# Exploring the Pharmacological Mechanism of Duhuo Jisheng Decoction in Treating Osteoporosis Based on Network Pharmacology

Authors: Zhencheng Xiong; Can Zheng; Yanan Chang; Kuankuan Liu; Li Shu; Chi Zhang
Journal: Evidence-Based Complementary and Alternative Medicine
 (2021)

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## Abstract

Objective. The purpose of this work is to study the mechanism of action of Duhuo Jisheng Decoction (DHJSD) in the treatment of osteoporosis based on the methods of bioinformatics and network pharmacology. Methods. In this study, the active compounds of each medicinal ingredient of DHJSD and their corresponding targets were obtained from TCMSP database. Osteoporosis was treated as search query in GeneCards, MalaCards, DisGeNET, Therapeutic Target Database (TTD), Comparative Toxicogenomics Database (CTD), and OMIM databases to obtain disease-related genes. The overlapping targets of DHJSD and osteoporosis were identified, and then GO and KEGG enrichment analysis were performed. Cytoscape was employed to construct DHJSD-compounds-target genes-osteoporosis network and protein-protein interaction (PPI) network. CytoHubba was utilized to select the hub genes. The activities of binding of hub genes and key components were confirmed by molecular docking. Results. 174 active compounds and their 205 related potential targets were identified in DHJSD for the treatment of osteoporosis, including 10 hub genes (AKT1, ALB, IL6, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, and EGF). Pathway enrichment analysis of target proteins indicated that osteoclast differentiation, AGE-RAGE signaling pathway in diabetic complications, Wnt signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, JAK-STAT signaling pathway, calcium signaling pathway, and TNF signaling pathway were the specifically major pathways regulated by DHJSD against osteoporosis. Further verification based on molecular docking results showed that the small molecule compounds (Quercetin, Kaempferol, Beta-sitosterol, Beta-carotene, and Formononetin) contained in DHJSD generally have excellent binding affinity to the macromolecular target proteins encoded by the top 10 genes. Conclusion. This study reveals the characteristics of multi-component, multi-target, and multi-pathway of DHJSD against osteoporosis and provides novel insights for verifying the mechanism of DHJSD in the treatment of osteoporosis.


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## Body

## 1. Introduction

Osteoporosis is characterized by low bone mass, impaired bone microstructure, increased bone fragility, and susceptibility to fracture and is also a systemic bone disease [1]. Osteoporosis can occur in different genders and ages, but it is more common in postmenopausal women and elderly men [2]. The serious consequences of osteoporosis are fragility fractures that can occur during minor trauma or daily activities, leading to an increase in disability and mortality [3]. Moreover, the treatment and nursing of osteoporosis and osteoporotic fracture need to invest huge manpower and material resources, and the cost is high, resulting in heavy family, social, and economic burden [4]. Therefore, early adoption of scientific prevention strategies and standardized treatment is very necessary. Among these, drug therapies such as bisphosphonates and denosumab are commonly used to treat osteoporosis by inhibiting the development, formation, and survival of osteoclasts [1]. Although these drugs are effective, high-dose or long-term use may cause serious adverse effects, such as gastrointestinal tolerance, atypical long bone fracture, jaw osteonecrosis, and acute renal failure [1]. Therefore, continuing to search for potential drugs with significant efficacy and high safety has become a consistent hot spot for the treatment of osteoporosis.As we all know, traditional Chinese medicine (TCM) plays an important role in health maintenance in China and other Asian countries [5]. For a long time, TCM has been used as a complementary and alternative treatment option for patients with osteoporosis [6]. Systematic reviews and experimental studies have explored the efficacy and safety of TCM prescriptions in the treatment of osteoporosis, including Duhuo Jisheng Decoction (DHJSD), Xianling Gubao capsules, Liuwei Dihuang Decoction, and Erxian Decoction [2, 7–9].DHJSD is a TCM recorded in Bei Ji Qian Jin Yao Fang of the Tang Dynasty for the treatment of “Bi Zheng,” usually consisting of the following 15 herbs, including Du Huo (Radix Angelicae Pubescentis), Sang Ji Sheng (Herba Taxilli), Qin Jiao (Radix Gentianae Macrophyllae), Fang Feng (Radix Saposhnikoviae), Xi Xin (Herba Asari), Fu Ling (Poria Cocos), Chuan Xiong (Rhizoma Chuanxiong), Bai Shao (Radix Paeoniae Alba), Du Zhong (Cortex Eucommiae Ulmoidis), Ren Shen (Panax Ginseng), Gan Cao (Radix Glycyrrhizae), Dang Gui (Radix Angelicae Sinensis), Niu Xi (Radix Achyranthis Bidentatae), Shu Di Huang (Radix Rehmanniae Preparata), and Rou Gui (Cortex Cinnamomi) [10]. Among them, Du Huo has the good effects of relieving the pain of “Bi Zheng,” dispelling cold and dehumidification, nourishing blood and Qi; Xi Xin, Fang Feng, and Qin Jiao dispel rheumatism, relax tendons and muscles, and benefit joints; Sang Ji Sheng, Du Zhong, Rou Gui, and Niu Xi nourish liver and kidney and strengthen bones and muscles; Dang Gui, Bai Shao, Shu Di Huang, and Chuan Xiong promote blood circulation; Ren Shen, Gan Cao, and Fu Ling strengthen spleen and supplement Qi [11]. The combination of the above herbs forms DHJSD, the classic bone injury prescription in TCM, it has the effect of nourishing the liver and kidney, benefiting Qi and blood, and stopping the pain of “Bi Zheng”, which obviously improves the microcirculation of the body [2]. According to the theory of Chinese medicine, “Bi Zheng” refers to the symptoms of numbness, soreness, and poor flexion of muscles and joints caused by external factors (wind, dampness, cold, heat) on the body surface, tendons, and veins [12]. Osteoporosis belongs to the category of “Gu Bi” and “Gu Lou” in TCM theory [13]. And the cause of osteoporosis is the deficiency of Qi and blood in the liver and kidney and the loss of nutrition in the tendons and bones, so the treatment should be to nourish the liver and kidney and strengthen the tendons and bones as the primary treatment [14]. Therefore, DHJSD has been widely used in China to treat rheumatoid arthritis, intervertebral disc disease, knee osteoarthritis, and osteoporosis, especially postmenopausal osteoporosis [2, 11, 14–17]. However, due to the ingredients contained, the mechanism of action of TCM is often elusive.The network pharmacology of TCM is a part of bioinformatics, and it is still a priori analysis method for studying the relationship between drugs, compounds, diseases, and targets [18]. Due to the characteristics of multi-component, multi-target, and multi-pathway of TCM, network pharmacology has been widely used to clarify the mechanism of action of TCM and provide researchers with new directions and strategies. In this work, we tried to use the network pharmacology method to reveal the active compounds of DHJSD, and the key genes and pathways of DHJSD against osteoporosis, which facilitated further research and development (Figure 1).Figure 1
The schematic diagram of the present study to investigate potential mechanisms of DHJSD against osteoporosis.


## 2. Materials and Methods

### 2.1. Screening of Potential Active Compounds and Related Targets in DHJSD

We used the Traditional Chinese Medicine Systems Pharmacology (TCMSP, Version: 2.3,https://tcmspw.com/tcmsp.php) database and the analysis platform [19], and input the names of 15 Chinese herbal medicines in DHJSD to obtain the corresponding compounds and related information. According to the absorption, distribution, metabolism, and excretion (ADME) protocols, the active compounds were screened, and the criteria were oral bioavailability (OB) ≥30 and drug-likeness (DL) ≥0.18 [20, 21]. Then, the potential target proteins of the selected active compounds were mined in TCSMP database to construct the potential target gene set of DHJSD. UniProt database (https://www.uniprot.org/) was used to obtain the unique corresponding gene names and UniProt ID of drug or disease-related targets [22]. The species was selected as “Homo sapiens.”


### 2.2. Mining of Osteoporosis-Related Targets

Osteoporosis-related targets were obtained through retrieving GeneCards (https://www.genecards.org/) [23], MalaCards (https://www.malacards.org/) [24], DisGeNet database (https://www.disgenet.org/, v7.0) [25], Therapeutic Target Database (TTD) (http://db.idrblab.net/ttd/) [26], Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/, Last update by June, 2020) [27], and Online Mendelian Inheritance in Man (OMIM) (https://omim.org/, updated November 25, 2020) [28] using the keyword “osteoporosis.” GeneCards is a comprehensive, user-friendly database providing information on all annotated and predicted human genes, and we screen out targets with the relevance score ≥10 [23]. DisGeNET is a discovery platform that contains one of the largest public collections of genes and variants related to human diseases; targets with the score ≥0.1 were screened [25]. CTD is a powerful public database designed to improve people's understanding of how environmental exposure affects human health; targets with the inference score ≥20 were screened [27]. Finally, the potential targets obtained from the six databases mentioned above were integrated and de-duplicated to construct a set of targets relevant to osteoporosis.


### 2.3. Network Construction and Analysis

DHJSD-related targets and osteoporosis-related targets were entered separately into the Venn online tool (http://www.bioinformatics.com.cn/) for common targets, which were candidate targets of DHJSD against osteoporosis. The interaction between “DHJSD-active compounds-target genes-osteoporosis” was constructed by Cytoscape software (version 3.7.2) [29].


### 2.4. Protein-Protein Interaction (PPI) Analysis

PPI underlies most biological processes in living cells and is essential for understanding cell physiology in normal and disease states [10]. In this study, PPI network analysis of the obtained common targets was performed using the STRING database (http://string-db.org/; version 11) with the species limited to “Homo sapiens” and a confidence score >0.4 [30]. The PPI networks were constructed by Cytoscape software (version 3.7.2). Additionally, the plug-in 12 CytoHubba algorithms of Cytoscape software (Degree, Maximal Clique Centrality (MCC), Density of Maximum Neighborhood Component (DMNC), Maximum Neighborhood Component (MNC), Edge Percolated Component (EPC), Closeness, Betweenness, ClusteringCoefficient, EcCentricity, Radiality, Stress, BottleNeck) were used to select the first 10 nodes for finding the hub genes [31, 32].


### 2.5. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Enrichment Analysis

We performed GO and KEGG pathway enrichment analysis using the clusterProfiler package inR (R 4.0.2 for Windows) to identify the biological processes and signaling pathways involved in DHJSD in treating osteoporosis [33–35]. An adjusted P value of <0.05 was considered to identify the enriched terms.


### 2.6. Molecular Docking

The plug-in CytoHubba of Cytoscape software was used to screen top 10 hub genes [32]. In addition, Sankey diagram (http://sankeymatic.com/) was built with top 10 hub genes and relative active ingredients of DHJSD to find out key active ingredients. Sankey diagram discloses the relationship among herb, ingredients, and targets. Subsequently, molecular docking between top 10 hub genes and key active ingredients was carried out to predict the accuracy of the pivotal components and prediction targets using AutoDock Vina [36]. PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and RCSB protein data (http://www.rcsb.org/) were selected to download the MOL2 format of ligands and PDB format of proteins [4]. Crystal of proteins were introduced to Pymol software (https://pymol.org/2/; version 2.4.1) to conduct dehydration and separation of ligands [37]. Subsequently, the crystal conducted was introduced to AutoDockTools to build a docking grid box of targets [1]. Molecular dockings were achieved via AutoDock Vina [37]. The lower Vina scores, one of the results of molecular docking, represent a more stable binding affinity of protein and ligand [38]. Eventually, the complexes of protein and compound were visualized by Pymol software.


## 2.1. Screening of Potential Active Compounds and Related Targets in DHJSD

We used the Traditional Chinese Medicine Systems Pharmacology (TCMSP, Version: 2.3,https://tcmspw.com/tcmsp.php) database and the analysis platform [19], and input the names of 15 Chinese herbal medicines in DHJSD to obtain the corresponding compounds and related information. According to the absorption, distribution, metabolism, and excretion (ADME) protocols, the active compounds were screened, and the criteria were oral bioavailability (OB) ≥30 and drug-likeness (DL) ≥0.18 [20, 21]. Then, the potential target proteins of the selected active compounds were mined in TCSMP database to construct the potential target gene set of DHJSD. UniProt database (https://www.uniprot.org/) was used to obtain the unique corresponding gene names and UniProt ID of drug or disease-related targets [22]. The species was selected as “Homo sapiens.”


## 2.2. Mining of Osteoporosis-Related Targets

Osteoporosis-related targets were obtained through retrieving GeneCards (https://www.genecards.org/) [23], MalaCards (https://www.malacards.org/) [24], DisGeNet database (https://www.disgenet.org/, v7.0) [25], Therapeutic Target Database (TTD) (http://db.idrblab.net/ttd/) [26], Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/, Last update by June, 2020) [27], and Online Mendelian Inheritance in Man (OMIM) (https://omim.org/, updated November 25, 2020) [28] using the keyword “osteoporosis.” GeneCards is a comprehensive, user-friendly database providing information on all annotated and predicted human genes, and we screen out targets with the relevance score ≥10 [23]. DisGeNET is a discovery platform that contains one of the largest public collections of genes and variants related to human diseases; targets with the score ≥0.1 were screened [25]. CTD is a powerful public database designed to improve people's understanding of how environmental exposure affects human health; targets with the inference score ≥20 were screened [27]. Finally, the potential targets obtained from the six databases mentioned above were integrated and de-duplicated to construct a set of targets relevant to osteoporosis.


## 2.3. Network Construction and Analysis

DHJSD-related targets and osteoporosis-related targets were entered separately into the Venn online tool (http://www.bioinformatics.com.cn/) for common targets, which were candidate targets of DHJSD against osteoporosis. The interaction between “DHJSD-active compounds-target genes-osteoporosis” was constructed by Cytoscape software (version 3.7.2) [29].


## 2.4. Protein-Protein Interaction (PPI) Analysis

PPI underlies most biological processes in living cells and is essential for understanding cell physiology in normal and disease states [10]. In this study, PPI network analysis of the obtained common targets was performed using the STRING database (http://string-db.org/; version 11) with the species limited to “Homo sapiens” and a confidence score >0.4 [30]. The PPI networks were constructed by Cytoscape software (version 3.7.2). Additionally, the plug-in 12 CytoHubba algorithms of Cytoscape software (Degree, Maximal Clique Centrality (MCC), Density of Maximum Neighborhood Component (DMNC), Maximum Neighborhood Component (MNC), Edge Percolated Component (EPC), Closeness, Betweenness, ClusteringCoefficient, EcCentricity, Radiality, Stress, BottleNeck) were used to select the first 10 nodes for finding the hub genes [31, 32].


## 2.5. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Enrichment Analysis

We performed GO and KEGG pathway enrichment analysis using the clusterProfiler package inR (R 4.0.2 for Windows) to identify the biological processes and signaling pathways involved in DHJSD in treating osteoporosis [33–35]. An adjusted P value of <0.05 was considered to identify the enriched terms.


## 2.6. Molecular Docking

The plug-in CytoHubba of Cytoscape software was used to screen top 10 hub genes [32]. In addition, Sankey diagram (http://sankeymatic.com/) was built with top 10 hub genes and relative active ingredients of DHJSD to find out key active ingredients. Sankey diagram discloses the relationship among herb, ingredients, and targets. Subsequently, molecular docking between top 10 hub genes and key active ingredients was carried out to predict the accuracy of the pivotal components and prediction targets using AutoDock Vina [36]. PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and RCSB protein data (http://www.rcsb.org/) were selected to download the MOL2 format of ligands and PDB format of proteins [4]. Crystal of proteins were introduced to Pymol software (https://pymol.org/2/; version 2.4.1) to conduct dehydration and separation of ligands [37]. Subsequently, the crystal conducted was introduced to AutoDockTools to build a docking grid box of targets [1]. Molecular dockings were achieved via AutoDock Vina [37]. The lower Vina scores, one of the results of molecular docking, represent a more stable binding affinity of protein and ligand [38]. Eventually, the complexes of protein and compound were visualized by Pymol software.


## 3. Results

### 3.1. Screening of Active Compounds in DHJSD

A total of 1939 compounds in DHJSD were retrieved through the TCMSP database, of which 99 were from Du Huo, 46 were from Sang Ji Sheng, 27 were from Qin Jiao, 173 were from Fang Feng, 192 were from Xi Xin, 34 were from Fu Ling, 189 were from Chuan Xiong, 85 from Bai Shao, 147 from Du Zhong 190 from Ren Shen, 280 from Gan Cao, 125 from Dang Gui, 176 from Niu Xi, 76 from Shu Di Huang, and 100 from Rou Gui. According to the criteria of OB ≥30% and DL ≥0.18, a total of 240 (after removing duplication: 209) active compounds of DHJSD were screened, of which 9 were from Du Huo, 2 were from Sang Ji Sheng, 2 were from Qin Jiao, 18 were from Fang Feng, and 8 were from Xi Xin, 15 from Fu Ling, 7 from Chuan Xiong, 13 from Bai Shao, 28 from Du Zhong, 22 from Ren Shen, 92 from Gan Cao, 2 from Dang Gui, 20 from Niu Xi, and 2 from Shu Di Huang. However, there is no compound in Rou Gui that meets the screening criteria. Table1 shows the basic information of some active compounds in DHJSD.Table 1
Basic information of some active compounds in DHJSD.
Molecule IDMolecule nameOB (%)DL2D structureSourcePubChem CIDMOL001924Paeoniflorin53.870.79Bai Shao (Radix Paeoniae Alba)442534MOL000492Cianidanol54.830.24Bai Shao9064MOL002135Myricanone40.600.51Chuan Xiong (Rhizoma Chuanxiong)161748MOL001494Ethyl linoleate42.000.19Chuan Xiong5282184MOL003608O-acetylcolumbianetin60.040.26Du Huo (Radix Angelicae Pubescentis)161409MOL004780Angelicone30.990.19Du Huo616303MOL002773Beta-carotene37.180.58Du Zhong (Cortex Eucommiae Ulmoidis)5280489MOL009031Epiquinidine68.220.40Du Zhong94175MOL000296Hederagenin36.910.75Fu Ling (Poria Cocos)NAMOL00027316alpha-Hydroxydehydrotrametenolic acid30.930.81Fu Ling10743008MOL004328Naringenin59.290.21Gan Cao (Radix Glycyrrhizae)439246MOL000354Isorhamnetin49.600.31Gan Cao5281654MOL000392Formononetin69.670.21Gan Cao5280378MOL002714Baicalein33.520.21Niu Xi (Radix Achyranthis Bidentatae)5281605MOL000785Palmatine64.600.65Niu Xi19009MOL005344Ginsenoside rh236.320.56Ren Shen (Panax Ginseng)119307MOL000787Fumarine59.260.83Ren Shen4970MOL001558Sesamin56.550.83Xi Xin (Herba Asari)72307MOL0029623-O-Methylviolanone48.230.33Xi Xin10019512MOL0117535-O-Methylvisamminol37.990.25Fang Feng (Radix Saposhnikoviae)441970MOL000011Cleomiscosin A68.830.66Fang Feng442510MOL000449Stigmasterol43.830.76Dang Gui (Radix Angelicae Sinensis), Niu Xi, Ren Shen, Shu Di Huang5280794MOL0003593-Epi-beta-Sitosterol36.910.75Bai Shao, Chuan Xiong, Fang Feng, Gan Cao, Qin Jiao, Sang Ji Sheng, Shu Di Huang (Radix Rehmanniae Preparata)12303645MOL000098Quercetin46.430.28Du Zhong, Gan Cao, Sang Ji Sheng (Herba Taxilli), Niu Xi5280343MOL000358Beta-sitosterol36.910.75Bai Shao, Dang Gui, Du Huo, Du Zhong, Fang Feng, Niu Xi, Qin Jiao, Ren Shen222284MOL000422Kaempferol41.880.24Bai Shao, Du Zhong, Gan Cao, Niu Xi, Ren Shen, Xi Xin5280863MOL000173Wogonin30.680.23Fang Feng, Niu Xi5281703MOL000211Mairin55.380.78Bai Shao, Du Zhong, Gan Cao64971MOL001942Isoimperatorin45.460.23Du Huo, Fang Feng68081MOL001941Ammidin34.550.22Du Huo, Fang Feng10212Abbreviations: DHJSD, Duhuo Jisheng Decoction; OB, oral bioavailability; DL, drug-likeness.


### 3.2. Mining of the Putative Target Genes for DHJSD

By using the compound-target search function of TCMSP database, it was confirmed that 179 of the 209 active compounds in DHJSD possess target proteins. Subsequently, UniProt database was used to convert the target protein predicted by the biologically active compound of DHJSD into gene name. Finally, 267 putative target genes were identified.


### 3.3. Mining of the Potential Therapeutic Targets of DHJSD in Treating Osteoporosis

A total of 3131 potential therapeutic targets of osteoporosis were obtained by searching GeneCards, MalaCards, DisGeNET, TTD, CTD, and OMIM databases. The Venn diagram was drawn by using Venn online platform, which is derived from the targets regulated by the active ingredients of DHJSD and the potential targets of osteoporosis. Subsequently, a total of 205 core targets were obtained, which were the potential therapeutic targets of DHJSD in the treatment of osteoporosis, as shown in Figure2. The “DHJSD-active compounds-target genes-osteoporosis” network of DHJSD against osteoporosis was constructed by Cytoscape software (Figure 3). In this case, the sub-network of “active compounds-target genes” includes 379 nodes and 1856 edges. We ranked the target genes according to the number of targeting nodes. Table 2 shows the basic information of some key targets of DHJSD against osteoporosis.Figure 2
Venn diagram of DHJSD-related targets and osteoporosis-related targets.Figure 3
“DHJSD-active compounds-target genes-osteoporosis” network. The red octagon represents the disease; the brown diamond represents the herbs contained in DHJSD; the cyan rectangle represents the potential targets; the blue ellipse belongs to the herbs contained in DHJSD; and the purple ones are common ingredients. The line between two nodes represents the interaction; the size of each node represents the number of connections.Table 2
Basic information of some key targets of DHJSD against osteoporosis.
UniProt IDGene symbolProtein namesDegreeP35354PTGS2Prostaglandin G/H synthase 2144P03372ESR1Estrogen receptor98P0DP23CALM1Calmodulin-196P07900HSP90AA1Heat shock protein HSP 90-alpha96P10275ARAndrogen receptor85P35228NOS2Nitric oxide synthase, inducible80P23219PTGS1Prostaglandin G/H synthase 174Q15596NCOA2Nuclear receptor coactivator 270P24941CDK2Cyclin-dependent kinase 268P37231PPARGPeroxisome proliferator-activated receptor gamma68P49841GSK3BGlycogen synthase kinase-3 beta65Q92731ESR2Estrogen receptor beta62P20248CCNA2Cyclin-A259P27487DPP4Dipeptidyl peptidase 457P07550ADRB2Beta-2 adrenergic receptor52P19793RXRARetinoic acid receptor RXR-alpha52Q16539MAPK14Mitogen-activated protein kinase 1451O14757CHEK1Serine/threonine-protein kinase Chk148P22303ACHEAcetylcholinesterase34P08709F7Coagulation factor VII33Q15788NCOA1Nuclear receptor coactivator 125P35968KDRVascular endothelial growth factor receptor 224P25100ADRA1DAlpha-1D adrenergic receptor15P06401PGRProgesterone receptor15P31645SLC6A4Sodium-dependent serotonin transporter15P35372OPRM1Mu-type opioid receptor14P21728DRD1D (1A) dopamine receptor12P08235NR3C2Mineralocorticoid receptor12Q01959SLC6A3Sodium-dependent dopamine transporter12P27338MAOBAmine oxidase [flavin-containing] B10Q92934BCL2Bcl2-associated agonist of cell death8P00918CA2Carbonic anhydrase 28P42574CASP3Caspase-38Q04206RELATranscription factor p657P31749AKT1RAC-alpha serine/threonine-protein kinase6Q07812BAXApoptosis regulator BAX6P05412JUNTranscription factor AP-16O95150TNFSF15Tumor necrosis factor ligand superfamily member 155P55211CASP9Caspase-94P24385CCND1G1/S-specific cyclin-D14P03956MMP1Interstitial collagenase4P08588ADRB1Beta-1 adrenergic receptor3P35869AHRAryl hydrocarbon receptor3P15121AKR1B1Aldo-keto reductase family 1 member B13Q14790CASP8Caspase-83P06493CDK1Cyclin-dependent kinase 13P05177CYP1A2Cytochrome P450 1A23P08684CYP3A4Cytochrome P450 3A43P09211GSTP1Glutathione S-transferase P3Q92819HAS2Hyaluronan synthase 23P09601HMOX1Heme oxygenase 13P05231IL6Interleukin-63P28482MAPK1Mitogen-activated protein kinase 13P04150NR3C1Glucocorticoid receptor3Q03181PPARDPeroxisome proliferator-activated receptor delta3P14672SLC2A4Solute carrier family 2, facilitated glucose transporter member 43P00441SOD1Superoxide dismutase [Cu-Zn]3Q9H3D4TP63Tumor protein 633P15692VEGFAVascular endothelial growth factor A3Q13085ACACAAcetyl-CoA carboxylase 12P00533EGFREpidermal growth factor receptor2P01106MYCMyc proto-oncogene protein2P02768ALBAlbumin1P01133EGFPro-epidermal growth factor1P27361MAPK3Mitogen-activated protein kinase 31


### 3.4. Construction of the PPI Network and Mining of the Hub Genes of DHJSD in Treating Osteoporosis

A total of 205 potential target genes of DHJSD in the treatment of osteoporosis were input into STRING database to obtain PPI network. The network involves 205 nodes and 4078 edges. Then, the obtained data was imported into Cytoscape 3.7.2 version for further visualization (Figure4(a)). Subsequently, according to the 12 CytoHubba algorithms of Cytoscape software, including Degree, MCC, DMNC, MNC, EPC, Closeness, Betweenness, ClusteringCoefficient, EcCentricity, Radiality, Stress, and BottleNeck, the top 10 hub genes of DHJSD for treating osteoporosis were selected based on the above results. The top 10 genes calculated based on 12 algorithms contain a total of 41 different genes. We showed the relationship between these genes and the corresponding algorithms in the form of Chord diagrams (Figure 4(b)). Then, we sorted the genes according to the number of algorithms corresponding to them and got the top 10 hub genes, and the results were consistent with the results of the Degree algorithm (Figure 4(c)). The top 10 hub genes contained AKT1, ALB, IL6, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, and EGF. In addition, the Sankey diagram was constructed using the top 10 hub genes and the relative active compounds of DHJSD, of which MOL000098 (Quercetin) targets most hub genes (Figure 5).Figure 4
The PPI network of potential targets of DHJSD in the treatment of osteoporosis. (a) The PPI network from STRING was further analyzed using Cytoscape software (the line between two nodes indicates the interaction. The darker the color of the node, the better the relationship between them). (b) Chord diagram of the corresponding relationship between the top 10 genes and 12 CytoHubba algorithms. (c) The top 10 hub genes were identified by Degree (the darker the color of the node, the greater the degree).
(a)(b)(c)Figure 5
Sankey diagram. The colored vertical bars on the left represent the different herbs contained in DHJSD, the middle bars represent the active ingredients of the herbs, and the right bars represent the target genes. The size of the bars and the thickness of the lines represent the number of interactions.


### 3.5. GO and KEGG Pathway Enrichment Analysis

In order to elucidate the biological mechanisms of DHJSD against osteoporosis, GO and KEGG pathway enrichment analysis was performed by using clusterProfiler in R. The 205 potential targets of DHJSD for treating osteoporosis were input into theR, and a total of 2590 GO terms (adjusted, P<0.05) were obtained, including 2320 biological process (BP) terms and 179 molecular function (MF) terms and 91 cellular component (CC) terms. Based on the adjusted P value from small to large, the top 10 GO-BP terms were mainly enriched in cellular response to chemical stress (GO:0062197), response to metal ion (GO:0010038), response to antibiotic (GO:0046677), response to lipopolysaccharide (GO:0032496), response to alcohol (GO:0097305), response to steroid hormone (GO:0048545), response to molecule of bacterial origin (GO:0002237), response to oxidative stress (GO:0006979), response to reactive oxygen species (GO:0000302), and response to nutrient levels (GO:0031667). The top 10 GO-CC terms were mainly enriched in membrane raft (GO:0045121), membrane microdomain (GO:0098857), membrane region (GO:0098589), transcription regulator complex (GO:0005667), vesicle lumen (GO:0031983), RNA polymerase II transcription regulator complex (GO:0090575), cyclin-dependent protein kinase holoenzyme complex (GO:0000307), serine/threonine protein kinase complex (GO:1902554), mitochondrial outer membrane (GO:0005741), and cytoplasmic vesicle lumen (GO:0060205). The top 10 GO-MF terms were mainly enriched in nuclear receptor activity (GO:0004879), ligand-activated transcription factor activity (GO:0098531), steroid hormone receptor activity (GO:0003707), RNA polymerase II-specific DNA-binding transcription factor binding (GO:0061629), DNA-binding transcription factor binding (GO:0140297), “DNA-binding transcription activator activity, RNA polymerase II-specific” (GO:0001228), DNA-binding transcription activator activity (GO:0001216), phosphatase binding (GO:0019902), heme binding (GO:0020037), and NADP binding (GO:0050661). Finally, the top 10 GO enrichment terms were selected and visualized using bar diagram, as shown in Figure 6. Table 3 shows the top 10 GO enrichment items.Figure 6
Barplot of the top 10 GO enrichment items. The GO enrichment items (BP, CC, MF) are arranged from left to right according to the adjustedP value.Table 3
The top 10 Gene Ontology (GO) enrichment items.
IDDescriptionP valueAdjustP valueGene numberGO itemsGO:0062197Cellular response to chemical stress2.20332E− 369.75E −3346Biological processGO:0010038Response to metal ion1.04408E− 352.31E −3246Biological processGO:0046677Response to antibiotic5.44269E− 348.03E −3143Biological processGO:0032496Response to lipopolysaccharide8.0729E− 348.93E −3143Biological processGO:0097305Response to alcohol1.19658E− 331.06E −3038Biological processGO:0048545Response to steroid hormone2.14139E −331.58E −3045Biological processGO:0002237Response to molecule of bacterial origin4.25534E −332.69E −3043Biological processGO:0006979Response to oxidative stress1.67651E −329.27E −3047Biological processGO:0000302Response to reactive oxygen species2.14509E −321.05E −2937Biological processGO:0031667Response to nutrient levels1.65363E −307.32E −2847Biological processGO:0004879Nuclear receptor activity1.2238E −163.33E −1414Molecular functionGO:0098531Ligand-activated transcription factor activity1.2238E −163.33E −1414Molecular functionGO:0003707Steroid hormone receptor activity1.51056E −122.74E −1012Molecular functionGO:0061629RNA polymerase II-specific DNA-binding transcription factor binding1.0407E −111.42E −0921Molecular functionGO:0140297DNA-binding transcription factor binding2.02886E −112.21E −0923Molecular functionGO:0001228DNA-binding transcription activator activity, RNA polymerase II-specific5.32777E −114.35E −0925Molecular functionGO:0001216DNA-binding transcription activator activity5.59254E −114.35E −0925Molecular functionGO:0019902Phosphatase binding4.84351E −103.29E −0816Molecular functionGO:0020037Heme binding5.8588E −093.54E −0713Molecular functionGO:0050661NADP binding7.53723E −094.10E −079Molecular functionGO:0045121Membrane raft3.10297E −155.63E −1325Cellular componentGO:0098857Membrane microdomain3.33964E −155.63E −1325Cellular componentGO:0098589Membrane region7.90284E −158.88E −1325Cellular componentGO:0005667Transcription regulator complex4.33831E −103.66E −0822Cellular componentGO:0031983Vesicle lumen1.59056E −091.07E −0719Cellular componentGO:0090575RNA polymerase II transcription regulator complex2.17645E −091.22E −0714Cellular componentGO:0000307Cyclin-dependent protein kinase holoenzyme complex1.03807E −085.00E −078Cellular componentGO:1902554Serine/threonine protein kinase complex2.64297E −081.11E −0610Cellular componentGO:0005741Mitochondrial outer membrane4.65534E −081.74E −0613Cellular componentGO:0060205Cytoplasmic vesicle lumen5.57446E −081.88E −0617Cellular componentAdditionally, a total of 170 enriched KEGG pathways (adjusted,P<0.05) were obtained, the top 10 including AGE-RAGE signaling pathway in diabetic complications (hsa04933), Kaposi sarcoma-associated herpesvirus infection (hsa05167), fluid shear stress and atherosclerosis (hsa05418), hepatitis B (hsa05161), prostate cancer (hsa05215), hepatitis C (hsa05160), pancreatic cancer (hsa05212), human cytomegalovirus infection (hsa05163), IL-17 signaling pathway (hsa04657), and TNF signaling pathway (hsa04668). The top 20 KEGG pathway enrichment terms were selected and visualized using bar diagram, as shown in Figure 7. Next, we searched the KEGG pathway database for osteoporosis, looking for potential pathways related to osteoporosis in the enriched 170 pathways. A total of 50 pathways were selected that may be associated with osteoporosis, and then we proceeded to build a network map of target genes and pathways using Cytoscape software (Figure 8). These include 13 of the pathways most strongly associated with osteoporosis, including osteoclast differentiation (hsa04380), AGE-RAGE signaling pathway in diabetic complications (hsa04933), Wnt signaling pathway (hsa04310), MAPK signaling pathway (hsa04010), apoptosis (hsa04210), chemokine signaling pathway (hsa04062), T cell receptor signaling pathway (hsa04660), B cell receptor signaling pathway (hsa04662), PI3K-Akt signaling pathway (hsa04151), JAK-STAT signaling pathway (hsa04630), calcium signaling pathway (hsa04020), NF-kappa B signaling pathway (hsa04064), and TNF signaling pathway (hsa04668). Table 4 shows the enriched 50 possible related pathways of osteoporosis.Figure 7
Barplot of the top 20 KEGG pathway enrichment items. The color of each bar represents the adjustedP value, and the length represents the number of enriched genes.Figure 8
“Target genes-pathways” network. The cyan ellipse represents the potential targets of DHJSD against osteoporosis; the red ellipse represents the top 10 hub genes of DHJSD against osteoporosis; the brown arrow represents potential osteoporosis-related pathways; the light blue arrow represents osteoporosis-related pathways. The line between two nodes represents the interaction; the size of each node represents the number of connections. The lines consistent with the color of each hub gene represent the pathways enriched by that gene.Table 4
The enriched 50 possible related pathways of osteoporosis.
IDDescriptionP valueAdjustP valueGene numberhsa04933AGE-RAGE signaling pathway in diabetic complications3.2722E −318.83495E −2934hsa05418Fluid shear stress and atherosclerosis8.02758E −266.42442E −2434hsa04657IL-17 signaling pathway2.23263E −216.69789E −2026hsa04668TNF signaling pathway1.93321E −205.21968E −1927hsa04659Th17 cell differentiation1.77974E −173.00331E −1624hsa04210Apoptosis5.07009E −178.0525E −1626hsa04218Cellular senescence1.70327E −151.9995E −1426hsa04066HIF-1 signaling pathway4.55414E −154.72929E −1422hsa01521EGFR tyrosine kinase inhibitor resistance1.19998E −141.11722E −1319hsa04115p53 signaling pathway3.86218E −143.36383E −1318hsa05235PD-L1 expression and PD-1 checkpoint pathway in cancer1.25365E −139.95542E −1319hsa04380Osteoclast differentiation1.52063E −131.14047E −1222hsa04620Toll-like receptor signaling pathway2.26415E −131.5283E −1220hsa04660T Cell receptor signaling pathway2.26415E −131.5283E −1220hsa04012ErbB signaling pathway6.6254E −134.25919E −1218hsa04151PI3K-akt signaling pathway1.24292E −127.80436E −1234hsa04370VEGF signaling pathway3.47037E −121.99361E −1115hsa04915Estrogen signaling pathway6.54943E −123.60403E −1121hsa04010MAPK signaling pathway6.67413E −123.60403E −1130hsa04658Th1 and Th2 cell differentiation3.05E −111.55E −1017hsa04068FoxO signaling pathway1.6511E −107.96064E −1019hsa04064NF-kappa B signaling pathway2.32207E −101.09993E −0917hsa04621NOD-like receptor signaling pathway1.2042E −095.16086E −0921hsa04931Insulin resistance3.58386E −091.423E −0816hsa04662B Cell receptor signaling pathway5.33879E −092.08909E −0814hsa04024cAMP signaling pathway5.66891E −092.1641E −0822hsa04910Insulin signaling pathway1.80579E −086.58868E −0817hsa04912GnRH signaling pathway2.87106E −081.03358E −0714hsa04071Sphingolipid signaling pathway1.04592E −073.62048E −0715hsa04014Ras signaling pathway4.64353E −071.475E −0620hsa04920Adipocytokine signaling pathway5.07934E −071.57635E −0611hsa04062Chemokine signaling pathway5.25301E −071.61172E −0618hsa04110Cell cycle1.11163E −063.15936E −0614hsa04630JAK-STAT signaling pathway1.15731E −063.25493E −0616hsa00140Steroid hormone biosynthesis1.30408E −063.59286E −0610hsa04930Type II diabetes mellitus9.82492E −062.50258E −058hsa05323Rheumatoid arthritis1.03843E −052.62033E −0511hsa04928Parathyroid hormone synthesis, secretion and action3.6295E −058.67227E −0511hsa03320PPAR signaling pathway6.67976E −050.0001554779hsa04020Calcium signaling pathway7.36869E −050.00016860615hsa04310Wnt signaling pathway9.54755E −050.00021304513hsa04929GnRH secretion0.0001160740.0002547978hsa04022cGMP-PKG signaling pathway0.0001474230.00032100113hsa04015Rap1 signaling pathway0.000415790.00086356414hsa04666Fc gamma R-mediated phagocytosis0.001959760.0038343138hsa04152AMPK signaling pathway0.0020195540.0039228749hsa01212Fatty acid metabolism0.0020982860.0039896996hsa04923Regulation of lipolysis in adipocytes0.0020982860.0039896996hsa04150mTOR signaling pathway0.0107730830.0184096999hsa04918Thyroid hormone synthesis0.0313619710.0498101895


### 3.6. Molecular Docking Results

According to the results of the Sankey diagram, we carried out molecular docking of the core target protein and active compound involved. Molecular docking between top 10 target proteins (AKT1, ALB, IL6, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, EGF) and key active compounds (Quercetin, Kaempferol, Beta-sitosterol, Wogonin, Beta-carotene, Baicalein, Naringenin, Formononetin, Paeoniflorin, Ginsenoside Rh2, and Epiquinidine) was carried out using AutoDock Vina. The docking scores of the strongest affinity of 10 core target proteins and 11 key active compounds were visualized using the heatmap, as shown in Figure9. The binding energy between target proteins and the active compounds was approximately between −5.8 and −10.9 kcal mol−1. AKT1, ALB, MAPK3, JUN, CASP3, and EGFR have stronger docking energy. The remaining target proteins also have relatively strong docking energy, which means that the compounds in DHJSD bind well to 10 core target proteins. Eventually, we chose the top 4 target protein macromolecules and small compound molecules with the best docking affinity for visualization with Pymol (Figure 10).Figure 9
Heatmap of the docking scores of the active compounds of DHJSD and the target proteins.Figure 10
Molecular docking results of “bioactive compound-hub gene.” (a) Quercetin to AKT1; (b) Beta-sitosterol to AKT1; (c) Beta-carotene to AKT1; (d) Formononetin to AKT1.
(a)(b)(c)(d)


## 3.1. Screening of Active Compounds in DHJSD

A total of 1939 compounds in DHJSD were retrieved through the TCMSP database, of which 99 were from Du Huo, 46 were from Sang Ji Sheng, 27 were from Qin Jiao, 173 were from Fang Feng, 192 were from Xi Xin, 34 were from Fu Ling, 189 were from Chuan Xiong, 85 from Bai Shao, 147 from Du Zhong 190 from Ren Shen, 280 from Gan Cao, 125 from Dang Gui, 176 from Niu Xi, 76 from Shu Di Huang, and 100 from Rou Gui. According to the criteria of OB ≥30% and DL ≥0.18, a total of 240 (after removing duplication: 209) active compounds of DHJSD were screened, of which 9 were from Du Huo, 2 were from Sang Ji Sheng, 2 were from Qin Jiao, 18 were from Fang Feng, and 8 were from Xi Xin, 15 from Fu Ling, 7 from Chuan Xiong, 13 from Bai Shao, 28 from Du Zhong, 22 from Ren Shen, 92 from Gan Cao, 2 from Dang Gui, 20 from Niu Xi, and 2 from Shu Di Huang. However, there is no compound in Rou Gui that meets the screening criteria. Table1 shows the basic information of some active compounds in DHJSD.Table 1
Basic information of some active compounds in DHJSD.
Molecule IDMolecule nameOB (%)DL2D structureSourcePubChem CIDMOL001924Paeoniflorin53.870.79Bai Shao (Radix Paeoniae Alba)442534MOL000492Cianidanol54.830.24Bai Shao9064MOL002135Myricanone40.600.51Chuan Xiong (Rhizoma Chuanxiong)161748MOL001494Ethyl linoleate42.000.19Chuan Xiong5282184MOL003608O-acetylcolumbianetin60.040.26Du Huo (Radix Angelicae Pubescentis)161409MOL004780Angelicone30.990.19Du Huo616303MOL002773Beta-carotene37.180.58Du Zhong (Cortex Eucommiae Ulmoidis)5280489MOL009031Epiquinidine68.220.40Du Zhong94175MOL000296Hederagenin36.910.75Fu Ling (Poria Cocos)NAMOL00027316alpha-Hydroxydehydrotrametenolic acid30.930.81Fu Ling10743008MOL004328Naringenin59.290.21Gan Cao (Radix Glycyrrhizae)439246MOL000354Isorhamnetin49.600.31Gan Cao5281654MOL000392Formononetin69.670.21Gan Cao5280378MOL002714Baicalein33.520.21Niu Xi (Radix Achyranthis Bidentatae)5281605MOL000785Palmatine64.600.65Niu Xi19009MOL005344Ginsenoside rh236.320.56Ren Shen (Panax Ginseng)119307MOL000787Fumarine59.260.83Ren Shen4970MOL001558Sesamin56.550.83Xi Xin (Herba Asari)72307MOL0029623-O-Methylviolanone48.230.33Xi Xin10019512MOL0117535-O-Methylvisamminol37.990.25Fang Feng (Radix Saposhnikoviae)441970MOL000011Cleomiscosin A68.830.66Fang Feng442510MOL000449Stigmasterol43.830.76Dang Gui (Radix Angelicae Sinensis), Niu Xi, Ren Shen, Shu Di Huang5280794MOL0003593-Epi-beta-Sitosterol36.910.75Bai Shao, Chuan Xiong, Fang Feng, Gan Cao, Qin Jiao, Sang Ji Sheng, Shu Di Huang (Radix Rehmanniae Preparata)12303645MOL000098Quercetin46.430.28Du Zhong, Gan Cao, Sang Ji Sheng (Herba Taxilli), Niu Xi5280343MOL000358Beta-sitosterol36.910.75Bai Shao, Dang Gui, Du Huo, Du Zhong, Fang Feng, Niu Xi, Qin Jiao, Ren Shen222284MOL000422Kaempferol41.880.24Bai Shao, Du Zhong, Gan Cao, Niu Xi, Ren Shen, Xi Xin5280863MOL000173Wogonin30.680.23Fang Feng, Niu Xi5281703MOL000211Mairin55.380.78Bai Shao, Du Zhong, Gan Cao64971MOL001942Isoimperatorin45.460.23Du Huo, Fang Feng68081MOL001941Ammidin34.550.22Du Huo, Fang Feng10212Abbreviations: DHJSD, Duhuo Jisheng Decoction; OB, oral bioavailability; DL, drug-likeness.


## 3.2. Mining of the Putative Target Genes for DHJSD

By using the compound-target search function of TCMSP database, it was confirmed that 179 of the 209 active compounds in DHJSD possess target proteins. Subsequently, UniProt database was used to convert the target protein predicted by the biologically active compound of DHJSD into gene name. Finally, 267 putative target genes were identified.


## 3.3. Mining of the Potential Therapeutic Targets of DHJSD in Treating Osteoporosis

A total of 3131 potential therapeutic targets of osteoporosis were obtained by searching GeneCards, MalaCards, DisGeNET, TTD, CTD, and OMIM databases. The Venn diagram was drawn by using Venn online platform, which is derived from the targets regulated by the active ingredients of DHJSD and the potential targets of osteoporosis. Subsequently, a total of 205 core targets were obtained, which were the potential therapeutic targets of DHJSD in the treatment of osteoporosis, as shown in Figure2. The “DHJSD-active compounds-target genes-osteoporosis” network of DHJSD against osteoporosis was constructed by Cytoscape software (Figure 3). In this case, the sub-network of “active compounds-target genes” includes 379 nodes and 1856 edges. We ranked the target genes according to the number of targeting nodes. Table 2 shows the basic information of some key targets of DHJSD against osteoporosis.Figure 2
Venn diagram of DHJSD-related targets and osteoporosis-related targets.Figure 3
“DHJSD-active compounds-target genes-osteoporosis” network. The red octagon represents the disease; the brown diamond represents the herbs contained in DHJSD; the cyan rectangle represents the potential targets; the blue ellipse belongs to the herbs contained in DHJSD; and the purple ones are common ingredients. The line between two nodes represents the interaction; the size of each node represents the number of connections.Table 2
Basic information of some key targets of DHJSD against osteoporosis.
UniProt IDGene symbolProtein namesDegreeP35354PTGS2Prostaglandin G/H synthase 2144P03372ESR1Estrogen receptor98P0DP23CALM1Calmodulin-196P07900HSP90AA1Heat shock protein HSP 90-alpha96P10275ARAndrogen receptor85P35228NOS2Nitric oxide synthase, inducible80P23219PTGS1Prostaglandin G/H synthase 174Q15596NCOA2Nuclear receptor coactivator 270P24941CDK2Cyclin-dependent kinase 268P37231PPARGPeroxisome proliferator-activated receptor gamma68P49841GSK3BGlycogen synthase kinase-3 beta65Q92731ESR2Estrogen receptor beta62P20248CCNA2Cyclin-A259P27487DPP4Dipeptidyl peptidase 457P07550ADRB2Beta-2 adrenergic receptor52P19793RXRARetinoic acid receptor RXR-alpha52Q16539MAPK14Mitogen-activated protein kinase 1451O14757CHEK1Serine/threonine-protein kinase Chk148P22303ACHEAcetylcholinesterase34P08709F7Coagulation factor VII33Q15788NCOA1Nuclear receptor coactivator 125P35968KDRVascular endothelial growth factor receptor 224P25100ADRA1DAlpha-1D adrenergic receptor15P06401PGRProgesterone receptor15P31645SLC6A4Sodium-dependent serotonin transporter15P35372OPRM1Mu-type opioid receptor14P21728DRD1D (1A) dopamine receptor12P08235NR3C2Mineralocorticoid receptor12Q01959SLC6A3Sodium-dependent dopamine transporter12P27338MAOBAmine oxidase [flavin-containing] B10Q92934BCL2Bcl2-associated agonist of cell death8P00918CA2Carbonic anhydrase 28P42574CASP3Caspase-38Q04206RELATranscription factor p657P31749AKT1RAC-alpha serine/threonine-protein kinase6Q07812BAXApoptosis regulator BAX6P05412JUNTranscription factor AP-16O95150TNFSF15Tumor necrosis factor ligand superfamily member 155P55211CASP9Caspase-94P24385CCND1G1/S-specific cyclin-D14P03956MMP1Interstitial collagenase4P08588ADRB1Beta-1 adrenergic receptor3P35869AHRAryl hydrocarbon receptor3P15121AKR1B1Aldo-keto reductase family 1 member B13Q14790CASP8Caspase-83P06493CDK1Cyclin-dependent kinase 13P05177CYP1A2Cytochrome P450 1A23P08684CYP3A4Cytochrome P450 3A43P09211GSTP1Glutathione S-transferase P3Q92819HAS2Hyaluronan synthase 23P09601HMOX1Heme oxygenase 13P05231IL6Interleukin-63P28482MAPK1Mitogen-activated protein kinase 13P04150NR3C1Glucocorticoid receptor3Q03181PPARDPeroxisome proliferator-activated receptor delta3P14672SLC2A4Solute carrier family 2, facilitated glucose transporter member 43P00441SOD1Superoxide dismutase [Cu-Zn]3Q9H3D4TP63Tumor protein 633P15692VEGFAVascular endothelial growth factor A3Q13085ACACAAcetyl-CoA carboxylase 12P00533EGFREpidermal growth factor receptor2P01106MYCMyc proto-oncogene protein2P02768ALBAlbumin1P01133EGFPro-epidermal growth factor1P27361MAPK3Mitogen-activated protein kinase 31


## 3.4. Construction of the PPI Network and Mining of the Hub Genes of DHJSD in Treating Osteoporosis

A total of 205 potential target genes of DHJSD in the treatment of osteoporosis were input into STRING database to obtain PPI network. The network involves 205 nodes and 4078 edges. Then, the obtained data was imported into Cytoscape 3.7.2 version for further visualization (Figure4(a)). Subsequently, according to the 12 CytoHubba algorithms of Cytoscape software, including Degree, MCC, DMNC, MNC, EPC, Closeness, Betweenness, ClusteringCoefficient, EcCentricity, Radiality, Stress, and BottleNeck, the top 10 hub genes of DHJSD for treating osteoporosis were selected based on the above results. The top 10 genes calculated based on 12 algorithms contain a total of 41 different genes. We showed the relationship between these genes and the corresponding algorithms in the form of Chord diagrams (Figure 4(b)). Then, we sorted the genes according to the number of algorithms corresponding to them and got the top 10 hub genes, and the results were consistent with the results of the Degree algorithm (Figure 4(c)). The top 10 hub genes contained AKT1, ALB, IL6, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, and EGF. In addition, the Sankey diagram was constructed using the top 10 hub genes and the relative active compounds of DHJSD, of which MOL000098 (Quercetin) targets most hub genes (Figure 5).Figure 4
The PPI network of potential targets of DHJSD in the treatment of osteoporosis. (a) The PPI network from STRING was further analyzed using Cytoscape software (the line between two nodes indicates the interaction. The darker the color of the node, the better the relationship between them). (b) Chord diagram of the corresponding relationship between the top 10 genes and 12 CytoHubba algorithms. (c) The top 10 hub genes were identified by Degree (the darker the color of the node, the greater the degree).
(a)(b)(c)Figure 5
Sankey diagram. The colored vertical bars on the left represent the different herbs contained in DHJSD, the middle bars represent the active ingredients of the herbs, and the right bars represent the target genes. The size of the bars and the thickness of the lines represent the number of interactions.


## 3.5. GO and KEGG Pathway Enrichment Analysis

In order to elucidate the biological mechanisms of DHJSD against osteoporosis, GO and KEGG pathway enrichment analysis was performed by using clusterProfiler in R. The 205 potential targets of DHJSD for treating osteoporosis were input into theR, and a total of 2590 GO terms (adjusted, P<0.05) were obtained, including 2320 biological process (BP) terms and 179 molecular function (MF) terms and 91 cellular component (CC) terms. Based on the adjusted P value from small to large, the top 10 GO-BP terms were mainly enriched in cellular response to chemical stress (GO:0062197), response to metal ion (GO:0010038), response to antibiotic (GO:0046677), response to lipopolysaccharide (GO:0032496), response to alcohol (GO:0097305), response to steroid hormone (GO:0048545), response to molecule of bacterial origin (GO:0002237), response to oxidative stress (GO:0006979), response to reactive oxygen species (GO:0000302), and response to nutrient levels (GO:0031667). The top 10 GO-CC terms were mainly enriched in membrane raft (GO:0045121), membrane microdomain (GO:0098857), membrane region (GO:0098589), transcription regulator complex (GO:0005667), vesicle lumen (GO:0031983), RNA polymerase II transcription regulator complex (GO:0090575), cyclin-dependent protein kinase holoenzyme complex (GO:0000307), serine/threonine protein kinase complex (GO:1902554), mitochondrial outer membrane (GO:0005741), and cytoplasmic vesicle lumen (GO:0060205). The top 10 GO-MF terms were mainly enriched in nuclear receptor activity (GO:0004879), ligand-activated transcription factor activity (GO:0098531), steroid hormone receptor activity (GO:0003707), RNA polymerase II-specific DNA-binding transcription factor binding (GO:0061629), DNA-binding transcription factor binding (GO:0140297), “DNA-binding transcription activator activity, RNA polymerase II-specific” (GO:0001228), DNA-binding transcription activator activity (GO:0001216), phosphatase binding (GO:0019902), heme binding (GO:0020037), and NADP binding (GO:0050661). Finally, the top 10 GO enrichment terms were selected and visualized using bar diagram, as shown in Figure 6. Table 3 shows the top 10 GO enrichment items.Figure 6
Barplot of the top 10 GO enrichment items. The GO enrichment items (BP, CC, MF) are arranged from left to right according to the adjustedP value.Table 3
The top 10 Gene Ontology (GO) enrichment items.
IDDescriptionP valueAdjustP valueGene numberGO itemsGO:0062197Cellular response to chemical stress2.20332E− 369.75E −3346Biological processGO:0010038Response to metal ion1.04408E− 352.31E −3246Biological processGO:0046677Response to antibiotic5.44269E− 348.03E −3143Biological processGO:0032496Response to lipopolysaccharide8.0729E− 348.93E −3143Biological processGO:0097305Response to alcohol1.19658E− 331.06E −3038Biological processGO:0048545Response to steroid hormone2.14139E −331.58E −3045Biological processGO:0002237Response to molecule of bacterial origin4.25534E −332.69E −3043Biological processGO:0006979Response to oxidative stress1.67651E −329.27E −3047Biological processGO:0000302Response to reactive oxygen species2.14509E −321.05E −2937Biological processGO:0031667Response to nutrient levels1.65363E −307.32E −2847Biological processGO:0004879Nuclear receptor activity1.2238E −163.33E −1414Molecular functionGO:0098531Ligand-activated transcription factor activity1.2238E −163.33E −1414Molecular functionGO:0003707Steroid hormone receptor activity1.51056E −122.74E −1012Molecular functionGO:0061629RNA polymerase II-specific DNA-binding transcription factor binding1.0407E −111.42E −0921Molecular functionGO:0140297DNA-binding transcription factor binding2.02886E −112.21E −0923Molecular functionGO:0001228DNA-binding transcription activator activity, RNA polymerase II-specific5.32777E −114.35E −0925Molecular functionGO:0001216DNA-binding transcription activator activity5.59254E −114.35E −0925Molecular functionGO:0019902Phosphatase binding4.84351E −103.29E −0816Molecular functionGO:0020037Heme binding5.8588E −093.54E −0713Molecular functionGO:0050661NADP binding7.53723E −094.10E −079Molecular functionGO:0045121Membrane raft3.10297E −155.63E −1325Cellular componentGO:0098857Membrane microdomain3.33964E −155.63E −1325Cellular componentGO:0098589Membrane region7.90284E −158.88E −1325Cellular componentGO:0005667Transcription regulator complex4.33831E −103.66E −0822Cellular componentGO:0031983Vesicle lumen1.59056E −091.07E −0719Cellular componentGO:0090575RNA polymerase II transcription regulator complex2.17645E −091.22E −0714Cellular componentGO:0000307Cyclin-dependent protein kinase holoenzyme complex1.03807E −085.00E −078Cellular componentGO:1902554Serine/threonine protein kinase complex2.64297E −081.11E −0610Cellular componentGO:0005741Mitochondrial outer membrane4.65534E −081.74E −0613Cellular componentGO:0060205Cytoplasmic vesicle lumen5.57446E −081.88E −0617Cellular componentAdditionally, a total of 170 enriched KEGG pathways (adjusted,P<0.05) were obtained, the top 10 including AGE-RAGE signaling pathway in diabetic complications (hsa04933), Kaposi sarcoma-associated herpesvirus infection (hsa05167), fluid shear stress and atherosclerosis (hsa05418), hepatitis B (hsa05161), prostate cancer (hsa05215), hepatitis C (hsa05160), pancreatic cancer (hsa05212), human cytomegalovirus infection (hsa05163), IL-17 signaling pathway (hsa04657), and TNF signaling pathway (hsa04668). The top 20 KEGG pathway enrichment terms were selected and visualized using bar diagram, as shown in Figure 7. Next, we searched the KEGG pathway database for osteoporosis, looking for potential pathways related to osteoporosis in the enriched 170 pathways. A total of 50 pathways were selected that may be associated with osteoporosis, and then we proceeded to build a network map of target genes and pathways using Cytoscape software (Figure 8). These include 13 of the pathways most strongly associated with osteoporosis, including osteoclast differentiation (hsa04380), AGE-RAGE signaling pathway in diabetic complications (hsa04933), Wnt signaling pathway (hsa04310), MAPK signaling pathway (hsa04010), apoptosis (hsa04210), chemokine signaling pathway (hsa04062), T cell receptor signaling pathway (hsa04660), B cell receptor signaling pathway (hsa04662), PI3K-Akt signaling pathway (hsa04151), JAK-STAT signaling pathway (hsa04630), calcium signaling pathway (hsa04020), NF-kappa B signaling pathway (hsa04064), and TNF signaling pathway (hsa04668). Table 4 shows the enriched 50 possible related pathways of osteoporosis.Figure 7
Barplot of the top 20 KEGG pathway enrichment items. The color of each bar represents the adjustedP value, and the length represents the number of enriched genes.Figure 8
“Target genes-pathways” network. The cyan ellipse represents the potential targets of DHJSD against osteoporosis; the red ellipse represents the top 10 hub genes of DHJSD against osteoporosis; the brown arrow represents potential osteoporosis-related pathways; the light blue arrow represents osteoporosis-related pathways. The line between two nodes represents the interaction; the size of each node represents the number of connections. The lines consistent with the color of each hub gene represent the pathways enriched by that gene.Table 4
The enriched 50 possible related pathways of osteoporosis.
IDDescriptionP valueAdjustP valueGene numberhsa04933AGE-RAGE signaling pathway in diabetic complications3.2722E −318.83495E −2934hsa05418Fluid shear stress and atherosclerosis8.02758E −266.42442E −2434hsa04657IL-17 signaling pathway2.23263E −216.69789E −2026hsa04668TNF signaling pathway1.93321E −205.21968E −1927hsa04659Th17 cell differentiation1.77974E −173.00331E −1624hsa04210Apoptosis5.07009E −178.0525E −1626hsa04218Cellular senescence1.70327E −151.9995E −1426hsa04066HIF-1 signaling pathway4.55414E −154.72929E −1422hsa01521EGFR tyrosine kinase inhibitor resistance1.19998E −141.11722E −1319hsa04115p53 signaling pathway3.86218E −143.36383E −1318hsa05235PD-L1 expression and PD-1 checkpoint pathway in cancer1.25365E −139.95542E −1319hsa04380Osteoclast differentiation1.52063E −131.14047E −1222hsa04620Toll-like receptor signaling pathway2.26415E −131.5283E −1220hsa04660T Cell receptor signaling pathway2.26415E −131.5283E −1220hsa04012ErbB signaling pathway6.6254E −134.25919E −1218hsa04151PI3K-akt signaling pathway1.24292E −127.80436E −1234hsa04370VEGF signaling pathway3.47037E −121.99361E −1115hsa04915Estrogen signaling pathway6.54943E −123.60403E −1121hsa04010MAPK signaling pathway6.67413E −123.60403E −1130hsa04658Th1 and Th2 cell differentiation3.05E −111.55E −1017hsa04068FoxO signaling pathway1.6511E −107.96064E −1019hsa04064NF-kappa B signaling pathway2.32207E −101.09993E −0917hsa04621NOD-like receptor signaling pathway1.2042E −095.16086E −0921hsa04931Insulin resistance3.58386E −091.423E −0816hsa04662B Cell receptor signaling pathway5.33879E −092.08909E −0814hsa04024cAMP signaling pathway5.66891E −092.1641E −0822hsa04910Insulin signaling pathway1.80579E −086.58868E −0817hsa04912GnRH signaling pathway2.87106E −081.03358E −0714hsa04071Sphingolipid signaling pathway1.04592E −073.62048E −0715hsa04014Ras signaling pathway4.64353E −071.475E −0620hsa04920Adipocytokine signaling pathway5.07934E −071.57635E −0611hsa04062Chemokine signaling pathway5.25301E −071.61172E −0618hsa04110Cell cycle1.11163E −063.15936E −0614hsa04630JAK-STAT signaling pathway1.15731E −063.25493E −0616hsa00140Steroid hormone biosynthesis1.30408E −063.59286E −0610hsa04930Type II diabetes mellitus9.82492E −062.50258E −058hsa05323Rheumatoid arthritis1.03843E −052.62033E −0511hsa04928Parathyroid hormone synthesis, secretion and action3.6295E −058.67227E −0511hsa03320PPAR signaling pathway6.67976E −050.0001554779hsa04020Calcium signaling pathway7.36869E −050.00016860615hsa04310Wnt signaling pathway9.54755E −050.00021304513hsa04929GnRH secretion0.0001160740.0002547978hsa04022cGMP-PKG signaling pathway0.0001474230.00032100113hsa04015Rap1 signaling pathway0.000415790.00086356414hsa04666Fc gamma R-mediated phagocytosis0.001959760.0038343138hsa04152AMPK signaling pathway0.0020195540.0039228749hsa01212Fatty acid metabolism0.0020982860.0039896996hsa04923Regulation of lipolysis in adipocytes0.0020982860.0039896996hsa04150mTOR signaling pathway0.0107730830.0184096999hsa04918Thyroid hormone synthesis0.0313619710.0498101895


## 3.6. Molecular Docking Results

According to the results of the Sankey diagram, we carried out molecular docking of the core target protein and active compound involved. Molecular docking between top 10 target proteins (AKT1, ALB, IL6, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, EGF) and key active compounds (Quercetin, Kaempferol, Beta-sitosterol, Wogonin, Beta-carotene, Baicalein, Naringenin, Formononetin, Paeoniflorin, Ginsenoside Rh2, and Epiquinidine) was carried out using AutoDock Vina. The docking scores of the strongest affinity of 10 core target proteins and 11 key active compounds were visualized using the heatmap, as shown in Figure9. The binding energy between target proteins and the active compounds was approximately between −5.8 and −10.9 kcal mol−1. AKT1, ALB, MAPK3, JUN, CASP3, and EGFR have stronger docking energy. The remaining target proteins also have relatively strong docking energy, which means that the compounds in DHJSD bind well to 10 core target proteins. Eventually, we chose the top 4 target protein macromolecules and small compound molecules with the best docking affinity for visualization with Pymol (Figure 10).Figure 9
Heatmap of the docking scores of the active compounds of DHJSD and the target proteins.Figure 10
Molecular docking results of “bioactive compound-hub gene.” (a) Quercetin to AKT1; (b) Beta-sitosterol to AKT1; (c) Beta-carotene to AKT1; (d) Formononetin to AKT1.
(a)(b)(c)(d)


## 4. Discussion

Osteoporosis is a degenerative disease, which increases with age. With the prolongation of human life and the coming of aging society, osteoporosis has become an important health problem [1]. Considering the threat of the aging of the global population and the increasing side effects of clinical drugs, finding potential drugs for the prevention and treatment of osteoporosis from natural products is a novel treatment strategy [2]. DHJSD is an extremely common TCM that has been used in China to treat patients with osteoporosis, especially postmenopausal osteoporosis [2]. However, the mechanism of action of TCM is usually elusive due to the ingredients it contains. It is of great significance to explore the molecular mechanism of TCM by using systematic and normative bioinformatics methods to mine multiple databases to integrate and analyze the information of target proteins and compounds [4]. A systematic network pharmacological method for determining molecular biological networks has been developed, which may be used to discover new therapeutic effects of drugs derived from medicinal plants. In this study, based on systematic network pharmacology, including ADME system assessment, PPI network analysis, GO and KEGG pathway analysis, and molecular docking verification, the active ingredients and potential targets of DHJSD in the treatment of osteoporosis were evaluated.According to the principle of ADME (setting OB ≥30 and DL ≥0.18) [20], we searched the TCMSP database and screened a total of 209 active compounds (after deduplication) from the 15 herbs contained in DHJSD (Rou Gui did not find a compound that meets the requirements). By using the compound-target search function of the TCMSP database, 267 target proteins were finally found from 209 active compounds (30 of which did not find the corresponding target protein). Then, we transformed the full name of the target protein into the gene ID through UniProt database. Through searching 6 disease-target databases (GeneCards, MalaCards, DisGeNET, TTD, CTD, and OMIM), a total of 3131 potential osteoporosis treatment targets were obtained. Finally, we obtained 205 potential target genes of DHJSD in the treatment of osteoporosis. The network of DHJSD against osteoporosis was built, which was involved in 394 nodes and 2265 interactions. Furthermore, PPI network has been constructed involved in 205 nodes and 4078 edges. Top 10 hub genes were revealed by weighing the CytoHubba 12 algorithms of Cytoscape software (Degree, MCC, DMNC, MNC, EPC, Closeness, Betweenness, ClusteringCoefficient, EcCentricity, Radiality, Stress, and BottleNeck), including AKT1, ALB, IL6, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, and EGF.AKT1 is one of three closely related serine/threonine protein kinases (AKT1, AKT2, and AKT3), which regulates many physiological processes including metabolism, proliferation, cell survival, and angiogenesis [39]. A study published in 2012 showed that AKT1 may be a regulator of the differentiation and function of osteoblasts and osteoclasts [40]. IL6 is an important inflammatory factor, and its function involves a variety of inflammation-related disease states, including diabetes mellitus susceptibility and systemic juvenile rheumatoid arthritis [41]. A study published in 2018 showed that the upregulation of IL6 expression is an important factor in promoting the osteogenic differentiation of adipose-derived stem cells [42]. ALB is the most important protein in human plasma, which maintains the body's nutrition and osmotic pressure, and acts as a carrier protein for a variety of endogenous molecules (including hormones, fatty acids, and metabolites) and exogenous drugs [43]. A retrospective study suggested that preoperative ALB levels may help predict complications after osteoporotic vertebral compression fractures [43]. MAPK3 is a member of the MAP kinase family and plays a role in the signal cascade that responds to various extracellular signals to regulate various cellular processes, such as proliferation, differentiation, and cell cycle progression [44]. Studies have shown that MAPK3 promotes the expression of RUNX2, and targeting MAPK3 can affect osteoblast differentiation [44]. VEGFA is a member of the PDGF/VEGF growth factor family, which is active in angiogenesis, angiogenesis, and endothelial cell growth, and induces endothelial cell proliferation, promotes cell migration, and inhibits cell apoptosis [45]. By inhibiting the expression of VEGFA, miR-16-5p could exert an anti-osteogenesis effect [45]. The human JUN gene encodes a protein that is highly similar to the viral protein, which directly interacts with specific target DNA sequences to regulate gene expression [46]. By activating the c-Fos/c-Jun pathway, IL-7/IL-7R could promote RANKL-mediated osteoclast formation and bone resorption and induce bone loss in ovariectomized mice [46]. The protein encoded by the CASP3 gene is a cysteine-aspartic acid protease, which plays a vital role in the execution-phase of cell apoptosis [47]. By targeting CASP3 and activating the PI3K-Akt signaling pathway, the overexpression of miR-378 could attenuate the osteogenic differentiation inhibited by high glucose [47]. The protein encoded by the EGF gene is a member of the epidermal growth factor superfamily, which acts as an effective mitogenic factor and plays an important role in the growth, proliferation, and differentiation of various cell types [48]. EGFR gene encodes a protein that is a receptor for members of the epidermal growth factor family [48]. Studies have shown that inhibition of the EGFR signaling pathway inhibits the expression of the enhancer of zeste homolog 2 (Ezh2) through the ERK1/2 pathway, thereby promoting the senescence of osteoprogenitor cells [48]. MYC is a proto-oncogene and encodes a nuclear phosphoprotein, which plays a role in cell cycle progression, apoptosis, and cell transformation [49]. In a mouse model of osteoporosis, both the loss of MYC and the pharmacological inhibitory effect of ERRα reduced bone loss [49]. The main physiological processes regulated by proteins encoded by the top 10 target genes include inflammatory response, immune response, cell proliferation, differentiation, apoptosis, migration, cell cycle progression, endocrine metabolism, angiogenesis, growth, and nutrition. Based on the above analysis results, it is also speculated that DHJSD against osteoporosis may play a role through the above process.The one-to-one correspondence between the top 10 hub genes and the corresponding active compounds contained in DHJSD had been shown using the Sankey diagram. The relative active compounds include MOL000098 (Quercetin), MOL000422 (Kaempferol), MOL000358 (Beta-sitosterol), MOL000173 (Wogonin), MOL002773 (Beta-carotene), MOL002714 (Baicalein), MOL004328 (Naringenin), MOL000392 (Formononetin), MOL001924 (Paeoniflorin), MOL005344 (Ginsenoside Rh2), and MOL009031 (Epiquinidine). Among them, Quercetin targets most central genes. Quercetin is a member of the flavonoid family isolated from onion, apple, grape, tea, and many kinds of Chinese herbal medicines, which seems to have obvious anti-osteoporosis properties [50]. Studies have shown that Quercetin alone or in combination with alendronate could prevent glucocorticoid-induced osteoporosis through its bone formation stimulation [50]. Quercetin promotes bone marrow mesenchymal stem cells (BMSCs) proliferation and osteogenic differentiation, improves the in vitro model of osteoporosis, and provides protection against TNF-α-induced impairment of BMSC osteogenic function [51].Then, we performed the GO and KEGG pathway enrichment analysis of genes that DHJSD against osteoporosis. Based on the adjustedP value from small to large, the top 3 GO-BP terms were mainly enriched in cellular response to chemical stress, response to metal ion, and response to antibiotic. The top 3 GO-CC terms were mainly enriched in membrane raft, membrane microdomain, and membrane region. The top 3 GO-MF terms were mainly enriched in nuclear receptor activity, ligand-activated transcription factor activity, and steroid hormone receptor activity. Based on the adjusted P value from small to large, the top 10 KEGG pathways were mainly enriched in AGE-RAGE signaling pathway in diabetic complications, Kaposi sarcoma-associated herpesvirus infection, fluid shear stress, and atherosclerosis, hepatitis B, prostate cancer, hepatitis C, pancreatic cancer, human cytomegalovirus infection, and IL-17 signaling pathway. We searched for osteoporosis in the KEGG pathway database and found that there are ten pathways directly related to osteoporosis and related diseases, including osteoclast differentiation (hsa04380), AGE-RAGE signaling pathway in diabetic complications (hsa04933), Wnt signaling pathway (hsa04310), MAPK signaling pathway (hsa04010), apoptosis (hsa04210), chemokine signaling pathway (hsa04062), T cell receptor signaling pathway (hsa04660), B cell receptor signaling pathway (hsa04662), endocrine and other factor-regulated calcium reabsorption (hsa04961), and mineral absorption (hsa04978). The first eight pathways are in the list of KEGG pathway enrichment analysis we have done. Six of the top 10 hub genes (AKT1, MAPK3, JUN, CASP3, IL6, and VEGFA) are enriched in AGE-AGE signaling pathway in diabetic complications, three of the top 10 hub genes (AKT1, MAPK3, and JUN) are enriched in osteoclast differentiation, eight of the top 10 hub genes (AKT1, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, and EGF) are enriched in MAPK signaling pathway, two of the top 10 hub genes (JUN, MYC) are enriched in Wnt signaling pathway, four of the top 10 hub genes (AKT1, MAPK3, JUN, and CASP3) are enriched in Apoptosis, two of the top 10 hub genes (AKT1, MAPK3) are enriched in Chemokine signaling pathway, and three of the top 10 hub genes (AKT1, MAPK3, and JUN) are enriched in T cell receptor signaling pathway and B cell receptor signaling pathway.In addition, the enriched pathways related to the above pathways in the KEGG database include PI3K-Akt signaling pathway (hsa04151; enriched genes: AKT1, IL6, MAPK3, VEGFA, EGFR, MYC, EGF), JAK-STAT signaling pathway (hsa04630; enriched genes: AKT1, IL6, EGFR, MYC, EGF), calcium signaling pathway (hsa04020; enriched genes: EGFR, EGF), TNF signaling pathway (hsa04668; enriched genes: AKT1, IL6, MAPK3, JUN, and CASP3), and NF-kappa B signaling pathway (hsa04064). Li et al. [52] found that, through the PI3K-Akt signaling pathway, the knockdown of LNC_000052 could promote BMSCs osteogenesis, proliferation, and migration and inhibit cell apoptosis. Studies have shown that miR-10b could promote osteogenic differentiation and increase bone formation through the TGF-β signaling pathway [53]. In addition, JAK-STAT signaling pathway plays a role in the differentiation of osteoblasts and osteoclasts [54]. Jin et al. [55] found that sclareol prevents bone loss caused by ovariectomy in vivo by inhibiting NF-κB and MAPK/ERK signaling pathways and inhibits osteoclast production in vitro. Zha et al. [56] found that miR-920 targets HOXA7 through the MAPK signaling pathway to promote the osteogenic differentiation of human bone mesenchymal stem cells. It is well known that there are many pathways that have a large or small relationship with osteoporosis, and these pathways are classified mainly as bone metabolism, inflammatory response, immune response, endocrine system, and cell apoptosis [57].By mining the database, we obtained the compounds contained in each herb in DHJSD, and then we predicted the potential targets of DHJSD by these compounds, and then we performed KEGG pathway enrichment analysis to obtain the potential pathways of action of DHJSD based on these targets. Therefore, the above obtained targets and signaling pathways should be relevant to the functions of the herbs in DHJSD. It has been shown that the therapeutic effect of the herb Du Huo in osteoporotic rats is associated with the activation of Wnt/β-catenin signaling pathway to promote bone formation [58]. Qin Jiao extract showed a better inhibitory effect on adjuvant arthritis rats, which may be related to the inhibition of JAK2/STAT3 signaling pathway [59]. Fu Ling significantly ameliorated renal injury in db/db mice, and the mechanism may be related to the inhibition of p38 MAPK phosphorylation and the activation of PPARγ pathway [60]. These results are consistent with the functional annotations of Du Huo (strengthening bones and reducing back and knee pain), Qin Jiao (dispelling rheumatism), and Fu Ling (strengthening the spleen and benefiting the kidney) in TCM theory. The number of studies on the targets and pathways of other herbs for the treatment of osteoporosis is currently very limited, and this becomes our next research direction. By using modern pharmacological methods to explore the specific mechanisms by which DHJSD exerts its therapeutic effects, it is more beneficial to understand and complement TCM theories, and thus to uncover the essence of TCM and find alternative therapies for diseases.


## 5. Conclusion

By using the network pharmacology approach, we have studied the potential targets of DHJSD and the underlying mechanism of its anti-osteoporosis effect, which has the characteristics of multi-component, multi-target, and multi-pathway. AKT1, ALB, IL6, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, and EGF may be potential targets of DHJSD in treating osteoporosis. According to the results of enrichment analysis of KEGG pathway, we found pathways closely related to the pathological process of osteoporosis, mainly including AGE-RAGE signaling pathway in diabetic complications, osteoclast differentiation, MAPK signaling pathway, Wnt signaling pathway, PI3K-Akt signaling pathway, JAK-STAT signaling pathway, and TNF signaling pathway. Therefore, this study reveals that the anti-osteoporosis effect of DHJSD may be based on its direct or indirect regulation of the above-mentioned potential targets and pathways. DHJSD may provide a promising direction for future research, so enough relevant experimental research verification is needed, which is very important for revealing its exact regulatory mechanism.



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*Source: 5510290-2021-04-07.xml*