Evidence-Based Complementary and Alternative Medicine

Evidence-Based Complementary and Alternative Medicine / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 7130105 | https://doi.org/10.1155/2020/7130105

Fanyu Fu, Zeqing Huang, Hengli Ye, Biao Tan, Rongtian Wang, Weiheng Chen, "Mechanisms and Molecular Targets of the Tao-Hong-Si-Wu-Tang Formula for Treatment of Osteonecrosis of Femoral Head: A Network Pharmacology Study", Evidence-Based Complementary and Alternative Medicine, vol. 2020, Article ID 7130105, 13 pages, 2020. https://doi.org/10.1155/2020/7130105

Mechanisms and Molecular Targets of the Tao-Hong-Si-Wu-Tang Formula for Treatment of Osteonecrosis of Femoral Head: A Network Pharmacology Study

Academic Editor: Adolfo Andrade-Cetto
Received13 Mar 2020
Revised19 Aug 2020
Accepted27 Aug 2020
Published09 Sep 2020

Abstract

The Tao-Hong-Si-Wu-Tang (THSWT) formula, a classic prescription of traditional Chinese medicine, has long been used for the treatment of osteonecrosis of femoral head (ONFH). However, its mechanisms of action and molecular targets are not comprehensively clear. In the present study, the Traditional Chinese Medicine System Pharmacology (TCMSP) database was employed to retrieve the active compounds of each herb included in the THSWT formula. After identifying the drug targets of active compounds and disease targets of ONFH, intersection analysis was conducted to screen out the shared targets. The protein-protein network of the shared targets was built for further topological analysis. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were then carried out. A gene pathway network was constructed to screen the core target genes. We identified 61 active compounds, 155 drug targets, and 5443 disease targets. However, intersection analysis only screened out 37 shared targets. Kaempferol, luteolin, and baicalein regulated the greatest number of targets associated with ONFH. The THSWT formula may regulate osteocyte function through specific biological processes, including responses to toxic substances and oxidative stress. The regulated pathways included the relaxin, focal adhesion, nuclear factor-κB, toll-like receptor, and AGE/RAGE signaling pathways. RELA, VEGFA, and STAT1 were the important target genes in the gene network associated with the THSWT formula for the treatment of ONFH. Therefore, the present study suggested that the THSWT formula has an action mechanism involving multiple compounds and network targets for the treatment of ONFH.

1. Introduction

Osteonecrosis of femoral head (ONFH) represents a disruption of the blood supply to the femoral head due to trauma, corticosteroids, alcohol, and other ill-defined etiologies [1]. ONFH mainly affects individuals of working age [2]. It is estimated that there are more than 8 million patients with nontraumatic ONFH in China [3]. According to natural history studies, approximately half of all affected hips at pre-collapse stages (Association Research Circulation Osseous stage I or II) [4] would progress to irreversible collapse of the femoral head if left untreated [5]. Femoral head collapse can then progress to severe premature osteoarthritis of the hip, which is a common cause of lifelong disability and total hip arthroplasty in this active population [6].

To date, there are no optimal treatments for ONFH [7]. Total hip arthroplasty is not the first-choice treatment option since revision procedures and implant longevity remain tricky problems [8]. Other treatment modalities, commonly known as hip-preserving treatments, have demonstrated both favorable and poor outcomes [9, 10]. According to a meta-analysis published in 2019, no marketed drugs are recommended for the treatment of ONFH [11].

During the past decade, Chinese experts have developed four versions of clinical guidelines, and traditional Chinese medicine (TCM) is consistently recommended as one of the main nonoperative treatments [1215]. TCM holds a relatively unique point of view when treating ONFH, and blood stasis (Yu-Xue) is considered the pathological basis of ONFH [16]. According to the TCM theory, once the meridian branches (Jing-Luo) passing through the femoral head are blocked, the femoral head loses nutrition from qi and blood. Based on the blood stasis theory, the primary management strategy of TCM is to activate blood circulation (Huo-Xue-Fa) [15]. The Tao-Hong-Si-Wu-Tang (THSWT) formula is composed of Tao-Ren (Persicae Semen), Hong-Hua (Carthami Flos), Dang-Gui (Angelicae Sinensis Radix), Chuang-Xiong (Chuanxiong Rhizoma), Shu-Di-Huang (Rehmanniae Radix Praeparata), and Bai-Shao (Paeoniae Radix Alba), of which the main TCM function is to activate blood circulation. The THSWT formula is frequently administered in patients with ONFH in China. Data from animal testing suggest that the THSWT formula may help ameliorate the progression of steroid-induced avascular necrosis [17]. However, the active compounds and potential targets, as well as action pathways, remain poorly understood.

A general solution related to network pharmacology has been proposed recently, which has become a hot topic to investigate multiple molecular mechanisms of multiple-target compounds affecting biological networks for herbal medicines. Therefore, we employed network pharmacology to probe the pharmacological mechanisms of the THSWT formula against ONFH in this study.

2. Materials and Methods

2.1. Screening of Active Compounds

The THSWT formula consists of six Chinese herbs, including Tao-Ren (Persicae Semen), Hong-Hua (Carthami Flos), Dang-Gui (Angelicae Sinensis Radix), Chuang-Xiong (Chuanxiong Rhizoma), Shu-Di-Huang (Rehmanniae Radix Praeparata), and Bai-Shao (Paeoniae Radix Alba). The chemical compounds of these six herbs were identified using the Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP, http://tcmspw.com/tcmsp.php) [18]. TCMSP provides important data on the absorption-, distribution-, metabolism-, and excretion-related properties of Chinese herbs, including the oral bioavailability (OB), half-life, and drug-likeness (DL). In the present study, chemical compounds with OBs ≥30% and DLs ≥0.18 were identified as active compounds. Eventually, 61 active compounds were screened out in total after removing duplications.

2.2. Identification of Drug Targets

The DrugBank (http://www.drugbank.ca) [19] was employed to investigate potential targets of the 61 selected compounds. The DrugBank is a database containing approved drugs as well as experimental drugs. Finally, 587 drug targets were identified, including 102 in Persicae Semen, 257 in Carthami Flos, 55 in Angelicae Sinensis Radix, 30 in Rehmanniae Radix Praeparata, 104 in Paeoniae Radix Alba, and 39 in Chuanxiong Rhizoma. A total of 155 drug targets were collected after removing duplications. Protein sequences of these drug targets were normalized to official gene symbols using the UniProt database (https://www.uniprot.org/) [20].

2.3. Identification of Disease Targets

The differentially expressed genes associated with ONFH were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/, Series: GSE74089, Samples: GSM1909502, GSM1909503, GSM1909504, GSM1909505, GSM1909506, GSM1909507, GSM1909508, GSM1909509) [21]. These original data were converted into a gene matrix using the Perl tool [22]. The collated data were analyzed using the Limma plugin of R software. Genes with a P-value <0.05 and |log2 (fold change)| >1 were identified as disease targets of ONFH.

2.4. Protein-Protein Interaction Network Construction

The Venny 2.1 online tool (http://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to draw a Venn diagram of drug targets and disease targets to obtain shared targets of the THSWT formula and ONFH. The shared target genes were then inputted into the String database (http://string-db.org), with species limited to Homo sapiens and a confidence score >0.4, to construct the protein-protein interaction (PPI) network. The PPI network of drug targets and disease targets was visualized using Cytoscape software. Maximum Clique Centrality (MCC) is a network topology algorithm of the Cytohubba plugin, which helps identify core targets in the network. In the present study, the top 10 targets with the highest MCC scores were considered the core targets of the THSWT formula against ONFH.

2.5. Network Construction Method

An active compound-shared target network was constructed and visualized using Cytoscape 3.7.2 software. The core compounds and core targets in this network were automatically identified. Each node in the network represented an active compound or shared target. The edge between two nodes implied that a particular compound might act on the target connected with it. The topological parameters of each node, including the degree, betweenness, and closeness, were calculated and used as screening criteria for the crucial nodes. Overall, nodes with greater parameter values were recognized as crucial nodes of the THSWT formula against ONFH. In the present study, a key compound was required to fulfill the criterion that these three parameters exceeded the median of the selected compounds.

2.6. Bioinformatics Analysis

Gene ontology (GO) biological process (BP) enrichment analysis and Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analysis were conducted using the David 6.8 database (https://david.ncifcrf.gov/). During these procedures, P.adjust <0.05 suggested statistical significance in the enrichment degree. The top 20 GO and top 20 KEGG results with the lowest P.adjust values were displayed in the form of bubble charts using R-studio software. The genes with significantly regulated pathways were selected for gene pathway network analysis to screen out the key target genes of the THSWT formula in the treatment of ONFH.

3. Results

3.1. Active Compounds and Shared Targets

Sixty-one chemical compounds of the THSWT formula (Table 1) were identified as the active compounds. The distribution of differentially expressed genes was displayed using volcanic maps (Figure 1). Data of upregulated genes were shown as red dots, and downregulated genes were shown as green dots. A total of 5443 differentially expressed genes in ONFH were collected from the GEO database, including 3291 upregulated genes and 2152 downregulated genes. Intersection analysis of 155 drug target genes and 5443 disease target genes identified 37 shared targets (Figure 2). These 37 targets were considered potential targets of the THSWT formula for the treatment of ONFH.


Chinese nameLatin nameSpeciesFamilyTCMSP IDChemical compoundsOral bioavailability (%)Drug-likeness

Bai-ShaoPaeoniae Radix AlbaPaeonia lactiflora PallRanunculaceaeMOL000211Mairin55.380.78
MOL000358β-sitosterol36.910.75
MOL001359Sitosterol36.910.75
MOL000422Kaempferol41.880.24
MOL000492(+)-catechin54.830.24
MOL00191011α, 12α-epoxy-3β-23-dihydroxy-30-norole-an-20-en-28, 12β-olide64.770.38
MOL001918Paeoniflorgenone87.590.37
MOL001919(3S, 5R, 8R, 9R, 10S, 14S) -3, 17-dihydroxy-4, 4, 8, 10, 14-pentamethyl-2, 3, 5, 6, 7, 9-hexahydro-1H-cyclopenta[a]phenanthrene-15, 16-dione43.560.53
MOL001921Lactiflorin49.120.80
MOL001924Paeoniflorin53.870.79
MOL001925paeoniflorin_qt68.180.40
MOL001928albiflorin_qt66.640.33
MOL001930Benzoyl paeoniflorin31.270.75
Dang-GuiAngelicae Sinensis RadixAngelica sinensis (Oliv.) DielsApiaceaeMOL000358β-sitosterol36.910.75
MOL000449Stigmasterol43.830.76
Tao-RenPersicae SemenPrunus persica (L.) BatschRosaceaeMOL000358β-sitosterol36.910.75
MOL001323Sitosterol alpha143.280.78
MOL0013282,3-didehydro GA7063.290.50
MOL0013292,3-didehydro GA7788.080.53
MOL001339GA11976.360.49
MOL001340GA12084.850.45
MOL001342GA121-isolactone72.700.54
MOL001343GA12264.790.50
MOL001344GA122-isolactone88.110.54
MOL001348Gibberellin 1794.640.49
MOL0013494a-formyl-7alpha-hydroxy-1-methyl-8-methylidene-4aalpha,4bbeta-gibbane-1alpha,10beta-dicarboxylic acid88.600.46
MOL001350GA3061.720.54
MOL001351Gibberellin A44101.610.54
MOL001352GA5464.210.53
MOL001353GA6093.170.53
MOL001355GA6365.540.54
MOL001358Gibberellin 773.800.50
MOL001360GA7787.890.53
MOL001361GA8768.850.57
MOL0013683-O-p-coumaroylquinic acid37.630.29
MOL001371Populoside_qt108.890.20
MOL000296Hederagenin36.910.75
MOL000493Campesterol37.580.71
Shu-Di-HuangRehmanniae Radix PraeparataRehmannia glutinosa LiboschScrophulariaceaeMOL001359Sitosterol36.910.75
MOL000449Stigmasterol43.830.76
Chuang-XiongChuanxiong RhizomaLigusticum chuanxiong HortApiaceaeMOL001359Sitosterol36.910.75
MOL000433Folic acid68.960.71
MOL001494Mandenol42.000.19
MOL002135Myricanone40.600.51
MOL002140Perlolyrine65.950.27
MOL002151Senkyunone47.660.24
MOL002157Wallichilide42.310.71
Hong-HuaCarthami FlosCarthamus tinctorius L.CompositaeMOL000358β-sitosterol36.910.75
MOL000449Stigmasterol43.830.76
MOL000422Kaempferol41.880.24
MOL001771Poriferast-5-en-3beta-ol36.910.75
MOL0026944-[(E)-4-(3,5-dimethoxy-4-oxo-1-cyclohexa-2,5-dienylidene) but-2-enylidene]-2,6-dimethoxycyclohexa-2,5-dien-1-one48.470.36
MOL002680Flavoxanthin60.410.56
MOL002695Lignan43.320.65
MOL002698Lupeol-palmitate33.980.32
MOL002706Phytoene39.560.50
MOL002707Phytofluene43.180.50
MOL002710Pyrethrin II48.360.35
MOL0027126-Hydroxykaempferol62.130.27
MOL002714Baicalein33.520.21
MOL002717qt_carthamone51.030.20
MOL0027196-Hydroxynaringenin33.230.24
MOL002721Quercetagetin45.010.31
MOL0027577,8-dimethyl-1H-pyrimido[5,6-g]quinoxaline-2,4-dione45.750.19
MOL002773Beta-carotene37.180.58
MOL002776Baicalin40.120.75
MOL000006Luteolin36.160.25
MOL000953CLR37.870.68
MOL000098Quercetin46.430.28

3.2. PPI Network Analysis

The PPI network (Figure 3) contained 37 nodes, which corresponded to 37 shared targets, and 120 edges that represented the target-target interactions. The top 10 target genes with the highest MCC scores were VEGFA, PTGS2, CCND1, JUN, RELA, STAT1, AHR, NR3C1, MCL1, and MMP2, which were considered the core targets (Figure 4).

3.3. Compound-Shared Target Network Analysis

The compound-shared target network (Figure 5) contained 67 nodes, which corresponded to 30 candidate compounds, 37 shared targets, and 94 edges representing the compound-target interactions (Table 2). Topological calculations revealed nine compounds fulfilling the criteria with all parameter values (degree, betweenness, and closeness) exceeding the median of the 30 selected compounds (Table 3). Overall, kaempferol, luteolin, and baicalein were found to act on the top three greatest numbers of targets (15, 14, and 8 targets, respectively). In addition, the OBs of kaempferol, luteolin, and baicalein were 41.88%, 36.16%, and 33.52%, respectively. Therefore, they were considered the key compounds in the THSWT formula for the treatment of ONFH.


TCMSP IDChemical compoundHerb sourceTarget

MOL000006LuteolinCarthami FlosPTGS2, PRSS1, RELA, VEGFA, CCND1, MMP2, RB1, JUN, IL6, MMP1, PCNA, MCL1, PTGES, MET
MOL000296HederageninPersicae SemenADH1C, SCN5A, PTGS2
MOL000358Gibberellin 7Paeoniae Radix Alba, Angelicae Sinensis Radix, Carthami Flos, Persicae SemenPTGS2, SCN5A, CHRM4, ADRA1A, OPRM1, JUN, PRKCA
MOL000422KaempferolPaeoniae Radix Alba, Carthami FlosPTGS2, PRSS1, ACHE, F7, CALM1, RELA, JUN, MMP1, STAT1, VCAM1, CYP1B1, ALOX5, HAS2, AHR, NR1I3
MOL000449StigmasterolRehmanniae Radix Praeparata, Angelicae Sinensis Radix, Carthami FlosADH1C, PTGS2, SCN5A, ADRA1A
MOL000492(+)-catechinPaeoniae Radix AlbaPTGS2, CALM1, HAS2
MOL000493CampesterolPersicae SemenPTGS2
MOL001323Sitosterol alpha1Persicae SemenPTGS2, ADH1C
MOL0013282,3-didehydro GA70Persicae SemenPTGS2, PRSS1
MOL0013292,3-didehydro GA77Persicae SemenPTGS2
MOL001340GA120Persicae SemenPTGS2
MOL001352GA54Persicae SemenPTGS2, CALM1
MOL001355GA63Persicae SemenPTGS2
MOL001358Gibberellin 7Persicae SemenPTGS2
MOL001361GA87Persicae SemenPTGS2
MOL0013683-O-p-coumaroylquinic acidPersicae SemenPTGS2, CALM1
MOL001494MandenolChuanxiong RhizomaPTGS2
MOL001924PaeoniflorinPaeoniae Radix AlbaIL6, CD14, LBP
MOL002135MyricanoneChuanxiong RhizomaSCN5A, PTGS2, F7
MOL002140PerlolyrineChuanxiong RhizomaPTGS2
MOL002157WallichilideChuanxiong RhizomaPTGS2, NR3C1
MOL0026944-[(E)-4-(3,5-dimethoxy-4-oxo-1-cyclohexa-2,5-dienylidene) but-2-enylidene]-2,6-dimethoxycyclohexa-2,5-dien-1-oneCarthami FlosPTGS2
MOL002695LignanCarthami FlosPTGS2, CALM1
MOL002710Pyrethrin IICarthami FlosPTGS2
MOL0027126-hydroxykaempferolCarthami FlosPTGS2, PRSS1
MOL002714BaicaleinCarthami FlosPTGS2, PRSS1, CALM1, RELA, VEGFA, AHR, EGLN1, APOD
MOL002717qt_carthamoneCarthami FlosPTGS2
MOL002721QuercetagetinCarthami FlosPTGS2, PRSS1
MOL0027577,8-dimethyl-1H-pyrimido[5,6-g]quinoxaline-2,4-dioneCarthami FlosPTGS2
MOL002773Beta-caroteneCarthami FlosVEGFA, MMP2, JUN, PTGS2, MMP1, CAV1, GJA1


TCMSP IDChemical compoundDegreeBetweennessCloseness

MOL000422Kaempferol151080.9036.32
MOL000006Luteolin141370.8136.17
MOL002714Baicalein8394.4631.65
MOL000358Gibberellin 77503.6630.98
MOL002773Beta-carotene7365.7630.98
MOL000449Stigmasterol4120.3628.98
MOL000296Hederagenin366.2928.32
MOL002135Myricanone365.7228.32
MOL000492(+)-catechin349.3528.32
MOL001924Paeoniflorin3258.0019.03
MOL002157Wallichilide2130.0027.65
MOL001323Sitosterol alpha1238.7727.65
MOL001352GA54211.6727.65
MOL0013683-O-p-coumaroylquinic acid211.6727.65
MOL002695Lignan211.6727.65
MOL0013282,3-didehydro GA7029.6327.65
MOL0027126-hydroxykaempferol29.6327.65
MOL002721Quercetagetin29.6327.65
MOL0013292,3-didehydro GA7710.0026.98
MOL001340GA12010.0026.98
MOL001355GA6310.0026.98
MOL001358Gibberellin 710.0026.98
MOL001361GA8710.0026.98
MOL000493Campesterol10.0026.98
MOL001494Mandenol10.0026.98
MOL002140Perlolyrine10.0026.98
MOL0026944-[(E)-4-(3,5-dimethoxy-4-oxo-1-cyclohexa-2,5-dienylidene) but-2-enylidene]-2,6-dimethoxycyclohexa-2,5-dien-1-one10.0026.98
MOL002710Pyrethrin II10.0026.98
MOL002717qt_carthamone10.0026.98
MOL0027577,8-dimethyl-1H-pyrimido[5,6-g]quinoxaline-2,4-dione10.0026.98

3.4. GO and KEGG Pathway Enrichment Analyses

BP, cellular component (CC), and molecular function (MF) analyses of the 37 target genes revealed 603 GO terms that were significantly enriched, including 540 in BP, 23 in CC, and 40 in MF analyses. The GO terms with the top 20 lowest P.adjust values are shown in Figure 6.

KEGG pathway analysis revealed 78 pathways that were significantly enriched. The top 20 terms are shown in Figure 7. The clinically significant pathways in the top 20 included the relaxin, focal adhesion, nuclear factor (NF)-κB, Toll-like receptor (TLR), and AGE/RAGE signaling pathways.

Finally, the gene pathway network was constructed based on the significantly enriched pathways and genes that regulated these pathways, as presented in Figure 8. Topological analysis of 20 pathways and 21 genes was carried out. The squares represented target genes, and the V-shapes represented pathways in the network. The network diagram suggested that RELA had the maximum degree (number of connected nodes) and thus was considered the core target. Several other targets also had more significant degrees, such as JUN, VEGFA, and CCND1.

4. Discussion

TCM holds a similar view that ischemia of the femoral head is a key pathological change in ONFH. Chinese herbal medications with the TCM function of activating blood (Huo-Xue-Fa) have been consistently recommended by Chinese guidelines as an important nonoperative treatment for ONFH [1215]. The THSWT formula, as a basic prescription to implement the therapeutic principle of activating blood [23], has demonstrated promising effects in ameliorating the progression of ONFH [24]. However, the biological activity of the THSWT formula remains poorly understood, particularly regarding whether it can increase the blood supply to the femoral head and whether it possesses any bone protective activity. Data from the present study suggest that the THSWT formula contains multiple active compounds that act on a network of different targets by regulating a number of signaling pathways, which contribute to the implementation of the THSWT formula in clinical practice.

An updated meta-analysis concluded that marketed drugs fail to prevent the progression of ONFH [11], but an increasing number of clinical studies on TCM have demonstrated promising outcomes [25, 26]. Essentially, TCM prescribes several natural compounds, most of which are still not approved as marketed productions. However, this can be an important way to discover potential drugs for ONFH. In the present study, kaempferol, luteolin, and baicalein were among the important active compounds of the THSWT formula, since these compounds can act on 15, 14, and 8 different disease targets, respectively. Kaempferol is a common flavonol present in Chinese herbs with therapeutic properties, including antioxidant and anti-inflammatory activities [27]. Recent studies have suggested that kaempferol also has bone protective activity, since animal testing has found that kaempferol antagonizes the apoptotic effect of dexamethasone on osteoblasts [28]. Both isolated luteolin and extracts from luteolin-rich plants exhibit anti-inflammatory activity [29]. Luteolin also helps inhibit the bone resorption induced by mature osteoclasts [30]. A number of studies have demonstrated that baicalein has potent neuroprotective properties [31]. Additionally, baicalein inhibits the bone resorptive activity of mature osteoclasts by inducing apoptosis [32]. We can easily conclude that the natural compounds of the THSWT formula, particularly the three aforementioned compounds, confer bone protective activity and have high OB scores; they are likely to be the core compounds for the treatment of ONFH.

GO enrichment analysis suggested that the THSWT formula regulates a variety of BPs and affects various CCs and MFs. Cellular responses to toxic substances and oxidative stress are important BPs involved in the development of ONFH. Corticosteroids and alcohol are key toxic substances that cause ONFH. Previous studies have confirmed that the rs1045642 single-nucleotide polymorphism of ABCB1, an important determinant in the elimination of toxic substances, is closely associated with the occurrence of steroid-induced ONFH [33]. Moreover, oxidative stress plays a role in the activation of coagulation, which is the underlying BP that leads to ischemia of the femoral head [34]. Our data showed that membrane raft and membrane microdomains are among the most significant CCs affected by the THSWT formula. Additionally, the significantly mediated MFs include protein heterodimerization activity and proximal promoter sequence-specific DNA binding.

KEGG enrichment analysis suggested that the THSWT formula may regulate various signaling pathways. The relaxin, focal adhesion, and NF-κB signaling pathways are enriched pathways with important clinical significance. The relaxin signaling pathway is a potent stimulator of osteoclastogenesis from hematopoietic precursors, which regulate the activity of mature osteoclasts [35]. Focal adhesion kinase (FAK) is a nonreceptor protein tyrosine kinase and scaffolding protein that mediates numerous cellular functions, including adhesion, migration, and invasion. FAK inhibitors reduce synovial fibroblast invasion and migration [36]; thus, inhibition of FAK may help ameliorate the bone marrow edema and synovitis observed in the development of ONFH. TLR antagonists can be used for the treatment of inflammatory and autoimmune diseases, which also inhibit the activation of NF-κB. NF-κB, one of the most important transcriptional signaling molecules, participates in downstream inflammatory pathways and the TLR signaling pathway. The essential role of NF-κB in osteoclastogenesis has been demonstrated genetically. NF-κB can transduce signals by recruiting adaptor molecules. In addition, NF-κB can induce the proliferation of monocytes/macrophages, which finally form osteoclasts [37]. Our data also suggested that the biological activity of the THSWT formula is associated with a number of pathways involved in other diseases, including infections, cancers, and diabetes-related complications. Interestingly, the AGE/RAGE signaling pathway is the most enriched pathway based on our data. AGE/RAGE signaling is a well-studied cascade in many different disease states; inhibition of the AGE/RAGE system may be a promising target for therapeutic intervention for vascular complications such as acquired blindness, end-stage renal failure, a variety of neuropathies, and accelerated atherosclerosis [38]. The AGE/RAGE signaling pathway also plays an important physiological role in the regulation of skeletal development, homeostasis, and repair/regeneration [39].

Gene pathway network analysis revealed that RELA, VEGFA, and STAT1 were among the core targets of the THSWT formula in the treatment of ONFH. RELA is a member of the NF-κB/Rel family. The transcription factor NF-κB is a critical regulator of immune and inflammatory responses. Mice lacking RelA/p65 in the hematopoietic compartment have been shown to have a deficient osteoclastogenic response to RANKL and are protected from arthritis-induced osteolysis. It has been shown that inhibition of NF-κB is an effective approach to inhibit osteoclast formation and bone resorptive activity [40]. The vascular endothelial growth factor A (VEGFA) gene is located on chromosome 6p31.3 [41]. It encodes a member of vascular endothelial growth factor. Several previous studies have linked multiple genetic polymorphisms within the promoter region of VEGFA to the disease status of nontraumatic ONFH. The STAT1 signaling pathway is strongly activated in the pathogenesis and progression of osteoporosis [42]. Acceleration in fracture callus remodeling and membranous ossification has been observed in STAT1-deficient mice [43].

The mechanisms of action and molecular targets of the THSWT formula for ONFH were explored using a network pharmacology approach in this study. Kaempferol, luteolin, and baicalein regulated the most number of targets associated with ONFH. The THSWT formula may regulate osteocyte function through specific BPs, including responses to toxic substances and oxidative stress. The regulated pathways include the relaxin, focal adhesion, NF-κB, TLR, and AGE/RAGE signaling pathways. RELA, VEGFA, and STAT1 are the important target genes in the gene network of the THSWT formula for the treatment of ONFH.

Data Availability

Raw data were generated at Wangjing Hospital. Derived data supporting the findings of this study are available from the corresponding authors on request.

Disclosure

Fanyu Fu and Zeqing Huang and should be considered co-first authors.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Fanyu Fu and Zeqing Huang equally contributed to this work.

Acknowledgments

This study was supported by grants from National Natural Science Foundation of China (No. 81973888) and the Research and Development Project of the G20 Program, Beijing Municipal Commission of Science and Technology (No. Z151100003815028).

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