients of SBD.

This study searched for the active ingredients of SBD in the Traditional Chinese Medicine System Pharmacology (TCMSP, https://old.tcmsp-e.com/index.php) database. The TCMSP database contains comprehensive information about traditional Chinese herbs, including their chemical structure, oral bioavailability, drug-likeness, half-life, Caco-2 intestinal epithelial permeability, blood-brain barrier, and associated drug-target-disease network. Subsequently, pharmacokinetic properties, including absorption, distribution, metabolism, and excretion (ADME), oral bioavailability (OB) ≥ 30%, and drug-likeness (DL) ≥ 0.18%, were used to screen for active ingredients of SBD. Compounds with OB ≥ 30% have good absorption, and a DL ≥ 0. 18% is chemically conducive to drug development.^[15] We then obtained the SDF structural formulae of active ingredients from the PubChem database (http://pubchem.ncbi.nlm.nih.gov/) for ulterior target prediction.^[16]

2.1.2. Prediction of potential targets of SBD.

This study used the TCMSP target module, SwissTargetPrediction, (http://www.swisstargetprediction.ch/), and PharmMapper (http://www.lilab-ecust.cn/pharmmapper/) databases to predict the potential targets of SBD active ingredients. The TCMSP database provides the chemical composition of Chinese herbs, all-sided targets of active ingredients, and their associated with diseases.^[15] SwissTargetPrediction predicts the targets of active ingredients by comparing their structures to the 2- and 3-dimensional structures of known compounds.^[17] PharmMapper is a freely accessible server that uses pharmacodynamic gene mapping methods to recognize candidate targets for drugs, natural products, or other newly found compounds with unidentified binding targets.^[18] The resulting target protein names were ID mapped to their gene names using the UniProt database. (http://www.UniProt.org/).^[19] All the targets were limited to “homo sapiens.”

2.1.3. Target prediction of ovarian cancer disease.

Four disease databases, including the Drugbank database (https://www.drugbank.ca/),^[20] DisGeNET database (https://www.disgenet.org/home/),^[21] GeneCards database (https://www.genecards.org/),^[22] and OMIM database (https://omim.org/),^[23] were integrated to obtain disease-related targets for ovarian cancer with “ovarian cancer” as the keyword. All the targets were limited to “homo sapiens.” The results retrieved at this stage were merged, and only one repeated target was reserved.

2.1.4. Intersection active ingredients and disease targets construction.

The Jvenn online tool was used to generate a Venn diagram and identify common targets for SBD and ovarian cancer. The identified common targets were then submitted to String (https://string-db.org/) to generate a protein-protein interaction (PPI) network.^[24,25] The obtained network was visualized using Cytoscape v3.9.0, and topological analysis was performed using the network analysis module.

2.1.5. Biological function analysis of GO and KEGG pathway enrichment analysis.

To gain insight into the gene functions and signaling pathways of the SBD targets, we used the Metascape (https://www.metascape.org/) tools,^[26,27] which are online biological knowledge base and analysis tools, to identify enriched cellular components (CC), biological processes (BP), molecular function (MF), and KEGG pathways. GO analysis is mainly used to describe the functions of gene products, including cellular, molecular, and biological functions. On the other hand, KEGG pathway enrichment analysis is used to analyze core pathways and explore the possible biological function and signaling pathway mechanisms in the treatment of ovarian cancer. The GO and KEGG results were visually analyzed by on the online bioinformatics platform (http://www.bioinformatics.com.cn/) and with the Cytoscape software.

2.2. Molecular docking analysis

The 2D structures of the potential active ingredients in SBD were downloaded from the TCMSP database while the 3D structure of the ovarian cancer docking targets (top 5 of degree in PPI network) treated by SBD was downloaded from the Worldwide Protein Data Bank (PDB) database (https://www.rcsb.org/).^[28] They were imported to AutoDockTools^[29] for hydrogenation, dehydration, and other pretreatments. Then, molecular docking of the receptor and ligand was conducted to analyze its binding activity. The docking results were visualized using the PyMol software.^[30]

2.3. Experimental verification

2.3.1. Cell culture, drugs, and reagents.

The SKOV3 cells were maintained in DMEM (Gibco, USA) with 10% FBS (Gibco, USA), 100 U/mL penicillin, and 100 g/mL streptomycin (Sigma, USA). The cells were cultured at 37°C in a water-saturated atmosphere under 5% CO₂.

Quercetin (Sigma, USA, purity ≥ 99.0%) and luteolin and wogonin (Shanghai Yuanye, China, purity ≥ 98.0%) were dissolved in dimethyl sulfoxide (DMSO)The final amount of DMSO added to the cell culture medium was <0.1% to ensure that there were no effects on cells.

MTT Kit (CCK) − 8 (Beyotime, China), paraformaldehyde (Beyotime, China), crystal violet (Sigma, USA), TRIzol solution (Life, China), ReverTra Ace Qpcr RT Kit (Takara, Japan), SYBR-Green Real-Time PCR Master Mix kit (Takara, Japan), Primers (Sangon Biotech, China), BCA protein Quantifier kit (Invitrogen, USA), and anti-PI3K, anti-AKT, anti-p-AKT, anti-Caspase (CST, USA) were used in this study.

2.3.2. Cell proliferation assay.

The MTT assay kit was used to assess cell proliferation. The SKOV3 cells at the logarithmic growth stage were digested with 0.25% trypsin and cultured in a 96-well plate at a density of 4 × 10³/well and a volume of 100 µL per well for 24 hours. This study had 5 replicate wells in each experimental group. After incubation, different concentrations of quercetin, luteolin, and wogonin (0, 30 µM, 60 µM, 90 µM, 120 µM, and 150 µM) were added to the culture. The cells were then cultured for 24 and 48 hours to observe the cell morphology. Then, 20 μL MTT solution was added to each well and incubated in the dark for 4 hours. Subsequently, the supernatant was discarded, and 150 μL DMSO was added to each well to dissolve methylzan crystals. Finally, the absorbance (OD) of the experimental wells was measured at 490 nm using a microplate reader. The Inhibition Rate (IR) of cell proliferation was calculated using the formula: $\mathrm{I}\mathrm{R}\mathrm{\%}=\frac{1-\mathrm{O}\mathrm{D}\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}\mathrm{o}\mathrm{f}\mathrm{d}\mathrm{r}\mathrm{u}\mathrm{g}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{p}}{ODvalueofcontrolgroup}\times 100\mathrm{\%}$. Half maximal inhibitory concentration(IC50) was calculated and was used for subsequent experiments.

2.3.3. Colony formation assay.

SKOV3 cells in the logarithmic growth stage were digested with 0.25% trypsin and inoculated in a 6 cm dish (1200 cells/well) to perform a colony formation assay. Then, the IC50 concentrations of quercetin, luteolin, and wogonin were added to the culture and incubated for 48 hours. The drug-containing medium was discarded and washed with PBS, and a fresh medium containing 10% FBS was added. The medium was changed every 3 days, and the culture was incubated for 8 days. The assay was stopped once the colonies were visible to the naked eye. The original culture medium was discarded, and the cells were carefully washed twice with precooled PBS. The cells were then fixed with 4% paraformaldehyde for 30 minutes. The colonies were stained with 0.1% crystal violet for 20 minutes at 37 °C, and colonies containing more than 50 cells were counted using microscopy. Finally, the colony formation rate was calculated using the formula: $\mathrm{C}\mathrm{o}\mathrm{l}\mathrm{o}\mathrm{n}\mathrm{y}\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}=\frac{numberofcellcolonies}{numberofinoculatedcells}\times 100\mathrm{\%}.$

2.3.4. Reverse transcription quantitative polymerase chain reaction (RT-qPCR).

After 48 hours of quercetin, luteolin, and wogonin intervention, SKOV3 cells were washed twice with precooled PBS. TRIzol solution was added to extract the total RNA of the SKOV3 cells. The PrimeScript RT reagent Kit was used to reverse RNA transcription into cDNA. The designed cDNA was used in the amplification process using TB Green® Premix Ex Taq. The accumulation of PCR products was monitored in real-time, and simultaneous determination of cycle threshold (Ct) values was conducted. Gene expression values were determined using the 2^−ΔΔCt method. The primer designs of the hub genes are shown in Table 1.

Table 1.

Primer sequences of PCR.

Gene	Primer sequence(5‘-3‘）	Length/bp
18s	F: CGTTGATTAAGTCCCTGCCCTT	137
	R: TCAAGTTCGACCGTCTTCTCAG
AKT1	F: AGCGACGTGGCTATTGTGAAG	96
	R: GCCATCATTCTTGAGGAGGAAGT
VEGFA	F: AGGGCAGAATCATCACGAAGT	75
	R: AGGGTCTCGATTGGATGGCA
Caspase 3	F: GAAATTGTGGAATTGATGCGTGA	164
	R: CTACAACGATCCCCTCTGAAAAA
Jun	F: TCCAAGTGCCGAAAAAGGAAG	78
	R: CGAGTTCTGAGCTTTCAAGGT
TNF	F: GAGGCCAAGCCCTGGTATG	91
	R: CGGGCCGATTGATCTCAGC

2.3.5. Western blotting analysis.

After 48 hours of quercetin, luteolin, and wogonin intervention, the protein lysate was added to the ice bath. The total protein concentration was determined quantitatively using the BCA protein concentration assay kit. After electrophoresis with 10% SDS-PAGE, the separated proteins from the gel were transferred onto the PVDF membrane, and 5% BSA blocking buffer was added and incubated for 1 hour. The primary antibodies against PI3K, Akt, P-Akt, Caspase-3, and GAPDH were used according to the manufactures’ instructions and incubated at 4 °C overnight. Next, the membrane was washed with TBST 5 times for 8 minutes each time, and then incubated with the corresponding secondary antibody (1:3000) at room temperature for 1 hour and then washed with TBST 5 times for 8 minutes each time. A ECL luminescence kit was used for color development, and exposed to the gel imaging system. Image J software was used to analyze protein bands and calculate gray values. The gray ratio of target protein/GAPDH protein represented its relative expression level.

2.4. Statistical analysis

The data were processed using SPSS 22.0 statistical software and presented as the mean ± standard deviation if they obeyed normal distribution. One-way ANOVA was used to compare the measurement data between single-factor design and multiple groups. The least significant difference method was used for multiple comparisons, and the Games–Howell test was used to test the variance. The results analysis map was prepared using GraphPad Prism 8.0 software.

3. Results

3.1. Network pharmacology analysis

3.1.1. Screening of active ingredients.

A total of 94 known active compounds of SBD, shown in Supplementary Table S1, http://links.lww.com/MD/L93, were identified from the TCMSP database. These active compounds were screened based on OB ≥ 30% and DL ≥ 0.18, yielding 29 active ingredients which met the conditions. The results are presented in Table 2.

Table 2.

Basic information on the main active ingredients of SBD.

Number	MOL ID	Active ingredients	OB (%)	DL
SBD1	MOL001040	(2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one	42.36	0.21
SBD2	MOL012245	5,7,4’-trihydroxy-6-methoxyflavanone	36.63	0.27
SBD3	MOL012246	5,7,4’-trihydroxy-8-methoxyflavanone	74.24	0.26
SBD4	MOL012248	5-hydroxy-7,8-dimethoxy-2-(4-methoxyphenyl)chromone	65.82	0.33
SBD5	MOL012250	7-hydroxy-5,8-dimethoxy-2-phenyl-chromone	43.72	0.25
SBD6	MOL012251	Chrysin-5-methylether	37.27	0.2
SBD7	MOL012252	9,19-cyclolanost-24-en-3-ol	38.69	0.78
SBD8	MOL002776	Baicalin	40.12	0.75
SBD9	MOL012254	campesterol	37.58	0.71
SBD10	MOL000953	CLR	37.87	0.68
SBD11	MOL000358	beta-sitosterol	36.91	0.75
SBD12	MOL012266	rivularin	37.94	0.37
SBD13	MOL001973	Sitosteryl acetate	40.39	0.85
SBD14	MOL012269	Stigmasta-5,22-dien-3-ol-acetate	46.44	0.86
SBD15	MOL012270	Stigmastan-3,5,22-triene	45.03	0.71
SBD16	MOL000449	Stigmasterol	43.83	0.76
SBD17	MOL000173	wogonin	30.68	0.23
SBD18	MOL001735	Dinatin	30.97	0.27
SBD19	MOL001755	24-Ethylcholest-4-en-3-one	36.08	0.76
SBD20	MOL002714	baicalein	33.52	0.21
SBD21	MOL002719	6-Hydroxynaringenin	33.23	0.24
SBD22	MOL002915	Salvigenin	49.07	0.33
SBD23	MOL000351	Rhamnazin	47.14	0.34
SBD24	MOL000359	sitosterol	36.91	0.75
SBD25	MOL005190	eriodictyol	71.79	0.24
SBD26	MOL005869	daucostero_qt	36.91	0.75
SBD27	MOL000006	luteolin	36.16	0.25
SBD28	MOL008206	Moslosooflavone	44.09	0.25
SBD29	MOL000098	quercetin	46.43	0.28

3.1.2. Potential targets of active ingredients.

Candidate targets of active components in SBD were searched on the TCMSP and,SwissTarget Prediction and PharmMapper databases. UniProt was used to standardize the nomenclature for the active compounds. After UniProt standardization, deduplication occurred. Duplicate data were removed, leaving 229 targets, shown in Supplementary Table S2, http://links.lww.com/MD/L94. The CytoScape 3.9.0 software was then used to build an “active ingredients–targets” interaction network, as shown in Figure 2. In the study, a total of 29 active components of SBD were identified. According to the degree value of the interaction between the active component and the target, 3 core active components (quercetin, luteolin, wogonin) were identified.

Figure 2.

“Ingredients–targets” network construction. The light orange prism nodes represent the targets, and the light blue round nodes represent SBD ingredients. SBD = Scutellaria barbata D. Don.

3.1.3. Potential targets of disease.

The GeneCards, DisGeNet, OMIM, and Drugbank databases were used to identify reviewed therapeutic targets in SBD. The scores in GeneCards and DisGeNet were used to define a set of high-confidence interactions between diseases and potential targets, with a relevance score ≥ 12.7 in GeneCards and a score-GDA ≥ 0.1 in DisGeNet. As a result, 1141 ovarian cancer-related targets were acquired from GeneCards, 1224 ovarian cancer-related targets were obtained from DisGeNet, 44 ovarian cancer-related targets were obtained from OMIM, and 75 ovarian cancer-related targets were obtained from Drugbank. After removing the duplicate targets, 1969 targets for ovarian cancer remained, as shown in Supplementary Table S3, http://links.lww.com/MD/L95.

3.1.4. Drug–disease intersection targets.

Venn analysis was performed using the 229 targets of SBD active components and 1969 ovarian cancer-related target genes, and 137 drug–disease intersection gene targets were obtained for further analysis, as shown in Figure 3 and Table 3.

Figure 3.

(A) Venn diagram shows ovarian cancer-related targets among the 4 databases. (B) Venn diagram of common intersecting targets genes in SBD and Ovarian cancer. SBD = Scutellaria barbata D. Don.

Table 3.

Potential targets of SBD against ovarian cancer.

Serial number	Gene name	Protein name	Uniprot ID
1	PTGS1	Prostaglandin G/H synthase 1	P23219
2	ESR1	Estrogen receptor	P03372
3	PTGS2	Prostaglandin G/H synthase 2	P35354
4	PRKACA	cAMP-dependent protein kinase catalytic subunit alpha	P17612
5	PGR	Progesterone receptor	P06401
6	NR3C1	Glucocorticoid receptor	P04150
7	PIK3CG	Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform	P48736
8	NOS2	Nitric oxide synthase, inducible	P35228
9	KCNH2	Potassium voltage-gated channel subfamily H member 2	Q12809
10	AR	Androgen receptor	P10275
11	F10	Coagulation factor X	P00742
12	NOS3	Nitric oxide synthase, endothelial	P29474
13	ESR2	Estrogen receptor beta	Q92731
14	GSK3B	Glycogen synthase kinase-3 beta	P49841
15	CDK2	Cyclin-dependent kinase 2	P24941
16	CHEK1	Serine/threonine-protein kinase Chk1	O14757
17	PRSS1	Trypsin-1	P07477
18	PPARG	Peroxisome proliferator-activated receptor gamma	P37231
19	ADRA1D	Alpha-1D adrenergic receptor	P25100
20	MAPK14	Mitogen-activated protein kinase 14	Q16539
21	ADRA1A	Alpha-1A adrenergic receptor	P35348
22	PTPN1	Tyrosine-protein phosphatase non-receptor type 1	P18031
23	BCL2	Apoptosis regulator Bcl-2	P10415
24	BAX	Apoptosis regulator BAX	Q07812
25	CASP9	Caspase-9	P55211
26	JUN	Transcription factor AP-1	P05412
27	CASP3	Caspase-3	P42574
28	CASP8	Caspase-8	Q14790
29	PRKCA	Protein kinase C alpha type	P17252
30	TGFB1	Transforming growth factor beta-1 proprotein	P01137
31	PON1	Serum paraoxonase/arylesterase 1	P27169
32	MAP2	Microtubule-associated protein 2	P11137
33	KDR	Vascular endothelial growth factor receptor 2	P35968
34	PLAU	Urokinase-type plasminogen activator	P00749
35	RELA	Transcription factor p65	Q04206
36	AKT1	RAC-alpha serine/threonine-protein kinase	P31749
37	CCND1	G1/S-specific cyclin-D1	P24385
38	CDKN1A	Cyclin-dependent kinase inhibitor 1	P38936
39	EIF6	Eukaryotic translation initiation factor 6	P56537
40	TNF	Tumor necrosis factor	P01375
41	IL6	Interleukin-6	P05231
42	AHSA1	Activator of 90 kDa heat shock protein ATPase homolog 1	O95433
43	TP53	Cellular tumor antigen p53	P04637
44	MMP1	Interstitial collagenase	P03956
45	CCL2	C-C motif chemokine 2	P13500
46	PRKCD	Protein kinase C delta type	Q05655
47	FN1	Fibronectin	P02751
48	CXCL8	Interleukin-8	P10145
49	MCL1	Induced myeloid leukemia cell differentiation protein Mcl-1	Q07820
50	VEGFA	Vascular endothelial growth factor A	P15692
51	FOS	Proto-oncogene c-Fos	P01100
52	MMP9	Matrix metalloproteinase-9	P14780
53	HIF1A	Hypoxia-inducible factor 1-alpha	Q16665
54	FOSL1	Fos-related antigen 1	P15407
55	CDK1	Cyclin-dependent kinase 1	P06493
56	CCNB1	G2/mitotic-specific cyclin-B1	P14635
57	MPO	Myeloperoxidase	P05164
58	AHR	Aryl hydrocarbon receptor	P35869
59	IGF2	Insulin-like growth factor II	P01344
60	CYCS	Cytochrome c	P99999
61	ALOX12	Polyunsaturated fatty acid lipoxygenase ALOX12	P18054
62	NFATC1	Nuclear factor of activated T-cells, cytoplasmic 1	O95644
63	CCNA2	Cyclin-A2	P20248
64	HMOX1	Heme oxygenase 1	P09601
65	NFE2L2	Nuclear factor erythroid 2-related factor 2	Q16236
66	NQO1	NAD	P15559
67	EGFR	Epidermal growth factor receptor	P00533
68	BCL2L1	Bcl-2-like protein 1	Q07817
69	MMP2	72 kDa type IV collagenase	P08253
70	MAPK1	Mitogen-activated protein kinase 1	P28482
71	IL10	Interleukin-10	P22301
72	RB1	Retinoblastoma-associated protein	P06400
73	CDK4	Cyclin-dependent kinase 4	P11802
74	NFKBIA	NF-kappa-B inhibitor alpha	P25963
75	TOP1	DNA topoisomerase 1	P11387
76	MDM2	E3 ubiquitin-protein ligase Mdm2	Q00987
77	PCNA	Proliferating cell nuclear antigen	P12004
78	ERBB2	Receptor tyrosine-protein kinase erbB-2	P04626
79	CASP7	Caspase-7	P55210
80	ICAM1	Intercellular adhesion molecule 1	P05362
81	BIRC5	Baculoviral IAP repeat-containing protein 5	O15392
82	IL2	Interleukin-2	P60568
83	TYR	Tyrosinase	P14679
84	IFNG	Interferon gamma	P01579
85	IL4	Interleukin-4	P05112
86	TOP2A	DNA topoisomerase 2-alpha	P11388
87	GSTP1	Glutathione S-transferase P	P09211
88	XIAP	E3 ubiquitin-protein ligase XIAP	P98170
89	INSR	Insulin receptor	P06213
90	CD40LG	CD40 ligand	P29965
91	MET	Hepatocyte growth factor receptor	P08581
92	MMP3	Stromelysin-1	P08254
93	EGF	Pro-epidermal growth factor	P01133
94	CDKN2A	Cyclin-dependent kinase inhibitor 2A	P42771
95	ELK1	ETS domain-containing protein Elk-1	P19419
96	ODC1	Ornithine decarboxylase	P11926
97	RAF1	RAF proto-oncogene serine/threonine-protein kinase	P04049
98	SOD1	Superoxide dismutase	P00441
99	STAT1	Signal transducer and activator of transcription 1-alpha/beta	P42224
100	HSPA5	Endoplasmic reticulum chaperone BiP	P11021
101	CYP3A4	Cytochrome P450 3A4	P08684
102	CYP1A2	Cytochrome P450 1A2	P05177
103	CAV1	Caveolin-1	Q03135
104	MYC	Myc proto-oncogene protein	P01106
105	F3	Tissue factor	P13726
106	GJA1	Gap junction alpha-1 protein	P17302
107	CYP1A1	Cytochrome P450 1A1	P04798
108	IL1B	Interleukin-1 beta	P01584
109	VCAM1	Vascular cell adhesion protein 1	P19320
110	HSPB1	Heat shock protein beta-1	P04792
111	SULT1E1	Sulfotransferase 1E1	P49888
112	NR1I2	Nuclear receptor subfamily 1 group I member 2	O75469
113	CYP1B1	Cytochrome P450 1B1	Q16678
114	THBD	Thrombomodulin	P07204
115	SERPINE1	Plasminogen activator inhibitor 1	P05121
116	ALOX5	Polyunsaturated fatty acid 5-lipoxygenase	P09917
117	PTEN	Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN	P60484
118	IL1A	Interleukin-1 alpha	P01583
119	ABCA2	ATP-binding cassette sub-family A member 2	Q9BZC7
120	PARP1	Poly	P09874
121	CXCL11	C-X-C motif chemokine 11	O14625
122	CHEK2	Serine/threonine-protein kinase Chk2	O96017
123	CLDN4	Claudin-4	O14493
124	HSF1	Heat shock factor protein 1	Q00613
125	CRP	C-reactive protein	P02741
126	CHUK	Inhibitor of nuclear factor kappa-B kinase subunit alpha	O15111
127	SPP1	Osteopontin	P10451
128	RUNX2	Runt-related transcription factor 2	Q13950
129	RASSF1	Ras association domain-containing protein 1	Q9NS23
130	E2F1	Transcription factor E2F1	Q01094
131	E2F2	Transcription factor E2F2	Q14209
132	CTSD	Cathepsin D	P07339
133	IGFBP3	Insulin-like growth factor-binding protein 3	P17936
134	IRF1	Interferon regulatory factor 1	P10914
135	ERBB3	Receptor tyrosine-protein kinase erbB-3	P21860
136	HK2	Hexokinase-2	P52789
137	GSTM1	Glutathione S-transferase Mu 1	P09488

3.1.5. PPI network analysis.

The String database was used to analyze the interactions between the 137 shared targets. The String analysis contained 137 nodes, 3071 link edges, and 956 expected edges at the medium confidence. The average node degree was 44.8, the average local clustering coefficient was 0.706, and the PPI enrichment P value was < 0.001. Afterward, the Cytoscape software was used to construct the PPI network. Different colors and rhombus sizes represent different biological information. The larger circles with darker colors (from orange to red) indicate stronger interactions, as shown in Figure 4A. In addition, a topological analysis of the 137 common targets was performed using a Cytoscape analysis module, shown in Table 4. The top 50 targets were ranked according to their degree values, represented by a bar graph, shown in Figure 4B.

Figure 4.

PPI network of 137 target genes and top 50 targets from the PPI network. (A) Visualization of the PPI network of 137 target genes: from orange to red, the larger rhombus suggests stronger interactions, and the central region is the top 50 targets. (B) A histogram of the top 50 targets from the PPI network. The numbers represent degree values. PPI = protein-protein interaction.

Table 4.

The topological analysis of top 50 genes. Data were ranked by degree.

Gene name	Degree	BetweennessCentrality	ClosenessCentrality	TopologicalCoefficient
TP53	115	0.043158555	0.88590604	0.383662714
AKT1	111	0.04562599	0.862745098	0.386022386
VEGFA	102	0.024739627	0.814814815	0.409907903
JUN	102	0.023990559	0.814814815	0.413770053
TNF	99	0.026484946	0.8	0.408478727
ESR1	99	0.038618348	0.8	0.398454239
MYC	98	0.020122447	0.795180723	0.412569573
IL6	96	0.022084432	0.785714286	0.41635101
CASP3	96	0.018645246	0.785714286	0.426136364
EGFR	93	0.025142474	0.771929825	0.417481264
HIF1A	93	0.01727923	0.771929825	0.430188987
EGF	88	0.017439426	0.75	0.434831267
PTGS2	88	0.024499214	0.75	0.431215565
CCND1	88	0.01359779	0.75	0.432162534
IL1B	87	0.012254171	0.745762712	0.430947405
PTEN	86	0.013018932	0.741573034	0.431994362
MMP9	85	0.014327873	0.737430168	0.442869875
ERBB2	80	0.017517308	0.717391304	0.435132576
FOS	79	0.010367113	0.709677419	0.458691661
PPARG	76	0.010672586	0.70212766	0.447468102
FN1	75	0.006577537	0.698412698	0.464242424
CXCL8	74	0.007114304	0.694736842	0.457002457
BCL2L1	74	0.006162498	0.691099476	0.471322468
CASP8	74	0.00737131	0.694736842	0.463042588
NFKBIA	72	0.005065923	0.683937824	0.479961832
CDKN2A	70	0.011972217	0.676923077	0.460196292
MAPK1	68	0.009270717	0.673469388	0.448975045
CYCS	68	0.009060935	0.673469388	0.467134581
IL10	66	0.004331235	0.663316583	0.480222068
MMP2	66	0.003485736	0.666666667	0.4890955
CCL2	66	0.005349782	0.666666667	0.46751607
RELA	63	0.003272746	0.653465347	0.495456198
MDM2	63	0.005222524	0.656716418	0.456108706
CASP9	62	0.002972236	0.650246305	0.497168185
CDKN1A	62	0.003743578	0.650246305	0.491135188
HMOX1	61	0.005224187	0.647058824	0.468777375
STAT1	61	0.003952042	0.647058824	0.49518208
MAPK14	60	0.004906218	0.643902439	0.49389313
ICAM1	58	0.002734795	0.637681159	0.484601211
GSK3B	57	0.004944944	0.637681159	0.487107921
IL2	56	0.002933688	0.628571429	0.509478022
IL4	56	0.002752027	0.631578947	0.496864776
AR	56	0.004264953	0.634615385	0.4745671
NOS3	56	0.004161875	0.634615385	0.472267316
MCL1	56	0.002805555	0.634615385	0.501217532
CAV1	55	0.010162808	0.631578947	0.484848485
CDK4	55	0.005392789	0.625592417	0.472867133
TGFB1	55	0.001273775	0.631578947	0.518595041
IFNG	54	0.00179449	0.625592417	0.508340401
PARP1	53	0.003632184	0.61971831	0.478664731

3.1.6. GO/KEGG function and pathway enrichment analysis.

The Metascape data platform was used for enrichment analysis of the 137 relevant ovarian cancer-related targets of SBD. The results were visualized using online biological tools. GO and KEGG pathways with FDR < 0.01 were considered significantly enriched. The GO and KEGG enrichment analyses were performed on the 137 therapeutic targets to obtain 2185 pathways (P < .01). In total, 1766, 81, 145, and 193 pathways were identified in GOBP, GOCC, GOMF, and KEGG, respectively (all P < .01), as shown in Supplementary Table S4, http://links.lww.com/MD/L96. The top 20 enriched GO terms are listed in Figure 5. The biological process involves responses to inorganic substances, xenobiotic stimuli, hormones, reactive oxygen species, positive cellular component movement regulation, and positive cell death regulation. On the other hand, CC involves a transcription regulator complex, membrane raft, membrane microdomain, cyclin-dependent protein kinase holoenzyme complex, vesicle lumen, and outer organelle membrane, and MF involves kinase binding, DNA-binding transcription factor binding, transcription factor binding, protein kinase activity, protein domain-specific binding, and cytokine receptor binding.

Figure 5.

GO enrichment analysis of the 137 potential therapeutic targets. (A) Dot plot of GO enrichment analysis: the top 20 BP terms. (B) GO enrichment analysis: the top 20 MF terms. (C) GO enrichment analysis: the top 20 CC terms. (A–C) Node color is displayed in a gradient from red to green in descending order of the P value. The size of the nodes is arranged in ascending order of the number of genes. (D) Enrichment of GO Term (the top ten) BP, CC, MF 3 in one histogram. BP = biological processes, CC = cellular components, GO = Gene Ontology, MF = molecular function.

The top 20 noticeably enriched KEGG terms related to cancer are listed in Figure 6. The results of the KEGG enrichment analysis showed that the cancer pathway and PI3K-Akt signaling pathway are the main anti-ovarian cancer pathways of SBD. The KEGG Mapper platform was used to draw the pathway mechanism diagram of the 2 signaling pathways. The specific mechanism of SBD in treating ovarian cancer was further analyzed according to the pathway mechanism diagram.

Figure 6.

KEGG pathway enrichment analysis of the 137 potential therapeutic targets. (A) Bubble diagram of KEGG analysis. (B) Bar plot of KEGG analysis, sorted by the importance of–log10(P) of each lane. (C) Pathways in cancer. (d) PI3K-AKT signal pathway (red nodes represent 50 core targets in the pathway). KEGG = Kyoto Encyclopedia of Genes and Genomes.

Table 5 shows the main signaling pathways with enrichment targets ≥ 20. The pathways include the cancer pathway, PI3K-Akt signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, Proteoglycans in cancer, Chemical carcinogenesis- receptor activation, MicroRNAs in cancer, Apoptosis, p53 signaling pathway, TNF signaling pathway, IL-17 signaling pathway and so on.

Table 5.

The signaling pathway of KEGG enrichment target number ≥ 20.

GO	Pathway	Count	Genes
hsa05200	Pathways in cancer	65	AKT1 XIAP BIRC5 AR BAX CCND1 BCL2 BCL2L1 CASP3 CASP7 CASP8 CASP9 CCNA2 CDK2 CDK4 CDKN1A CDKN2A CHUK NQO1 E2F1 E2F2 EGF EGFR ELK1 ERBB2 ESR1 ESR2 FN1 FOS GSK3B GSTM1 GSTP1 HIF1A HMOX1 IFNG IGF2 IL2 IL4 IL6 CXCL8 JUN MDM2 MET MMP1 MMP2 MMP9 MYC NFE2L2 NFKBIA NOS2 PPARG PRKACA PRKCA MAPK1 PTEN PTGS2 RAF1 RB1 RELA STAT1 TGFB1 TP53 VEGFA RASSF1 CYCS
hsa05417	Lipid and atherosclerosis	36	AKT1 BAX BCL2 BCL2L1 CASP3 CASP7 CASP8 CASP9 CD40LG CHUK MAPK14 CYP1A1 FOS GSK3B HSPA5 ICAM1 IL1B IL6 CXCL8 JUN MMP1 MMP3 MMP9 NFATC1 NFE2L2 NFKBIA NOS3 PPARG PRKCA MAPK1 RELA CCL2 TNF TP53 VCAM1 CYCS
hsa05163	Human cytomegalovirus infection	35	AKT1 BAX CCND1 CASP3 CASP8 CASP9 CDK4 CDKN1A CDKN2A CHUK MAPK14 E2F1 E2F2 EGFR ELK1 GSK3B IL1B IL6 CXCL8 MDM2 MYC NFATC1 NFKBIA PRKACA PRKCA MAPK1 PTGS2 RAF1 RB1 RELA CCL2 TNF TP53 VEGFA CYCS
hsa04151	PI3K-Akt signaling pathway	35	AKT1 CCND1 BCL2 BCL2L1 CASP9 CDK2 CDK4 CDKN1A CHUK EGF EGFR ERBB2 ERBB3 FN1 GSK3B IGF2 IL2 IL4 IL6 INSR KDR MCL1 MDM2 MET MYC NOS3 PIK3CG PRKCA MAPK1 PTEN RAF1 RELA SPP1 TP53 VEGFA
hsa05161	Hepatitis B	34	AKT1 BIRC5 BAX BCL2 CASP3 CASP8 CASP9 CCNA2 CDK2 CDKN1A CHUK MAPK14 E2F1 E2F2 ELK1 FOS IL6 CXCL8 JUN MMP9 MYC NFATC1 NFKBIA PCNA PRKCA MAPK1 RAF1 RB1 RELA STAT1 TGFB1 TNF TP53 CYCS
hsa05166	Human T-cell leukemia virus 1 infection	33	AKT1 XIAP BAX CCND1 BCL2L1 CCNA2 CDK2 CDK4 CDKN1A CDKN2A CHEK1 CHUK E2F1 E2F2 ELK1 FOS ICAM1 IL2 IL6 JUN MYC NFATC1 NFKBIA PRKACA MAPK1 PTEN RB1 RELA TGFB1 TNF TP53 FOSL1 CHEK2
hsa05167	Kaposi sarcoma-associated herpesvirus infection	32	AKT1 BAX CCND1 CASP3 CASP8 CASP9 CDK4 CDKN1A CHUK MAPK14 E2F1 E2F2 FOS GSK3B HIF1A ICAM1 IL6 CXCL8 JUN MYC NFATC1 NFKBIA PIK3CG MAPK1 PTGS2 RAF1 RB1 RELA STAT1 TP53 VEGFA CYCS
hsa04933	AGE-RAGE signaling pathway in diabetic complications	30	AKT1 BAX CCND1 BCL2 CASP3 CDK4 MAPK14 F3 FN1 ICAM1 IL1A IL1B IL6 CXCL8 JUN MMP2 NFATC1 NOS3 SERPINE1 PRKCA PRKCD MAPK1 RELA CCL2 STAT1 TGFB1 THBD TNF VCAM1 VEGFA
hsa04218	Cellular senescence	29	AKT1 CCND1 CCNA2 CCNB1 CDK1 CDK2 CDK4 CDKN1A CDKN2A CHEK1 MAPK14 E2F1 E2F2 IGFBP3 IL1A IL6 CXCL8 MDM2 MYC NFATC1 SERPINE1 MAPK1 PTEN RAF1 RB1 RELA TGFB1 TP53 CHEK2
hsa05205	Proteoglycans in cancer	29	AKT1 CCND1 CASP3 CAV1 CDKN1A MAPK14 EGFR ELK1 ERBB2 ERBB3 ESR1 FN1 HIF1A IGF2 KDR MDM2 MET MMP2 MMP9 MYC PLAU PRKACA PRKCA MAPK1 RAF1 TGFB1 TNF TP53 VEGFA
hsa04010	MAPK signaling pathway	29	AKT1 CASP3 CHUK MAPK14 EGF EGFR ELK1 ERBB2 ERBB3 FOS HSPB1 IGF2 IL1A IL1B INSR JUN KDR MET MYC NFATC1 PRKACA PRKCA MAPK1 RAF1 RELA TGFB1 TNF TP53 VEGFA
hsa05165	Human papillomavirus infection	29	AKT1 BAX CCND1 CASP3 CASP8 CCNA2 CDK2 CDK4 CDKN1A CHUK E2F1 EGF EGFR FN1 GSK3B IRF1 MDM2 PRKACA MAPK1 PTEN PTGS2 RAF1 RB1 RELA SPP1 STAT1 TNF TP53 VEGFA
hsa05207	Chemical carcinogenesis - receptor activation	28	AHR AKT1 XIAP BIRC5 AR CCND1 BCL2 CYP1A1 CYP1A2 CYP1B1 CYP3A4 E2F1 EGF EGFR ESR1 ESR2 FOS GSTM1 JUN MYC PGR PRKACA PRKCA MAPK1 RAF1 RB1 RELA VEGFA
hsa05418	Fluid shear stress and atherosclerosis	27	AKT1 BCL2 CAV1 CHUK MAPK14 NQO1 FOS GSTM1 GSTP1 HMOX1 ICAM1 IFNG IL1A IL1B JUN KDR MMP2 MMP9 NFE2L2 NOS3 RELA CCL2 THBD TNF TP53 VCAM1 VEGFA
hsa05160	Hepatitis C	27	AKT1 BAX CCND1 CASP3 CASP8 CASP9 CDK2 CDK4 CDKN1A CHUK CLDN4 E2F1 E2F2 EGF EGFR GSK3B IFNG MYC NFKBIA MAPK1 RAF1 RB1 RELA STAT1 TNF TP53 CYCS
hsa05225	Hepatocellular carcinoma	27	AKT1 BAX CCND1 BCL2L1 CDK4 CDKN1A CDKN2A NQO1 E2F1 E2F2 EGFR ELK1 GSK3B GSTM1 GSTP1 HMOX1 IGF2 MET MYC NFE2L2 PRKCA MAPK1 PTEN RAF1 RB1 TGFB1 TP53
hsa05169	Epstein-Barr virus infection	27	AKT1 BAX CCND1 BCL2 CASP3 CASP8 CASP9 CCNA2 CDK2 CDK4 CDKN1A CHUK MAPK14 E2F1 E2F2 ICAM1 IL6 JUN MDM2 MYC NFKBIA RB1 RELA STAT1 TNF TP53 CYCS
hsa05215	Prostate cancer	26	AKT1 AR CCND1 BCL2 CASP9 CDK2 CDKN1A CHUK E2F1 E2F2 EGF EGFR ERBB2 GSK3B GSTP1 MDM2 MMP3 MMP9 NFKBIA PLAU MAPK1 PTEN RAF1 RB1 RELA TP53
hsa05208	Chemical carcinogenesis - reactive oxygen species	26	AHR AKT1 CHUK MAPK14 CYP1A1 CYP1A2 CYP1B1 NQO1 EGF EGFR FOS GSTM1 HIF1A HMOX1 JUN MET NFE2L2 NFKBIA PRKCD MAPK1 PTEN PTPN1 RAF1 RELA SOD1 VEGFA
hsa05206	MicroRNAs in cancer	26	CCND1 BCL2 CASP3 CDKN1A CDKN2A CYP1B1 E2F1 E2F2 EGFR ERBB2 ERBB3 HMOX1 MCL1 MDM2 MET MMP9 MYC PLAU PRKCA MAPK1 PTEN PTGS2 RAF1 TP53 VEGFA RASSF1
hsa05222	Small cell lung cancer	24	AKT1 XIAP BAX CCND1 BCL2 BCL2L1 CASP3 CASP9 CDK2 CDK4 CDKN1A CHUK E2F1 E2F2 FN1 MYC NFKBIA NOS2 PTEN PTGS2 RB1 RELA TP53 CYCS
hsa01522	Endocrine resistance	24	AKT1 BAX CCND1 BCL2 CDK4 CDKN1A CDKN2A MAPK14 E2F1 E2F2 EGFR ERBB2 ESR1 ESR2 FOS JUN MDM2 MMP2 MMP9 PRKACA MAPK1 RAF1 RB1 TP53
hsa04210	Apoptosis	23	PARP1 AKT1 XIAP BIRC5 BAX BCL2 BCL2L1 CASP3 CASP7 CASP8 CASP9 CHUK CTSD FOS JUN MCL1 NFKBIA MAPK1 RAF1 RELA TNF TP53 CYCS
hsa05162	Measles	23	AKT1 BAX CCND1 BCL2 BCL2L1 CASP3 CASP8 CASP9 CDK2 CDK4 CHUK FOS GSK3B IL1A IL1B IL2 IL6 JUN NFKBIA RELA STAT1 TP53 CYCS
hsa05164	Influenza A	23	AKT1 BAX CASP3 CASP8 CASP9 CDK4 CHUK ICAM1 IFNG IL1A IL1B IL6 CXCL8 NFKBIA PRKCA MAPK1 PRSS1 RAF1 RELA CCL2 STAT1 TNF CYCS
hsa05212	Pancreatic cancer	22	AKT1 BAX CCND1 BCL2L1 CASP9 CDK4 CDKN1A CDKN2A CHUK E2F1 E2F2 EGF EGFR ERBB2 MAPK1 RAF1 RB1 RELA STAT1 TGFB1 TP53 VEGFA
hsa04657	IL-17 signaling pathway	22	CASP3 CASP8 CHUK MAPK14 FOS GSK3B IFNG IL1B IL4 IL6 CXCL8 JUN MMP1 MMP3 MMP9 NFKBIA MAPK1 PTGS2 RELA CCL2 TNF FOSL1
hsa05145	Toxoplasmosis	22	AKT1 ALOX5 XIAP BCL2 BCL2L1 CASP3 CASP8 CASP9 CD40LG CHUK MAPK14 IFNG IL10 NFKBIA NOS2 PIK3CG MAPK1 RELA STAT1 TGFB1 TNF CYCS
hsa05224	Breast cancer	22	AKT1 BAX CCND1 CDK4 CDKN1A E2F1 E2F2 EGF EGFR ERBB2 ESR1 ESR2 FOS GSK3B JUN MYC PGR MAPK1 PTEN RAF1 RB1 TP53
hsa05170	Human immunodeficiency virus 1 infection	22	AKT1 BAX BCL2 BCL2L1 CASP3 CASP8 CASP9 CCNB1 CDK1 CHEK1 CHUK MAPK14 FOS JUN NFATC1 NFKBIA PRKCA MAPK1 RAF1 RELA TNF CYCS
hsa05022	Pathways of neurodegeneration - multiple diseases	22	BAX BCL2 BCL2L1 CASP3 CASP7 CASP8 CASP9 MAPK14 GSK3B HSPA5 IL1A IL1B IL6 NOS2 PRKCA MAPK1 PTGS2 RAF1 RELA SOD1 TNF CYCS
hsa05219	Bladder cancer	21	CCND1 CDK4 CDKN1A CDKN2A E2F1 E2F2 EGF EGFR ERBB2 CXCL8 MDM2 MMP1 MMP2 MMP9 MYC MAPK1 RAF1 RB1 TP53 VEGFA RASSF1
hsa04115	p53 signaling pathway	21	BAX CCND1 BCL2 BCL2L1 CASP3 CASP8 CASP9 CCNB1 CDK1 CDK2 CDK4 CDKN1A CDKN2A CHEK1 IGFBP3 MDM2 SERPINE1 PTEN TP53 CHEK2 CYCS
hsa04668	TNF signaling pathway	21	AKT1 CASP3 CASP7 CASP8 CHUK MAPK14 FOS ICAM1 IL1B IL6 IRF1 JUN MMP3 MMP9 NFKBIA MAPK1 PTGS2 RELA CCL2 TNF VCAM1
hsa05152	Tuberculosis	21	AKT1 BAX BCL2 CASP3 CASP8 CASP9 MAPK14 CTSD IFNG IL1A IL1B IL6 IL10 NOS2 MAPK1 RAF1 RELA STAT1 TGFB1 TNF CYCS
hsa05132	Salmonella infection	21	AKT1 BAX BCL2 CASP3 CASP7 CASP8 CHUK MAPK14 FOS IL1B IL6 CXCL8 JUN MYC NFKBIA PIK3CG MAPK1 RAF1 RELA TNF CYCS
hsa05142	Chagas disease	20	AKT1 CASP8 CHUK MAPK14 FOS IFNG IL1B IL2 IL6 CXCL8 IL10 JUN NFKBIA NOS2 SERPINE1 MAPK1 RELA CCL2 TGFB1 TNF
hsa04625	C-type lectin receptor signaling pathway	20	AKT1 CASP8 CHUK MAPK14 IL1B IL2 IL6 IL10 IRF1 JUN MDM2 NFATC1 NFKBIA PRKCD MAPK1 PTGS2 RAF1 RELA STAT1 TNF
hsa04510	Focal adhesion	20	AKT1 XIAP CCND1 BCL2 CAV1 EGF EGFR ELK1 ERBB2 FN1 GSK3B JUN KDR MET PRKCA MAPK1 PTEN RAF1 SPP1 VEGFA
hsa05131	Shigellosis	20	AKT1 BAX BCL2 BCL2L1 CHUK MAPK14 EGFR GSK3B HK2 IL1B CXCL8 JUN MDM2 NFKBIA PRKCD MAPK1 RELA TNF TP53 CYCS

In addition, this study constructed a Compound-Target-Pathway network, shown in Figure 7. Analysis of the Compound-Target-Pathway network revealed comprehensive information and intricate relationships that manage cellular activities. Statistical analysis showed that several proteins participated in the top 20 pathways with a high frequency, indicating that they play an important role in the enrichment pathway. The core proteins are TP53, AKT1, VEGFA, JUN, TNF, ESR1, IL-6, and Caspase-3. Therefore, these proteins are critical in the SBD treatment of ovarian cancer.

Figure 7.

Analysis of Compound-Target-Pathway network. (A) An integrated network of Compound-Target-Pathway of SBD for ovarian cancer: SBD is represented by a hexagon (red), active ingredients by squares (blue), target genes by rhombus (orange), and pathways by arrowheads (green). (B) Network of 20 pathways and their core shared targets: pathways are represented by arrowheads (green) and shared targets by rhombus (orange).

3.2. Molecular docking results and analysis

The interactions between ligands and target proteins is shown in Table 6 and Figure 8. Electrostatic force and van der Waals force are the main forces between ligand and target protein. The lower the binding energy between ligand and receptor was, the more stable the binding conformation and the greater the possibility of interaction. The molecular docking results showed that the binding energy between the effective chemical active ingredient and the key target protein was less than −10 kcal/mol, suggesting that the binding activity between the effective core ingredient and the core target protein is stable.

Table 6.

Docking results of core target proteins with bioactive components.

Core target	PDB ID	Binding energy/(kcal Mol-1)
		Quercetin	Luteolin	Wogonin
AKT1	1H10	−6.2	−6.0	−5.9
VEGFA	6D3O	−6.7	−6.5	−6.5
JUN	1JNM	−8.0	−8.1	−6.8
TNF	1A8M	−6.6	−7.1	−6.3
Caspase-3	1GFW	−6.9	−7.8	−7.3

Figure 8.

Binding mode of protein and different ligands. (A) Binding mode of AKT1 with quercetin, luteolin and wogonin. (B) Binding mode of Caspase-3 with quercetin, luteolin and wogonin.

3.3. Experimental verification

3.3.1. Quercetin, luteolin, and wogonin inhibits SKOV3 cell proliferation.

The MTT results showed that quercetin, luteolin, and wogonin inhibit the proliferation of SKOV3 cells in a time- and concentration-dependent manner. The study found that the IR of luteolin on the SKOV3 cells was higher than quercetin and wogonin. The results show that the IC50 of quercetin, luteolin, and wogonin in SKOV3 cells treated for 48 hours were 121.43μmol/L, 95.27μmol/L, and 115.57μmol/L respectively, as shown in Figure 9A. Therefore, these concentrations were selected as the intervention concentrations of quercetin, luteolin, and wogonin in subsequent experiments. The plate colony formation assay results show that the colony formation of SKOV3 cells decreased after treatment with quercetin, luteolin, and wogonin than the nontreated groups, as shown in Figure 9B. These results indicate that the 3 core components have an antiproliferative effect on SKOV3 cells. In conclusion, quercetin, luteolin, and wogonin inhibit SKOV3 cell proliferation in a time- and concentration-dependent manner.

Figure 9.

Quercetin, luteolin, and wogonin inhibit SKOV3 cell proliferation. (A) SKOV3 cells were exposed to a culture medium containing various concentrations of quercetin, luteolin, and wogonin for 24 and 48 hours. The cell viability was measured using the MTT assay. (B) A colony-forming growth assay detection of cell colony formation ability. The colonies were counted and captured. Data are represented as mean ± SD (n = 3). *P < .05, **P < .01, ***P < .001 versus the non-treated group.

3.3.2. Quercetin, luteolin, and wogonin downregulate the mRNA levels of AKT1, VEGFA, JUN, and TNF and upregulate Caspase-3 in SKOV3 cells.

The PPI network screened the top 50 genes, and RT-qPCR verified the expression of 5 key genes. The RT-qPCR results in Figure 10A show that the 5 genes changed as expected after 48 hours of quercetin, luteolin, and wogonin treatment at IC50 concentrations. The mRNA expression levels of AKT1, VEGFA, JUN, and TNF decreased, while the mRNA expression level of Caspase-3 increased in SKOV3 cells treated with quercetin, luteolin, and wogonin than the control group. Moreover, luteolin has the most significant effect on downregulating the expression of AKT1, VEGFA, JUN, and TNF. At the same time, gene expression is shown as a heat map in Figure 10B.

Figure 10.

(A) RT-qPCR analysis of AKT1, VEGFA, JUN, TNF, and Caspase-3 mRNA expression in SKOV3 cells after stimulation with quercetin(121.43 μmol/L), luteolin (95.27 μmol/L), and wogonin(115.57 μmol/L) for 48 hours. (B) A heat map showing changes in gene expression. Figure 10B is a quantification of Figure 10A. (C, D) Western blotting analysis of markers involved in the PI3K/Akt signaling pathway. Protein levels of PI3K and P-Akt were significantly reduced, and the protein levels of Caspase-3 increased after stimulation with quercetin, luteolin, and wogonin for 48 hours in SKOV3 cells. Data are represented as mean ± SD (n = 3). *P < .05, **P < .01, ***P = .001, ns = no significant differences versus the non-treated group.

3.3.3. Quercetin, luteolin, and wogonin inhibit the activation of the PI3K-Akt signaling pathway.

According to KEGG analysis, the PI3K/Akt signaling pathway was inferred to be the key pathway involved in SBD anti-ovarian cancer pathways. To confirm the KEGG analysis, Western blotting was used to detect the effects of quercetin, luteolin, and wogonin induction of the PI3K/Akt pathway in SKOV3 cells. Figure 10C and 10D show that PI3K and P-Akt were significantly decreased, and the protein levels of Caspase-3 were increased in cells treated with quercetin, luteolin, and wogonin than in the control groups. The results also show that the expression of AKT did not change in the quercetin and luteolin groups. These results suggest that quercetin, luteolin, and wogonin can regulate ovarian cancer by inhibiting the activation of the PI3K/Akt pathway in SKOV3 cells.

4. Discussion

A recent study showed that ovarian cancer (OC) accounted for 3.4% of the 9.2 million new cancer cases in women worldwide in 2020. In addition, OC accounted for 4.7% of 4.4 million female cancer deaths.^[31] OC represents the eighth most common cancer and the fifth leading cause of cancer-related deaths among the female population.^[32] Although significant progress has been made in treatment of OC, the prognosis remains poor.^[33] SBD is a widely used medicinal herb in China because it exerts various medicinal properties, including antioxidative, anti-inflammatory, and anti-cancer effects.^[10] The monomer compounds of SBD targeting multiple pathways have been widely studied as antitumor drugs. However, the specific action and mechanism of SBD against ovarian cancer are yet to be clarified.

This study used network pharmacology to explore the molecular mechanism of SBD in treating ovarian cancer. In total, 29 SBD bioactive ingredients were obtained through database screening. The construction of the active ingredients-target interaction network showed that the main active ingredients of SBD were quercetin, luteolin, and wogonin, which are all flavonoids. Studies have shown that flavonoids have significant anticancer effects in various cancer models in vivo and in vitro.^[34] Natural flavonoids exert antioxidant, anti-inflammation, and anti-cancerous activities through multiple pathways, and have been shown to induce apoptosis of breast, colorectal, and prostate cancers, lower the nucleoside diphosphate kinase-B activity in lung, bladder, and colon cancers, inhibit cell proliferation and cell cycle arrest in various cancers.^[35] Quercetin is the major representative of the flavonoid subclass of flavonols, which widely exists in nuts, teas, vegetables, herbs, and the general daily diet of people.^[36] Liu et al investigated the effect of quercetin on apoptosis in mice with ovarian cancer and found that quercetin induced apoptosis via the mitochondria intrinsic and caspase-dependent pathways. Liu et al also reported that quercetin evoked endoplasmic reticulum stress in ovarian cancer resulting in mitochondria-mediated apoptosis.^[37] In the xenograft mouse model of ovarian cancer, quercetin treatment increased the anti-tumor effects of cisplatin. In mice treated with cisplatin in combination with pretreatment of quercetin, Bcl-2 expression was downregulated and apoptosis was increased observed compared with other groups. This study suggested that quercetin is a good candidate for adjuvant therapy in ovarian cancer.^[38]

On the other hand, related studies have shown that luteolin enhances the anti-tumor effect of cisplatin in ovarian cancer. Combination of luteolin and cisplatin is more effective in suppressing CAOV3/DDP cell growth and metastasis. Therefore, luteolin enhances cisplatin-induced apoptosis in cisplatin-resistant ovarian cancer CAOV3/DDP cells via decreasing Bcl-2 expression.^[39] Elsewhere, wogonin treatment increased apoptosis of tumor cells, enhanced the toxicity of TNF-α and TRAIL to tumor cells, blocked the tumor cell cycle, inhibited tumor angiogenesis, and synergized with chemotherapeutic drugs through ROS and Ca²⁺-mediated signaling pathways.^[40] In addition, wogonin acts as an inhibitor of ovary cancer growth. It also induces apoptotic changes and inhibits migration, leading to cell cycle arrest.^[41] These active ingredients are the main materials contributing to the anti-ovarian cancer effects of SBD. These ingredients indicate that SBD may produce its anti-ovarian cancer effects through multiple compounds acting on multiple targets and pathways.

In this study, we analyzed active ingredients of SBD and ovarian cancer targets using a PPI network to explore the pharmacological mechanism of SBD in the treatment of ovarian cancer. Consequently, we identified a subset of genes, including TP53, AKT1, VEGFA, JUN, TNF, ESR1, IL-6, and Caspase-3, which exert their effects via multiple different biochemical pathways. Notably, TP53 (p53) is the most widely studied gene in the human genome.^[42] Across all human cancers, TP53 is the most commonly altered gene with mutations occurring in approximately 50% of all cancer cases.^[43,44] Generally, TP53-mutated tumors exhibit an aggressive phenotype and are characterized by poor differentiation, increased invasiveness, and high metastatic potential. Mutations in TP53 are infrequent in low-grade serous carcinomas or serous borderline tumors but ubiquitous in high-grade serous ovarian cancer, i.e., TP53 mutations in up to 100% of the cases.^[45] AKT1 belongs to the protein kinase superfamily and regulates various cellular processes, including cellular metabolism, apoptosis, proliferation, transcription, and migration.^[46] AKT1 overexpression or amplification is responsible for the acquired resistance of ovarian cancer cells to paclitaxel.^[47] VEGFA is overexpressed in most solid tumors and malignant diseases and significantly stimulates tumor angiogenesis.^[48] In addition to affecting endothelial and tumor cells, VEGFA affects tumor function by targeting other cell types in the tumor microenvironment.^[49] Previous studies suggest that c-JUN activation subsequently induces both transcription of pro-apoptotic and pro-survival factors. In the context of ovarian cancer, c-JUN enhances the promoter activity of PD-L1 and promotes PD-L1 transcription and expression, which partly explains the upregulation of PD-L1 expression in ovarian cancer.^[50] TNF is widely studied in various cancers, including ovarian cancer, and is a major mediator of inflammation. Studies have shown that TNF-α expression is detected by RNA ISH technique in approximately 50% of high-grade serous and 83% of endometrioid ovarian carcinomas.^[51] IL-6 is a pleiotropic cytokine and an important biomarker in the cytokine cascade involved in the initiation and regulation of inflammation.^[52] IL-6 in ascites and the serum of patients with advanced ovarian cancer has been most strongly correlated with poor survival.^[53] ESR1 mutations are associated with resistance to endocrine therapy in metastatic breast cancer and their detection has elicited the development and current evaluation of novel, highly promising therapeutic strategies. In ovarian cancer, ESR1 mutations were detected in 15% of primary tumor tissues and in 13.8% of plasma-cfDNA samples tested using drop-off ddPCR.^[54] Caspases are a 15-member family of cysteine proteases, that modulate programmed cell death and inflammation.^[55] Caspase-3 activation is critical in breaking down the vital cellular proteins, nuclear fragmentation, and the ultimate death of the cell.^[56]

GO functional enrichment and KEGG pathway enrichment analyses found that the cancer pathway, PI3K-Akt signaling pathway and mitogen-activated protein kinase (MAPK) signaling pathway were the most significantly enriched gene sets. Among these, the PI3K/AKT/ pathway is the most frequently altered signaling, found in ~70% of ovarian cancer cases.^[57,58] PI3K pathway is frequently upregulated in ovarian cancer and activated PI3K signaling increases cell survival and chemoresistance.^[59] MAPK regulates signaling transduction pathways and capacity to control intracellular processes including cell survival, differentiation, proliferation, and apoptosis, via sequential phosphorylation of substrate protein Ser/Thr kinase protein cascades.^[60] Activation of the MAPK JNK/p38 pathway is regulated by Gal-1 and facilitates epithelial-mesenchymal transition, suggesting it may be a promising target for the prevention of epithelial ovarian cancer metastasis.^[61] Quercetin promotes cell viability loss, apoptosis, and autophagy in cancer by reducing β-catenin and HIF-1α stabilization, inducing caspase-3 activation and inhibiting Akt, mTOR, and ERK phosphorylation. By interfering with PI3K/Akt/mTOR pathways, Quercetin exerts its metabolic effect on cancer, inhibiting key enzymes of glycolysis and glucose uptake.^[62] Luteolin inhibits cancer cell proliferation, and survival pathways, including PI3K/Akt, NF-κB, and MAPKs in cancer cells, which may mimic the deprivation of growth factors that blocks the growth factor-triggered signaling pathways.^[63] Studies show that wogonin could significantly increase the sensitivity of cisplatin-resistant ovarian cancer cells to cisplatin by downregulating the PI3K/Akt pathway.^[64] Therefore, SBD may play an anti-ovarian cancer role via the cancer pathway, the PI3K/Akt signaling pathway, and the MAPK signaling pathway.

This study systematically predicted the material basis and mechanism of SBD in ovarian cancer treatment. In vitro experiments were consistent with the network pharmacology prediction results. Through experiments, the major active ingredients of SBD, i.e., quercetin, luteolin, and wogonin, inhibited proliferation and clone formation, induced apoptosis of SKOV3 cells, regulated mRNA expression of 5 key genes, and inhibited the activity of the PI3K/AKT signaling pathway. This provides a theoretical basis for clinical intervention of ovarian cancer by SBD as well as evidence and references for the development of TCM and monomers for treatment. Therefore, quercetin, luteolin, and wogonin may be ideal drugs for ovarian cancer treatment, however, the underlying molecular mechanism remains to be further studied.

We focused on the study of these 3 different types of small molecular of TCM on ovarian cancer, and the research content in this area is relatively small. In this study, 3 core active components (quercetin, luteolin, wogonin) of SBD were identified. The results of molecular docking has showed a good binding activity between the 3 most important components and the 5 important target proteins (AKT1, VEGFA, JUN, TNF and Caspase-3). And the results of experiments also confirmed that AKT1 and Caspase-3 were the explicit targets and the PI3K/AKT signaling pathway may be the key pathway of the 3 core components in the treatment of ovarian cancer. Therefore, quercetin, luteolin and wogonin are the possible active components of SBD acting on ovarian cancer. However, our study also had certain limitations. Firstly, the current network information technology needs to be further improved, and the accuracy and timeliness of database data require scientific verification. Secondly, the mechanism underlying the effect of SBD on ovarian cancer may be very complicated, the interactions between the multiple components and multiple targets of SBD are also unclear, further in-depth studies will be conducted in the future.

5. Conclusion

Based on the network pharmacology approach, we identified 229 potential active components and 137 related core targets. We screened the key active ingredients, including quercetin, luteolin, wogonin, and hub genes AKT1, VEGFA, JUN, TNF and Caspase-3 of SBD on ovarian cancer. Functional enrichment analysis through KEGG pathways and GO terms revealed that the PI3K/Akt pathway mediated the anti-ovarian cancer effects of SBD. In vitro experiments showed that quercetin, luteolin, and wogonin inhibited the proliferation and induced apoptosis of SKOV3 cells by regulating mRNA expression of 5 key genes, and inhibiting the PI3K/AKT signaling pathway activity.

These results verified the reliability and accuracy of the systematic network pharmacological screening results in this study, suggesting that SBD may potentially treat ovarian cancer, which is worthy of further study. This provides a theoretical basis for the future application of SBD in the treatment of ovarian cancer. Further studies are needed validate our findings.

Author contributions

Data curation: Jie Zhang, Chenhuan Ding.

Project administration: Cong Qi, He Li.

Writing – original draft: Jie Zhang.

Writing – review & editing: Libo Wang, Hongjin Wu, Weiwei Dai, Chenglong Wang.