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Abstract

Introduction: Polycystic ovary syndrome (PCOS) is a common endocrine disease that is characterized by chronic inflammation. Baishao (radix paeoniae alba, RPA), classified as a top-grade herbal medicine in TCM, has anti-inflammatory, immune adjustment, and anti-oxidative activities. RPA has been used to treat PCOS in clinical practice, but its potential mechanism of action is unclear. This study aimed at exploring the potential mechanism of RPA in PCOS treatment and to investigate the anti-inflammatory constituents of RPA. Methods: The TCMSP database was used to screen the potential active compounds of RPA. On the other hand, the possible target genes of PCOS were collected from the TTD, DrugBank, GeneCard, OMIM, and PharmGKB databases. The PPI network of RPA–PCOS target genes was established by STRING, and the visual network among RPA, ingredients, disease and potential target genes was constructed in Cytoscape. The bioactivity and potential mechanism of RPA in PCOS treatment were analyzed by GO and KEGG enrichment approaches. Furthermore, molecular docking was utilized to indicate the best compounds of RPA binding with TNF-α. Results: Fifty-four potential target genes were filtered and gathered from RPA and PCOS. Visual network indicated that 8 ingredients in RPA, β-sitosterol, ( + )-catechin, palbinone, sitosterol, mairin, kaempferol, paeoniflorigenone, and paeoniflorin, were closely correlated with 54 possible therapeutic target genes. The possible pathways relating the treatment of PCOS by RPA mainly contained the TNF, lipid atherosclerosis, C-type lectin receptor, and AGE-RAGE signal pathways. Paeoniflorigenone was the optimal anti-inflammatory compound of RPA to bind with TNF-α in molecular docking analysis. Conclusion: This study manifested that the most effective anti-inflammation compound of RPA was paeoniflorigenone and provided a potential therapeutic strategy for PCOS treatment.

Keywords

radix paeoniae alba polycystic ovary syndrome TNF-α drug targets monoterpene paeoniflorigenone

Introduction

Polycystic ovary syndrome (PCOS) is a common endocrine disease among reproductive women and exhibits a snowballing threat to their health.¹ According to the standardized diagnosis, the incidence of PCOS has increased to 18% in the United States.² PCOS is characterized by polycystic ovary, hyperandrogenism, ovulatory dysfunction and even develops into anovulation and infertility.³ Besides, most patients suffering from obesity, insulin resistance, and abnormal lipid metabolism have a greater risk than normal of developing T2DM, angiocardiopathy, atherosclerosis, and gynecological cancers.⁴ The etiology and pathogenesis of PCOS are complex and have not been elaborated clearly. Current medical treatment includes contraceptives, progestin, antiandrogen medication, and Metformin.⁵ However, limited efficacy and serious side effects, such as nausea, vomiting, and increased risk of cardiovascular disease, afflict the patients. Therefore, the disorder is plagued by lacking optimal treatment. Recent research proposed that PCOS was a chronic low-grade inflammation with abnormally increased inflammatory markers, including TNF-α, CRP, IL-6, IL-18, and MCP-1.⁶ This inflammation is strongly associated with obesity, hyperandrogenism, and insulin resistance.⁷ Therefore, eliminating the inflammation might be beneficial to normalizing the various biochemical indicators and relieve the symptoms of PCOS.

TNF-α, which plays an essential role in inflammation, is secreted by adipocytes and is regarded as a significant mediator of IR and androgen expression.⁸ Therefore, inhibiting the activity of TNF-α is a potential therapeutic strategy for PCOS. ETA, a TNF-α inhibitor, significantly reduced the weight and downgraded androgen in PCOS rats.⁹ Moreover, attention has been attracted to a TNF-α inhibitor for other inflammatory disease treatments.¹⁰ In spite of various TNF-α inhibitors existing on the market, their serious side effects impede their drug use.¹¹ Exploring and developing natural therapeutic products are necessary for clinical practice.

The dried root of Paeonia lactiflora Pallas (Baishao; radix paeoniae alba, RPA) is classified as the top-grade herbal medicine in Shenong's Herbal Classic, which is the first monograph on materia medica in the history of TCM.¹² RPA has a bitter and sour taste and belongs to “liver and spleen channels.” RPA has the effects of nourishing blood and liver, regulating menstruation, and relieving pain. The chemical constituents of PRA include terpenoids, polyphenolic compounds, volatile oil, monoterpene glycosides, galloylglucoses, and phenols, measured by UPLC and TOF-MS.¹³ The major pharmacological effects of RPA include anti-inflammatory, immune adjustment, and anti-oxidative activities.¹⁴ Therefore, RPA has been widely used to treat menstrual diseases and different inflammatory states, including endometritis and cholecystitis.¹⁵ A nationwide matched cohort study revealed that RPA was the common ingredient in products for decreasing the incidence of T2DM in PCOS patients.¹⁶ In an in vivo study, PRA decreased toxic alkaloid absorption into the brain and inhibited oxidative stress, which demonstrated a protective effect of PRA against neurotoxicity in Strychnos alkaloid brain damaged rats.¹⁷ Furthermore, research has found that paeoniflorin could decrease the expression of CYP17A1 and reverse testosterone elevation in primary theca cells of mice.¹⁸ RPA extracts also inhibited LPS-induced inflammatory reaction¹⁹ and decreased the inflammatory response in MPGN rats.²⁰ However, it is still unknown whether RPA alleviates PCOS by suppressing TNF-α expression and inhibiting inflammation.

In the present study, we primarily utilized network pharmacology to screen possible effective ingredients of RPA in PCOS treatment. Subsequently, targets genes and pathways of effective ingredients were clustered using GO and KEGG.²¹ Subsequently, to explore the optimal ingredient binding with TNF-α, molecular docking was applied to assess the binding capacity between the ingredients and TNF-α. Briefly, this study aimed to identify the anti-inflammatory compounds of RPA and provide the theory of treatment in PCOS.

Methods

Study Procedure

A scheme of the pharmacology and molecular docking workflow is shown in Figure 1. Compounds of RPA were extracted from the TCMSP database.²² PCOS correlated disease targets were acquired through GeneCards (https://www.genecards.org/),²³ OMIM (https://www.omim.org/),²⁴ Pharmgkb (https://www.pharmgkb.org/),²⁵ TTD (http://db.idrblab.net/ttd),²⁶ and Drug Bank (https://www.drugbank.ca).²⁷ After finding these targets, RPA–PCOS possible targets were filtered by the DAVID database.²⁸ Subsequently, Cytoscape²⁹ was used to construct an RPA-ingredients-PCOS-target network. Molecular docking was presented by CB-Dock ((http://cao.labshare.cn/cb-dock/).³⁰

Figure 1.

Scheme of pharmacology and molecular docking workflow.

Extracting Active Ingredients of RPA

The potential active ingredients of RPA were searched and extracted from TCMSP. The filter conditions were oral bioavailability (OB) >30% and drug-likeness (DL) >0.18. Thirteen ingredients were collected, and the 2D structures of 8 of the potentially active and PCOS related ingredients of RPA were downloaded from PubChem (http://pubchem.ncbi.nlm.nih.gov).³¹ 2D structures were transformed into 3D structures with structure optimization in Chemdraw 3D Ultra software.³²

Clustering of RPA and PCOS Correlated Target Genes

The target genes of RPA were collected from TCMSP, and the normalizing names of these genes were calibrated in Uniprot (https://www.uniprot.org/).³³ PCOS-associated target genes were gathered from 5 databases, GeneCards, OMIM, Pharmgkb, TTD, and DrugBank. They were consolidating RPA-correlated potential genes for PCOS treatment through the Venn diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/).³⁴

Constructing PPI Network of RPA–PCOS-Potential Target Genes

The RPA–PCOS potential target genes were entered into the STRING database (https://string-db.org/)³⁵ and the organism selected was “Homo sapiens, confidence = 0.900.” Subsequently, a PPI network was constructed to predict the neighborhood, co-expression, and fusion among potential target genes. Each node and edge represent a protein and a functional correlation, respectively.

Visualized Analysis of RPA and PCOS-Potential Target Genes Network

Cytoscape software (version 3.9.1) was used to visualize the correlation of RPA–PCOS-potential target genes. One node symbolically signified a compound or a target gene in the visual representation. Meanwhile, each line symbolizes an intermolecular correlation between one compound and one target gene.

Bioinformatics and Enrichment Analysis

Entrez ID of RPA–PCOS-potential target genes were converted by R packages “org.Hs.egdb version 4.1.2.” GO biological functions annotation and the KEGG Pathway analyses were visualized using the R packages “clusterProfiler,” “pathview,” and “DOSE.”³⁶

Accessing Combining Capacity Between Active Ingredients and Target Genes by Molecular Docking

Eight potential active ingredients were used to proceed to molecular docking with TNF-α receptor. Accessing combining capacity was conducted in CB-Dock, which is an available online docking tool (http://cao.labshare.cn/cb-dock/).³⁰ CB-Dock could automatically match the optimal docking pocket and list the Vina scores and related active configuration parameters. Vina scores could preliminarily assess the stability of the combining capability between the selected ingredient and the target protein. The lower the Vina score the more stable is the structure.

Results

Extracting Active Ingredients of RPA

Eighty-five active ingredients of RPA were found from the TCMSP database. After screening, 13 of these matched the required conditions (OB > 30% and DL > 0.18); these are shown in Table 1. The 13 ingredients were paeoniflorigenone, palbinone, mairin, paeoniflorin, kaempferol, ( + )-catechin, β-sitosterol, sitosterol, lactiflorin, albiflorin_qt, benzoyl paeoniflorin, paeoniflorin_qt, and 11α,12α-epoxy-3β-23-dihydroxy-30-norolean-20-en-28,12β-olide.

Table 1.

The Information of Screening Ingredients in RPA.

No.	Molecule ID	Molecule name	Molecule weight	OB^a (%)	DL^b
1	MOL001930	benzoyl paeoniflorin	584.62	31.27	0.75
2	MOL001928	albiflorin_qt	318.35	66.64	0.33
3	MOL001925	paeoniflorin_qt	318.35	68.18	0.4
4	MOL001924	paeoniflorin	480.51	53.87	0.79
5	MOL001921	lactiflorin	462.49	49.12	0.8
6	MOL001919	palbinone	358.52	43.56	0.53
7	MOL001918	paeoniflorgenone	318.35	87.59	0.37
8	MOL001910	11α,12α-epoxy-3β-23-dihydroxy-30-norolean-20-en-28,12β-olide	470.71	64.77	0.38
9	MOL000492	( + )-catechin	290.29	54.83	0.24
10	MOL000422	kaempferol	286.25	41.88	0.24
11	MOL000359	sitosterol	414.79	36.91	0.75
12	MOL000358	β-sitosterol	414.79	36.91	0.75
13	MOL000211	mairin	456.78	55.38	0.78

Oral bioavailability.

Drug-likeness.

Clustering of RPA and PCOS Correlated Target Genes and Construction of PPI Network

After eliminating repetitions, 3993 target genes were gathered for PCOS therapy from 5 databases. Meanwhile, 91 target genes of RPA were collected using the TCMSP database. The number of unique target genes of RPA was 68. Clustering RPA and PCOS related target genes led to the identification of 54 possible target genes, shown in Figure 2A and Table 2. STRING database was utilized to build a PPI network map of the 54 potential target genes represented in Figure 2B, and a histogram was used to show the clustering results of the top 30 target genes in Figure 2C. The histogram revealed that the target genes contained retinoid x receptor a (RXRA), serine/threonine-protein kinase (AKT1), tumor necrosis factor (TNF), and cytochrome P450 1a1 (CYP1A1).

Figure 2.

The target genes and PPI network of RPA–PCOS key target genes. (A) The potential target genes are shown in a Venn diagram. (B) The PPI network of 54 key target genes. (C) The top 30 genes in the PPI network were counted and listed in the histogram.

Table 2.

The 54 Potential Target Genes of RPA for PCOS Treatment.

No.	Target	Symbol	Entrez ID	No.	Target	Symbol	Entrez ID
1	NOS2	Nitric oxide synthase, inducible	4843	28	SLC6A4	Sodium-dependent serotonin transporter	6532
2	PPP3CA	Serine/threonine-protein phosphatase 2B catalytic subunit alpha isoform	5530	29	RXRA	Retinoic acid receptor RXR-alpha	6256
3	BCL2	Apoptosis regulator Bcl-2	596	30	PON1	Serum paraoxonase/arylesterase 1	5444
4	CYP1A1	Cytochrome P450 1A1	1543	31	LBP	Lipopoly-saccharide-binding protein	3929
5	GSTP1	Glutathione S-transferase P	2950	32	KCNH2	Potassium voltage-gated channel subfamily H member 2	3757
6	AHR	Aryl hydrocarbon receptor	196	33	CASP8	Caspase-8	841
7	TNF	Tumor necrosis factor	7124	34	PPARG	Peroxisome proliferator activated receptor gamma	5468
8	PTGS2	Prostaglandin G/H synthase 2	5743	35	SELE	E-selectin	6401
9	HAS2	Hyaluronan synthase 2	3037	36	SLPI	Antileuko-proteinase	6590
10	ESR1	Estrogen receptor	2099	37	ADRB2	Beta-2 adrenergic receptor	154
11	MMP1	Interstitial collagenase	4312	38	ACHE	Acetylcholinesterase	43
12	STAT1	Signal transducer and activator of transcription 1-alpha/beta	6772	39	IL6	Interleukin-6	3569
13	GABRA1	Gamma-aminobutyric acid receptor subunit alpha-1	2554	40	CASP3	Caspase-3	836
14	CD14	Monocyte differentiation antigen CD14	929	41	CYP1A2	Cytochrome P450 1A2	1544
15	NR3C2	Mineralocorticoid receptor	4306	42	ICAM1	Intercellular adhesion molecule 1	3383
16	HMOX1	Heme oxygenase 1	3162	43	SLC2A4	Solute carrier family 2, facilitated glucose transporter member 4	6517
17	GSTM1	Glutathione S-transferase Mu 1	2944	44	MAP2	Microtubule-associated protein 2	4133
18	IKBKB	Inhibitor of nuclear factor kappa-B kinase subunit beta	3551	45	OPRM1	Mu-type opioid receptor	4988
19	MAPK8	Mitogen-activated protein kinase 8	5599	46	VCAM1	Vascular cell adhesion protein 1	7412
20	PGR	Progesterone receptor	5241	47	INSR	Insulin receptor	3643
21	CASP9	Caspase-9	842	48	AKT1	RAC-alpha serine/threonine-protein kinase	207
22	CAT	Catalase	847	49	AKR1C3	Aldo-keto reductase family 1 member C3	8644
23	NCOA2	Nuclear receptor coactivator 2	10499	50	PTGS1	Prostaglandin G/H synthase 1	5742
24	SCN5A	Sodium channel protein type 5 subunit alpha	6331	51	AR	Androgen receptor	367
25	CHRM3	Muscarinic acetylcholine receptor M3	1131	52	BAX	Apoptosis regulator BAX	581
26	CYP1B1	Cytochrome P450 1B1	1545	53	PRKCA	Protein kinase C alpha type	5578
27	SLC6A2	Sodium-dependent noradrenaline transporter	6530	54	CYP3A4	Cytochrome P450 3A4	1576

Visualized Analysis of RPA and PCOS-Potential Target Genes Network

The RPA–PCOS potential target gene visual network is shown in Figure 3. The network includes 64 nodes and 132 lines, which indicate the complex association of the RPA–PCOS-potential target gene. In this network, the key target genes with higher degree values (degree >4) were progesterone receptor (PGR), nuclear receptor coactivator 2 (NCOA2), gamma-aminobutyric acid receptor subunit alpha-1 (GABRA1), prostaglandin G/H synthase 1 (PTGS1), and prostaglandin G/H synthase 2 (PTGS2), which are listed in Table 3.

Figure 3.

The visual network of ingredients-disease-target genes. The yellow node indicates PCOS. The red node represents RPA. The blue and green nodes indicate bioactive ingredients and target genes, respectively. Interaction of each node is shown with gray lines.

Table 3.

The top 10 Target Genes in the Visual Network (Degree >3).

No.	Target	Symbol	Degree
1	Progesterone receptor	PGR	6
2	Nuclear receptor coactivator 2	NCOA2	5
3	Gamma-aminobutyric acid receptor subunit alpha-1	GABRA1	4
4	Prostaglandin G/H synthase 1	PTGS1	4
5	Prostaglandin G/H synthase 2	PTGS2	4
6	Tumor necrosis factor	TNF	3
7	Mineralocorticoid receptor	NR3C2	3
8	Caspase-3	CASP3	3
9	Hyaluronan synthase 2	HAS2	3
10	Peroxisome proliferator activated receptor gamma	PPARG	3

GO and KEGG Pathway Enrichment Analysis

The Entrez IDs of 54 target genes are listed in Table 2. For GO analysis, the 71 biological functions of genes were gathered (P < .05). The histograms and scatterplots representing the top 20 gene biological functions were significantly gathered using GO analysis (Figure 4A and Supplementary Figure 1). The therapeutic effect of RPA on PCOS involved heme binding, amide binding, tetrapyrrole binding, oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen and peptide binding, which were the key biological functions. Furthermore, KEGG pathway enrichment uncovered a deeper mechanism in RPA for PCOS therapy. The top 20 signaling pathways are contained in the scatterplots and histograms shown in Figure 4B and Supplementary Figure 2. Lipid and atherosclerosis, diabetic complications related to the AGE-RAGE signal pathway and TNF signal pathway were the crucial signal pathways that were correlated with the protective mechanism of RPA in PCOS. The crucial target genes that correlated with the TNF signal pathway in RPA–PCOS are shown in Figure 5.

Figure 4.

Clustering analysis of GO (A) and KEGG (B).

Figure 5.

The network of the TNF signal pathway and the target genes of RPA for PCOS treatment are shown in red.

Binding Capacity Between the Key Ingredients With TNF-α Receptor Using Molecular Docking

Eight key ingredients were used to access the binding affinity with the TNF-α receptor, and the results are represented in Table 4. Paeoniflorigenone had lower Vina scores than the other 7 components. This result manifested the most stable binding capacity between paeoniflorigenone and TNF-α with the lowest Vina score. Thus, paeoniflorigenone might be the natural product acting as a TNF inhibitor. The 3D docking models of the 8 components with TNF-α are shown in Figure 6.

Figure 6.

The 3D structure of the molecular docking of (A) β-sitosterol, (B) ( + )-catechin, (C) palbinone, (D) sitosterol, (E) mairin, (F) kaempferol, (G) paeoniflorigenone, and (H) paeoniflorin with TNF-α.

Table 4.

Molecular Docking Parameters and Results of TNF-α and 8 Active Ingredients of RPA.

				Center^a			Size^b
Molecule ID	Molecule name	Vina scores	Cavity size	X	Y	Z	X	Y	Z
MOL001918	paeoniflorigenone	−8.9	1280	5	−4	8	19	19	19
MOL000211	mairin	−8.4	562	1	29	20	22	22	22
MOL001924	paeoniflorin	−8.2	1280	5	−4	8	21	21	21
MOL000422	kaempferol	−8	1280	5	−4	8	21	21	21
MOL000492	( + )-catechin	−7.6	1280	5	−4	8	20	20	20
MOL001919	palbinone	−7.5	562	1	29	20	21	21	21
MOL000358	β-sitosterol	−7.5	562	1	29	20	25	25	25
MOL000359	sitosterol	−6.9	562	1	29	20	26	26	26

Coordinates of docking pocket center.

The size in the X, Y, and Z directions of the docking pocket.

Discussion

Polycystic ovary syndrome is an intricate and unmanageable health issue, which afflicts more than 5% of premenopausal women worldwide.³⁷ The exact etiology and underlying mechanism have not been illustrated.³⁸ Currently, PCOS has been regarded as chronic low-grade inflammation. Despite this, there are no effective anti-inflammatory drugs for PCOS. Therefore, the efficient treatment of PCOS is still impeded. In China, treatment based on syndrome differentiation is the principle of TCM, and Chinese herbs and formulas have been widely used to relieve symptoms in the PCOS process.

Baishao is frequently used to relieve PCOS-related symptoms including decreasing the level of LH, T, and E2 and improving ovarian function.³⁹ In this study, we filtered 8 ingredients of RPA, which showed good anti-inflammatory effects. The 3 monoterpenes paeoniflorigenone, paeoniflorin, and albiflorin⁴⁰ showed noteworthy inhibiting effects on TNF-α-induced damage of endothelial cells.⁴¹ Paeoniflorin also recuperated the abnormal signal pathway related to inflammation and regulated immune cytotic function in several disorders such as rheumatoid arthritis⁴² and psoriasis. Albiflorin attenuated anxiety, neuropathic aches, and depressive disorder by reducing the activity of NLRP3 inflammasome in rats.⁴³ β-Sitosterol is a natural phytosterol with potential effects against various disorders. It alleviated adipocyte triggered inflammation and inhibited the level of pro-inflammatory factor such as IL-6 and TNF-α through decreasing the expression of JNK and the IKKβ/NF-κB signaling pathway.⁴⁴ Furthermore, in the HFD-induced intestinal inflammation model, β-sitosterol restrained the transposition of NF-κB and blocked the combination of LPS with TLR4.⁴⁵ Kaempferol significantly decreased the level of I-κB and NF-κB in the LPS-induced IBD vitro model.⁴⁶ For other compounds, (+)-catechin could regulate the expression of TLR2/4, MAPK, and NF-κB in mice infected with Porphyromonas gingivalis.⁴⁷ In brief, the widespread use of Baishao and the anti-inflammatory effects of its ingredients indicated the possibility of these components for PCOS treatment.

RPA for PCOS treatment is a multicomponent and multitargeted therapeutic strategy. There were 54 proteins related to RPA and PCOS in this study, and, simultaneously, different degrees of connection and interaction existed between these proteins. According to the visual network, the potential and vital targets of RPA for PCOS contained PGR, NCOA2, GABRA1, PTGS1, PTGS2, TNF, NR3C2, CASP3, HAS2, and PPARG. These potential targets are correlated with regulating the level of gonadal hormone, inflammation, and oxidative stress. In GO and KEGG enrichment analysis, 71 biological functions and 132 pathways were potentially associated with the mechanism of RPA for PCOS treatment. Inflammation and TNF-α were important biofunctions and pathways in GO and KEGG clustering analysis. Meanwhile, research revealed that anti-inflammation would be a crucial mechanism in PCOS.⁷

The molecular docking parameters and results indicated that paeoniflorigenone was the best ingredient of RPA for binding to TNF-α protein. The previous studies indicated that the level of TNF-α was associated with IR and inflammation in PCOS patients.^48,49 In another study, paeoniflorigenone inhibited apoptosis and increased autophagy in human HNSCC cells by regulating the PI3K/AKT signaling pathway.⁵⁰ Moreover, paeoniflorigenone was the top compound with the best oral bioavailability in Xijiao Dihuang Tang. The formula downregulated the levels of pro-inflammatory factors IL-1β and TNF-α in Kawasaki disease mice.⁴¹ This research revealed that paeoniflorigenone possessed various bioactive functions including anti-inflammation and inhibiting the expression of TNF-α. The present study expanded the scope of basic research on paeoniflorigenone.

Conclusion

RPA is a preferred Chinese herb in PCOS treatment, which has been verified in clinical practice and previous research. The GO and KEGG analysis of RPA bioactive ingredients and PCOS target genes were explored through network pharmacology. Paeoniflorigenone was the optimal ingredient for binding to TNF-α using the molecular docking approach. In a word, paeoniflorigenone could be the optimal compound of RPA acting as a natural TNF-α inhibitor for PCOS treatment.

Supplemental Material

sj-docx-1-npx-10.1177_1934578X221129136 - Supplemental material for Identification of the Anti-Inflammatory Compound, Paeoniflorigenone, in Radix Paeoniae Alba for the Treatment of Polycystic Ovary Syndrome Through Network Pharmacology and Molecular Docking

Supplemental material, sj-docx-1-npx-10.1177_1934578X221129136 for Identification of the Anti-Inflammatory Compound, Paeoniflorigenone, in Radix Paeoniae Alba for the Treatment of Polycystic Ovary Syndrome Through Network Pharmacology and Molecular Docking by Juntong Li, Gaoxiang Wang, Shufang Chu, Deliang Liu, Hengxia Zhao and Huilin Li in Natural Product Communications

Footnotes

Authors’ Note

Juntong Li and Gaoxiang Wang contributed equally to this work. LJT and WGX collected the data and wrote the original draft. CSF, LDL, ZHX, and LHL revised the manuscript. The relevant data and code used in this study are accessible upon reasonable request from the corresponding author.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (NO.82104759), and the Guangdong Natural Science Foundation (2020A1515010775).

ORCID iD

Juntong Li

Supplemental Material

Supplemental material for this article is available online.

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Supplementary Material

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Identification of the Anti-Inflammatory Compound,Paeoniflorigenone,in Radix Paeoniae Alba for the Treatment of Polycystic Ovary Syndrome Through Network Pharmacology and Molecular Docking

Abstract

Keywords

Introduction

Methods

Study Procedure

Extracting Active Ingredients of RPA

Clustering of RPA and PCOS Correlated Target Genes

Constructing PPI Network of RPA–PCOS-Potential Target Genes

Visualized Analysis of RPA and PCOS-Potential Target Genes Network

Bioinformatics and Enrichment Analysis

Accessing Combining Capacity Between Active Ingredients and Target Genes by Molecular Docking

Results

Extracting Active Ingredients of RPA

Clustering of RPA and PCOS Correlated Target Genes and Construction of PPI Network

Visualized Analysis of RPA and PCOS-Potential Target Genes Network

GO and KEGG Pathway Enrichment Analysis

Binding Capacity Between the Key Ingredients With TNF-α Receptor Using Molecular Docking

Discussion

Conclusion

Supplemental Material

sj-docx-1-npx-10.1177_1934578X221129136 - Supplemental material for Identification of the Anti-Inflammatory Compound, Paeoniflorigenone, in Radix Paeoniae Alba for the Treatment of Polycystic Ovary Syndrome Through Network Pharmacology and Molecular Docking

Footnotes

Authors’ Note

Declaration of Conflicting Interests

Funding

ORCID iD

Supplemental Material

References

Supplementary Material