BioMed Research International
Volume 2021 (2021), Article ID 5538643, 14 pages
https://doi.org/10.1155/2021/5538643

Research Article

A Network Pharmacology and Molecular Docking Strategy to Explore Potential Targets and Mechanisms Underlying the Effect of Curcumin on Osteonecrosis of the Femoral Head in Systemic Lupus Erythematosus

Pan Kang,¹ Zhiming Wu,1,2 Yue Zhong,¹ Zihao Wang,³ Chi Zhou,⁴ Shaochuan Huo,² Hai Guo,⁵ Songtao Li,¹ Kun Xu,⁶ Lingyun Liu,⁷ Shuai Chen,¹ Hongyu Tang,⁴ and Haibin Wang⁴

¹Guangzhou University of Chinese Medicine, Guangzhou 510405, China
²Shenzhen Hospital (Futian) of Guangzhou University of Chinese Medicine, No. 6001, North Ring Road, Futian District, Shenzhen City, Guangdong Province, 518048, China
³Queen’s University Belfast, BT71NN, UK
⁴Department of Orthopaedic Surgery, First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
⁵Liuzhou Traditional Chinese Medicine Hospital (Liuzhou Zhuang Medical Hospital), No. 6, Red Gourd, Donghuan Avenue, Chengzhong District, Liuzhou City, Guangxi Province 545000, China
⁶Shi’s Center of Orthopedics and Traumatology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Institute of Traumatology & Orthopedics, Shanghai Academy of Traditional Chinese Medicine, China
⁷College of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou 510006, China

Correspondence should be addressed to Hongyu Tang; yu15116274107@163.com and Haibin Wang; hipman@163.com

Received 18 February 2021; Accepted 6 August 2021; Published 14 September 2021

Academic Editor: Renata Ferreira

Copyright © 2021 Pan Kang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Systemic lupus erythematosus (SLE) is a refractory immune disease, which is often complicated with osteonecrosis of the femoral head (ONFH). Curcumin, the most active ingredient of Curcuma longa with a variety of biological activities, has wide effects on the body system. The study is aimed at exploring the potential therapeutic targets underlying the effect of curcumin on SLE-ONFH by utilizing a network pharmacology approach and molecular docking strategy. Methods. Curcumin and its drug targets were identified using network analysis. First, the Swiss target prediction, GeneCards, and OMIM databases were mined for information relevant to the prediction of curcumin targets and SLE-ONFH-related targets. Second, the curcumin target gene, SLE-ONFH shared gene, and curcumin-SLE-ONFH target gene networks were created in Cytoscape software followed by collecting the candidate targets of each component by R software. Third, the targets and enriched pathways were examined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Eventually, a gene-pathway network was constructed and visualized by Cytoscape software; key potential central targets were verified and checked by molecular docking and literature review. Results. 201 potential targets of curcumin and 170 related targets involved in SLE-ONFH were subjected to network analysis, and the 36 intersection targets indicated the potential targets of curcumin for the treatment of SLE-ONFH. Additionally, for getting more comprehensive and accurate candidate genes, the 36 potential targets were determined to be analyzed by network topology and 285 candidate genes were obtained finally. The top 20 biological processes, cellular components, and molecular functions were identified, when corrected by a value ≤ 0.05. 20 related signaling pathways were identified by KEGG analysis, when corrected according to a Bonferroni value ≤ 0.05. Molecular docking showed that the top three genes (TP53, IL6, VEGFA) have good binding force with curcumin; combined with literature review, some other genes such as TNF, CCND1, CASP3, and MMP9 were also identified. Conclusion. The present study explored the potential targets and signaling pathways of curcumin against SLE-ONFH, which could provide a better understanding of its effects in terms of regulating cell cycle, angiogenesis, immunosuppression, inflammation, and bone destruction.

1. Introduction

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease, which prevalence ranges from 20 to 150 cases per 100,000 population and appears to be increasing as the disease is recognized more readily and survival increases [1]. SLE can cause multiple organ dysfunction such as the lung, kidney, blood vessel, and joint; the diverse clinical manifestations of SLE present a challenge to the clinician [2]. Osteonecrosis (ON) remains a serious complication in SLE with prevalence ranging from 10% to 50%, and the femoral head is the most common site [3–9]. The pathogenesis of SLE-ONFH has not yet been fully elucidated. Except for joint arthroplasty in the late inactive phase, the active phase usually requires effective drugs or other measures for SLE itself, which is more complicated. To date, the drug for SLE is mainly glucocorticoids with immunosuppressive effect, which have achieved good therapeutic effects. However, some side effects have resulted because of their extensive use. A large number of studies have reported that the use of glucocorticoids can cause femoral head necrosis (ONFH) [10–12] and long-term high-dose cyclophosphamide can induce infection, reproductive toxicity, and other adverse reactions [13, 14]. Obviously, none of these seems to be an ideal treatment for SLE-ONFH in the future. Traditional Chinese medicine (TCM) has the characteristics of multitarget, multicomponent, multifunction, and generally low toxicity or side effects [15–18]. It has been widely used in the treatment of various diseases including immune diseases [19, 20] and has attracted more and more attention in recent years. The Chinese medicine arsenic being found to be effective in treating leukemia is one of the best examples [21]. Curcumin, an effective monomeric component mainly extracted from the traditional Chinese medicine turmeric, has been reported to have protective effects on SLE and ONFH [22–25]. However, its potential targets and mechanisms for the treatment of SLE-ONFH are not still very clear, and there is no systematic and comprehensive understanding of the relationship between therapeutic targets and pathways. With the improvement of bioinformatics and related databases, network pharmacology and molecular docking strategies are widely used in drug research, which revealed the interrelationship between drug-target-pathway-disease. In this study, we constructed an interaction network between SLE and ONFH, as well as their interaction network with curcumin’s targets. And molecular docking technology was used to verify the potential targets of curcumin in the treatment of SLE-ONFH. In addition, relevant signal pathways are obtained by bioinformatics analysis to reveal the underlying mechanisms. The entire research workflow is shown in Figure 1.

Figure 1: Workflow of the systematic strategies to elucidate the mechanisms of curcumin in the treatment of SLE-ONFH. ① Acquisition of drugs and disease targets. ② Collection of curcumin targets for the treatment of SLE-ONFH. ③ Analysis of therapeutic target proteins. ④ Topology of analysis of therapeutic targets. ⑤ The construction of curcumin target network, SLE-ONFH target network, and curcumin-SLE-ONFH target protein-protein interaction (PPI) network. ⑥ GO and KEGG enrichment analysis. ⑦ Gene-pathway network analysis. ⑧ Molecular docking.

2. Materials and Methods

2.1. Data Preparation

2.1.1. Chemical Structures

The 2D chemical structure of curcumin (Figure 2) was obtained from PubChem (http://pubchem.ncbi.nlm.nih.gov), which is an open chemical database that provides information on compound structures and descriptive data.

Figure 2: Curcumin chemical structure, CAS number: 458-37-7.

2.1.2. Prediction of Curcumin Targets

The Swiss target prediction database (http://www.swisstargetprediction.ch/) is a web-based tool, on-line since 2014, to perform ligand-based target prediction for any bioactive small molecule [26]. To get curcumin relevant target genes, the “SMILES” format of curcumin was obtained and inputted into the Swiss target prediction database by searching curcumin in the PubChem database. In addition, the HERB database (http://herb.ac.cn/) was used to get curcumin targets for a comprehensive collection of drug targets, which is a high-throughput experiment- and reference-guided database of traditional Chinese medicine [27]. The targets are mainly from TCMID (http://www.megabionet.org/tcmid/) and TCMSP (https://tcmspw.com/tcmsp.php) databases [28, 29]. Finally, each of the predicted targets was inputted into the UniProt database (https://www.uniprot.org/) for screening. In order to increase credibility, we only need human targets which have been annotated and reviewed, and the potential targets of curcumin were identified by eliminating duplicate and nonstandard targets.

2.1.3. Predict Targets of Curcumin against SLE-ONFH

The SLE and ONFH-related targets were identified in the GeneCards database (https://www.genecards.org/) using the phrase “Systemic lupus erythematosus”, “SLE”, “Osteonecrosis of Femoral Head”, and “ONFH” as the keyword. The targets were also supplemented with the OMIM database (https://omim.org/) [30]. A Venn script was installed in the R software 4.0.2 software to get targets of SLE-ONFH, which was the intersection genes of two diseases. To determine the predicted targets of curcumin relevant to SLE-ONFH, the potential targets of curcumin were taken intersection with the relevant targets involved in SLE-ONFH by R software.

2.2. Network Construction

2.2.1. Curcumin Target Network and SLE-ONFH Target Network

The curcumin target network and SLE-ONFH network were constructed and visualized using Cytoscape 3.8.0 software [31–33]; they were uploaded to the String database (https://string-db.org/), which is including direct and indirect interactions between proteins and calculates a confidence score for all protein interactions [34, 35]. The score is proportional to the confidence in the protein interaction, ranged from 0 to 1, and we set the score greater than 0.9 to be high confidence [36, 37].

2.2.2. Curcumin-SLE-ONFH Target PPI Network

Shared targets of curcumin, SLE, and ONFH were obtained by running the Venn script as described above, which was installed in the R 4.0.2 software. They were uploaded to Cytoscape software. The PPI data were obtained from the Database of Interacting Proteins (DIP™), Biological General Repository for Interaction Datasets (Bio GRID), Human Protein Reference Database (HPRD), IntAct Molecular Interaction Database (IntAct), Molecular INTeraction database (MINT), and biomolecular interaction network database (BIND) using the plugin BisoGenet of Cytoscape software [38].

2.3. Topology Analysis

After uploading the common gene above, the nodes with topological importance in the interaction network were screened by calculating Degree Centrality (DC), betweenness centrality (BC), closeness centrality (CC), Eigenvector Centrality (EC), local average connectivity-based method (LAC), and Network Centrality (NC) with the Cytoscape plugin CytoNCA. At the heart of Cytoscape is a network. A simple network diagram consists of nodes and edges. Each node represents a gene. The node-node connection represents the interaction between these nodes. DC believes that the greater the number of neighbors of a node, the greater its impact. CC was used to calculate the importance of nodes. BC is a kind of centrality measure. EC is an indicator in ranking the importance of nodes in the network. The closer the EC of a node is to the network radius, the closer the node is to the network center. LAC is often used to determine the importance of proteins by assessing the relationship between proteins and their neighbors. NC is a method based on the edge clustering coefficient, which considers not only the centrality of nodes but also the relationship between nodes and adjacent nodes [39–41]. These parameters represent the topological importance, and they have been reported about their definitions and computational formulas. They were all used in network pharmacology and systems pharmacology [42].

2.4. Enrichment Analysis

GO analysis with the biological process, cellular component, and molecular function was carried out by R software. The result is obtained by running the script installed on R software including “colorspace,” “stringi,” “ggplot2,” “BiocManager,” “clusterProfiler,” and “enrichplot.” The top 20 items were selected and visualized; a Bonferroni-corrected value ≤ 0.05 was set as the default option. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis method is similar to the GO analysis above.

2.5. Gene-Pathway Network Analysis

The gene-pathway network was constructed based on the significantly enriched pathways with genes that regulated these pathways. The topological analysis of 20 pathways and 285 genes was carried out with BC. The squares represented target genes, and the V-shapes represented pathways in the network.

2.6. Molecular Docking

Molecular docking simulations were used to explore the potential interaction between the top 3 genes of the network above. The SDF format of curcumin was downloaded from the PubChem database; Chem 3D software was used to convert the SDF format into mol2 format file; then, the RESB database was used to get the PDB format structure of the top 3 target proteins. After that, solvent molecules and ligands were removed by Pymol software and saved as pdbqt format. Docking simulations were performed in Autodock1.1.2 software, and Discovery Studio 2020 was used to visually analyze the docking conformation at last [43, 44].

3. Results

3.1. Target Network Analysis

3.1.1. Curcumin Target Network

100, 152, and 11 target genes of curcumin were identified by searching the Swiss, HERB, and STITCH databases, respectively, and 240 genes were selected as curcumin target candidate genes through running R software to remove duplicate genes. In order to ensure credibility, the 240 target genes were inputted into the STRING database; by setting the highest confidence (0.9000) and selecting the condition “Homo sapiens,” 201 targets were finally identified after hiding disconnected nodes in the network. The PPI network of them was saved as “tsv” file and visualized by Cytoscape software according to the degree (Figure 3). This network represented the regulatory relationship of the curcumin targets. In the figure, the nodes represented the target points, and the edges indicated the relationships between the targets. Four important values (Table 1) average shortest path length (ASPL), betweenness centrality (BC), closeness centrality (CC), and clustering coefficient were analyzed; ASPL represented the smallest number of links that a node needed to connect the whole network. In a network, the smaller the ASPL, the more efficient the signal conduction through a node. BC represented the probability that the signal passed through the nodes, and the higher the value of BC, the more important the node. The closeness centrality (CC) and the clustering coefficient could reflect the network tightness, and a tighter network was of higher efficiency [45, 46]. The degree of a node referred to the number of edges between nodes in a network. The greater a node’s degree is, the more nodes are connected to it and the more important it is in the network [47, 48]. We used the value of the degree to analyze the node’s importance in the network and screening the top 30 genes, which is more than twice the median of the target genes at least [49]. It indicated that these targets might be more effective in the treatment of SLE-ONFH.

Figure 3: Curcumin target network. Note: PPI network of targets regulated by curcumin.

Table 1: Topological analysis of curcumin target network.

Name
ASPL
BC
CC
Clustering coefficient
Degree

TP53
2.015625
0.11128227
0.49612403
0.20244898
50

SRC
2.05729167
0.1213118
0.48607595
0.18454106
46

STAT3
2
0.08600855
0.5
0.23809524
43

AKT1
2.078125
0.08456637
0.48120301
0.20557491
42

MAPK3
2.06770833
0.0426935
0.4836272
0.26666667
40

MAPK1
2.05208333
0.07932813
0.48730964
0.25
40

EP300
2.125
0.09219908
0.47058824
0.20769231
40

JUN
2.0625
0.05135675
0.48484848
0.28609987
39

RELA
2.16666667
0.03133128
0.46153846
0.3125
32

VEGFA
2.11979167
0.04222026
0.47174447
0.27513228
28

TNF
2.25
0.03547444
0.44444444
0.29365079
28

FOS
2.1875
0.02564237
0.45714286
0.33903134
27

HSP90AA1
2.13541667
0.04893392
0.46829268
0.2991453
27

MAPK14
2.18229167
0.02577391
0.45823389
0.31054131
27

MYC
2.27604167
0.01608063
0.43935927
0.38666667
25

RB1
2.29166667
0.02207007
0.43636364
0.4
25

CCND1
2.27604167
0.01473079
0.43935927
0.40333333
25

EGFR
2.22916667
0.03631959
0.44859813
0.30434783
24

CDKN1A
2.34375
0.01530251
0.42666667
0.46014493
24

CDK1
2.53645833
0.01897448
0.39425051
0.43478261
23

ESR1
2.19270833
0.05987742
0.45605701
0.37944664
23

JAK2
2.40625
0.01035399
0.41558442
0.29437229
22

IL6
2.40625
0.0188487
0.41558442
0.37662338
22

CASP3
2.44791667
0.03709723
0.40851064
0.1991342
22

CXCL8
2.36458333
0.03987748
0.42290749
0.33766234
22

CASP8
2.40625
0.02257322
0.41558442
0.28095238
21

MMP9
2.48958333
0.0374213
0.40167364
0.29473684
20

CCNA2
2.77083333
0.00477339
0.36090226
0.47894737
20

CDK2
2.57291667
0.00859466
0.38866397
0.52105263
20

PTK2
2.328125
0.0222
0.4295302
0.26315789
19

3.1.2. Targets and Network of SLE-ONFH

After removing the duplication, we obtained 4338 SLE-related targets and 300 ONFH-related targets from the GeneCards and OMIM databases, respectively. In order to improve the comprehensiveness and credibility of the data, these targets were all included in the current study. Finally, 170 intersection genes related to SLE-ONFH were obtained by running the Venn map script installed in R software, and 36 genes related to SLE, ONFH, and curcumin were also received, as shown in Figure 4. The STRING database and Cytoscape software were used to construct and visualize the PPI network of 170 genes (Figure 5); the method is similar to the description in 3.1.1.

Figure 4: Venn diagram of curcumin, SLE, and ONFH intersection targets.

Figure 5: PPI network of SLE-ONFH; the closer to the center of the circle, the darker the color, and the more likely it is to be a potential target.

3.1.3. Curcumin-SLE-ONFH Target Network

According to the network pharmacology analysis method, 36 overlapping genes for curcumin, SLE, and ONFH were defined as potential genes (Table 2) for curcumin against SLE-ONFH. They were imported into the STRING database, and the results were saved as a “tsv” file. Then, the file was imported into Cytoscape software, and the curcumin-SLE-ONFH target network diagram was generated (Figure 6). We found that the number of nodes was 36, the number of edges was 406, the average node degree was 22.55, and the genes with a degree greater than 22.55 were represented by an ellipse in the network; they may be important, among which TP53, VEGFA, IL6, TNF, EGFR, CASP3, MMP9, etc.

Table 2: The potential targets of curcumin-SLE-ONFH (36 intersection genes).

Gene names
Annotation
Degree

TP53
Tumor protein p53
35

VEGFA
Vascular endothelial growth factor A
34

IL6
Interleukin 6
33

TNF
Tumor necrosis factor
32

EGFR
Epidermal growth factor receptor
31

CASP3
Caspase 3
31

ESR1
Estrogen receptor 1
29

MMP9
Matrix metallopeptidase 9
29

CCND1
Cyclin D1
28

TGFB1
Transforming growth factor beta 1
27

IL1B
Interleukin 1 beta
27

CXCL8
C-X-C motif chemokine ligand 8
27

FGF2
Fibroblast growth factor 2
26

SRC
SRC protooncogene, nonreceptor tyrosine kinase
26

MMP2
Matrix metallopeptidase 2
26

SERPINE1
Serpin family E member 1
26

KDR
Kinase insert domain receptor
25

TLR4
Toll like receptor 4
25

PPARG
Peroxisome proliferator-activated receptor gamma
25

PECAM1
Platelet and endothelial cell adhesion molecule 1
25

TIMP1
TIMP metallopeptidase inhibitor 1
24

MMP3
Matrix metallopeptidase 3
24

HIF1A
Hypoxia inducible factor 1 subunit alpha
24

NGF
Histone acetyltransferase GCN5
21

EP300
E1A binding protein p300
19

COL1A1
Collagen type I alpha 1 chain
18

MPO
Myeloperoxidase
17

SOD1
Superoxide dismutase 1
15

NOS2
Nitric oxide synthase 2
15

TGFBR1
Transforming growth factor beta receptor 1
15

ABCB1
ATP binding cassette subfamily B member 1
14

CYP19A1
Cytochrome P450 family 19 subfamily A member 1
11

CYP3A4
Cytochrome P450 family 3 subfamily A member 4
8

TNFRSF10A
TNF receptor superfamily member 10a
8

BAX
BCL2 associated X, apoptosis regulator
6

PCNA
Proliferating cell nuclear antigen
6

Figure 6: PPI network of curcumin-SLE-ONFH; the top three potential genes sorted by degree were TP53, VEGFA, and IL6.

3.2. Identification of Candidate Targets for Curcumin against SLE-ONFH Based on Topology Analysis

To reveal the mechanisms underlying curcumin’s effects on SLE-ONFH and obtain candidate genes more comprehensively and accurately, topological analysis of 36 potential genes was performed through the plugin BisoGenet and CytoNCA of Cytoscape; a network consisting of 3653 nodes and 87728 edges is presented in Figure 7(a). The median degree of all nodes was 28, and the nodes with more than 56 degrees were identified as significant targets according to the previous research [49]. A network of significant targets for curcumin against SLE-ONFH was constructed, and it contained 945 nodes and 39,757 edges (Figure 7(b)). The median values of DC, BC, CC, EC, LAC, and NC were 117, 6,403.125, 0.318309, 0.024411, 19.61376, and 27.984, respectively. The candidate targets were further screened, and 285 targets with were identified (Figure 7(c)).

Figure 7: Topological analysis of 36 potential genes; 285 targets were finally identified.

3.3. GO and KEGG Enrichment Analysis

3.3.1. GO Analysis

GO analysis of 285 candidate targets was analyzed based on biological process (BP), cellular component (CC), and molecular function (MF). 2,364 GO terms were finally enriched, 1,973 in biological process, 200 in cellular component, and 191 in molecular function. The top 20 terms are shown in Figure 8 ( value < 0.05). The results demonstrated many targets involved in multiple BPs associated with mRNA catabolic process, regulation of DNA-binding transcription factor activity, and regulation of cell cycle phase transition, which confirmed the correlation with the gene transcriptional activity in SLE-ONFH. The CC results suggested that most targets are localized to the focal adhesion, cell-substrate junction, and nuclear chromatin. The MF results showed that many targets are associated with ubiquitin protein ligase binding and DNA-binding transcription factor binding.

Figure 8: GO analysis of 285 candidate targets, BP, CC, and MF.

3.3.2. KEGG Pathway Analysis

The pathways that are significantly influenced by curcumin in the process of treating SLE-ONFH were identified by KEGG pathway analysis; the analysis method is similar to the GO analysis by running the corresponding script in R software. The top 20 significant pathways ( value < 0.05) are identified and shown in Figure 9(a), including cell cycle, viral carcinogenesis, hepatitis B, hepatitis C, chronic myeloid leukemia, and human T-cell leukemia virus 1 infection pathways; like the GO analysis above, they were closely related to cell proliferation, differentiation, senescence, and apoptosis. The most significant signaling pathway is the cell cycle pathway, in which the central genes such as TP53 play the pivotal role as shown in Figure 9(b). The data of analysis are shown in Table 3.

(a)

(b)

Figure 9: (a) KEGG analysis of 285 candidate targets; (b) cell cycle signaling pathway. The central gene p53 plays a pivotal role in the pathway.

Table 3: The enrichment pathways corresponding to genes after topology.

Term
Description
value

hsa04110
Cell cycle

hsa05203
Viral carcinogenesis

hsa05161
Hepatitis B

hsa05160
Hepatitis C

hsa05220
Chronic myeloid leukemia

hsa05171
Coronavirus disease—COVID-19

hsa05215
Prostate cancer

hsa05166
Human T-cell leukemia virus 1 infection

hsa05167
Kaposi sarcoma-associated herpesvirus infection

hsa05169
Epstein-Barr virus infection

hsa05163
Human cytomegalovirus infection

hsa04120
Ubiquitin mediated proteolysis

hsa05131
Shigellosis

hsa04210
Apoptosis

hsa04218
Cellular senescence

hsa05205
Proteoglycans in cancer

hsa04919
Thyroid hormone signaling pathway

hsa04010
MAPK signaling pathway

hsa05212
Pancreatic cancer

hsa05170
Human immunodeficiency virus 1 infection

3.4. Gene-Pathway Network Analysis

The gene-pathway network was constructed based on the significantly enriched pathways and genes that regulated these pathways, which are presented in Figure 10. Topological analysis of 20 pathways and 285 candidate genes was carried out with BC. The squares represented target genes, and the V shapes represented pathways in the network. The network diagram suggested that TP53 has the most maximum BC and was the core target gene. Other several genes also had larger BC, such as HSP90AA1, GAPDH, MYC, and RPS27A. They might be the key target genes for the regulatory pathways of curcumin against SLE-ONFH.

Figure 10: Gene-pathway network of SLE-ONFH.

3.5. Molecular Docking

Molecular docking was applied to validate the binding action mode of the top three genes (TP53, VEGFA, and IL6), according to the network analysis. The results revealed that they can interact with curcumin; the combination of curcumin with them is shown in Figure 11. For curcumin and TP53, a ring forms a hydrophobic interaction with ILe232, a hydrogen bond and a hydrophobic interaction with HIS233, and a hydrogen bond with GLY199; the carbonyl group between the a and b rings forms a hydrogen bond with GLU224 and PRO223 (Figure 11(a)). For curcumin and VEGFA, a ring forms a hydrogen bond with GLU93, and the carbonyl group between the a and b rings forms a hydrogen bond with SER95; the b ring forms a hydrophobic interaction with MET94 and a hydrogen bond with ARG82 (Figure 11(b)). For curcumin and IL6, a ring forms a hydrophobic interaction with LEU178 and ARG182 and forms a hydrogen bond with ASP26, the carbonyl group between the a and b rings forms a hydrogen bond with ARG30, and the b ring forms a hydrogen bond with ARG179 (Figure 11(c)). The Affinity (kcal/mol) of curcumin and TP53, VEGFA, and IL6 was -6.5, -5, and -5.9, respectively, which means they can interact well indeed.

(a)

(b)

(c)

Figure 11: (a) Molecular docking between curcumin and TP53 (Affinity, -6.5). (b) Molecular docking between curcumin and VEGFA (Affinity, -5). (c) Molecular docking between curcumin and IL6 (Affinity, -5.9).

4. Discussion

4.1. Role of Network Pharmacology and Molecular Docking Strategy in the Research of Autoimmune Diseases

SLE is an autoimmune disease with multiple organ involvement and multiple antibodies. It is an urgent problem in the field of medicine. High disease activity of SLE is closely related to osteonecrosis especially ONFH, which has become the main cause of disability in SLE patients. In the whole process of disease development, immunity, inflammation, apoptosis, and angiogenesis are important factors [50–54]. Because of the complexity of its treatment, it is more worthwhile to find the appropriate drugs and explore the mechanism(s) than the final operation. Traditional Chinese medicine (TCM) has the characteristics of multitargets and multipathway effect, which may be more effective for complex diseases. As a drug research strategy, network pharmacology has been widely applied in drug research, which gives us a better understanding of the role of drug and their compounds [55]. In addition, molecular docking can help reveal the relationship between the drug and the target. Hence, in the present study, combined with topology analysis and molecular docking strategy, we used network pharmacology to investigate the potential targets involved in curcumin against SLE-ONFH by constructing and analyzing target networks, performing enrichment analysis with targets, and revealing the potential mechanism of curcumin against SLE-ONFH.

4.2. Analysis of Central Genes and Significant Pathways of Curcumin against SLE-ONFH

Curcumin has been found to be useful in the treatment of SLE and ONFH, but the central genes and significant pathways of curcumin against SLE-ONFH are not clear. Through the target interaction network, we found 36 potential targets as described in Results; there were 16 targets (TP53, VEGFA, IL6, TNF, EGFR, CASP3, ESR1, MMP9, CCND1, TGFB1, IL1B, CXCL8, FGF2, SRC, MMP2, and SERPINE1) with connectivity greater than the median degree. The TP53 gene is at the core of the whole network, which means that it is a central target. p53 protein is a tumor suppressor that inhibits the growth of aberrant cells; functional p53 is believed to sense DNA damage and, subsequently, to induce DNA repair, growth arrest, or apoptosis of the aberrant cell [56, 57]. On the one hand, as a cell cycle protein, p53 inhibits the proliferation of bone marrow-derived mesenchymal stem cells (BM-MSCs) of SLE patients. Previous studies have shown that p53 protein is highly expressed in SLE patients compared with the control group, and the activation of the p53 mediated signaling pathway was closely associated with the senescence of BM-MSCs from SLE patients [58]. On the other hand, it is wildly known that the function of BM-MSCs is closely related to the pathology of ONFH [59]. One study shows that p53 and Parkin that coregulate mitophagy in BM-MSCs could promote the repair of early steroid-induced ONFH [60]. Hence, it is at least indicated that p53 and its associated cell cycle signaling pathways are associated with BM-MSCs in SLE-ONFH patients. What is more, as an effective drug for the treatment of SLE and ONFH, curcumin could improve the function of BM-MSCs at the same time and is more likely to play a role by targeting p53 and related cell cycle signal pathway proteins. Of course, it also needs to be further determined by in vivo or in vitro experiments. In the network above, the second central gene is VEGFA. VEGFA is a highly specific vascular endothelial growth factor, which is involved in the occurrence and development of many angiogenesis-dependent diseases. A study found that the serum VEGF level in the active SLE group was significantly higher than that in the control and inactive SLE groups [51]. Moreover, it is reported that SNP rs2010963 of VEGFA is significantly associated with the risk of ONFH [61]. It is worth mentioning that VEGFA is also significantly associated with other autoimmune diseases such as rheumatoid arthritis [62]. IL6 and TNF are important cytokines involved in the immunopathological mechanism of SLE [63, 64]; they mediate the cellular inflammatory process. EGFR, epidermal growth factor receptor, which is Bsr I polymorphism is associated with SLE [65], and it also influences bone formation [66]. The process of ONFH is manifested by the apoptosis of bone cells, CASP3, as an extremely important molecule for apoptosis, which participated in the apoptosis process of bone-related cells [67]. MMP2 and MMP9 are members of the matrix metalloproteinase family. Studies have found that they are susceptibility genes for ONFH. MMP2 is known to be increased in osteoarthritic cartilage while MMP-9 regulates apoptosis of hypertrophic chondrocytes and is a key regulator of growth plate angiogenesis, thus rendering MMP-9 essential for normal bone development and remodeling [68]. ESR-1 and IL1B are crucial metabolism and inflammatory molecules; they are involved in the process of osteoblast-osteoclast metabolism and inflammation [69, 70]. Previous studies have also shown that they are associated with susceptibility to SLE [71–73]. CCND1, a cyclin indispensable for cell proliferation, participates in cell cycle regulation [74]. TGFB1, transforming growth factor-β, is associated with angiogenesis and vascularization and facilitates inflammation and joint damage [75]. CXCL8 is a member of the CXC family of chemokines, which is expressed in the kidneys of SLE patients. A meta-analysis showed that SLE patients have higher circulating IL-8 levels than normal controls and IL-8 levels in SLE patients are influenced by age, region, and disease duration [76]. FGF2 is a possible marker of kidney damage in SLE. SRC, the first identified viral oncogene, encoding tyrosine-protein kinase, has been reported to cause autoinflammatory bone disease [77]. SERPINE1 is also known as the plasminogen activator inhibitor-1; previous studies have demonstrated that decreased fibrinolytic activity due to elevated plasminogen activator inhibitor-1 (PAI-1) levels correlates with ONFH pathogenesis [78].

4.3. Research Value and Bright Prospects for Revealing the Targets and Mechanism of Curcumin against SLE-ONFH

At present, the treatment of SLE depends on the organ involved in the disease. Patients with mild symptoms can use low-dose corticosteroids; however, moderate and severe SLE may require higher doses of corticosteroids or other immunosuppressive agents. The adverse effects and limitations of hormones and immunosuppressive agents make TCM become a candidate therapeutic drug because of their unique advantages. The characteristic of SLE is that the complement system is activated; curcumin could inhibit the complement cascade. Studies have found that curcumin inhibits the increase of matrix protein, glial fibrillary acidic protein, and vimentin in the hippocampus of lupus mice [79]. As mentioned above, we found that curcumin may be able to treat SLE-ONFH by regulating TP53, involving the cell cycle pathway. As we all know, p53 protein plays a very important role in controlling the cell cycle, apoptosis, and DNA repair. There are abnormalities in BM-MSCs in SLE patients. Curcumin could have a positive effect on the treatment of SLE-ONFH by influencing the fate of BM-MSCs in SLE-ONFH patients. This provides an idea for the study of small molecular compounds specifically targeting p53 protein in SLE-ONFH disease. We initially confirmed this through molecular docking strategy. Combined with literature review, we found that curcumin could stabilize p53 by interaction with NAD(P)H: quinone oxidoreductase 1 in tumor-derived cell lines. Above all, according to our analysis, curcumin regulates p53 and influences the cell cycle. It may also regulate VEGFA, IL6, ERK1/2 (MAPK1/MAPK3), AKT1, MYC, CDKN1A, NF-KB, and other genes to effectively against SLE-ONFH because they have an important position in the gene-pathway network; this requires further research in the future, and we will continue to explore their roles in the process in SLE-ONFH.

Data Availability

The data that support the findings of this study are available from the corresponding authors upon request.

Conflicts of Interest

The authors report no conflicts of interest regarding this work.

Authors’ Contributions

Pan Kang worked on the investigation, methodology, writing (original draft), resources, and software. Zhiming Wu performed the investigation and methodology. Haibin Wang and Hongyu Tang performed the funding acquisition, project administration, and writing (review and editing). Yue Zhong, Zihao Wang, and Kun Xu worked on picture and software. Chi Zhou, Shaochuan Huo, Hai Guo, Songtao Li, Lingyun Liu, and Shuai Chen worked on data curation and advice. Pan Kang and Zhiming Wu contributed equally to this work.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (Nos.: 81774339 and 82074462) and project funded by Guangzhou Science and Technology Plan Project (No.: 201707010319). Thanks are due to Mr. Wang Haibin, for the care and help he gave to the first author in the study and work.

References

G. C. Tsokos, “Systemic lupus erythematosus,” The New England Journal of Medicine, vol. 365, no. 22, pp. 2110–2121, 2011.

G. Yaniv, G. Twig, D. B.-A. Shor et al., “A volcanic explosion of autoantibodies in systemic lupus erythematosus: a diversity of 180 different antibodies found in SLE patients,” Autoimmunity Reviews, vol. 14, no. 1, pp. 75–79, 2015.

R. A. Asherson, “Avascular necrosis of bone and antiphospholipid antibodies in systemic lupus erythematosus,” The Journal of Rheumatology, vol. 20, no. 2, pp. 284–288, 1993.

R. Hisada, M. Kato, N. Ohnishi et al., “Antiphospholipid score is a novel risk factor for idiopathic osteonecrosis of the femoral head in patients with systemic lupus erythematosus,” Rheumatology (Oxford), vol. 58, no. 4, pp. 645–649, 2019.

C. C. Mok, C. S. Lau, and R. W. Wong, “Risk factors for avascular bone necrosis in systemic lupus erythematosus,” British Journal of Rheumatology, vol. 37, no. 8, pp. 895–900, 1998.

M. Y. Mok, V. T. Farewell, and D. A. Isenberg, “Risk factors for avascular necrosis of bone in patients with systemic lupus erythematosus: is there a role for antiphospholipid antibodies?” Annals of the Rheumatic Diseases, vol. 59, no. 6, pp. 462–467, 2000.

M. Petri, “Musculoskeletal complications of systemic lupus erythematosus in the Hopkins Lupus Cohort: an update,” Arthritis Care and Research, vol. 8, no. 3, pp. 137–145, 1995.

A. Rascu, K. Manger, H.-G. Kraetsch, J. R. Kalden, and B. Manger, “Osteonecrosis in systemic lupus erythematosus, steroid-induced or a lupus-dependent manifestation?” Lupus, vol. 5, no. 4, pp. 323–327, 1996.

M. Sayarlioglu, N. Yuzbasioglu, M. Inanc et al., “Risk factors for avascular bone necrosis in patients with systemic lupus erythematosus,” Rheumatology International, vol. 32, no. 1, pp. 177–182, 2012.

L. Cui, Q. Zhuang, J. Lin et al., “Multicentric epidemiologic study on six thousand three hundred and ninety five cases of femoral head osteonecrosis in China,” International Orthopaedics, vol. 40, no. 2, pp. 267–276, 2016.

T. Kubo, K. Ueshima, M. Saito, M. Ishida, Y. Arai, and H. Fujiwara, “Clinical and basic research on steroid-induced osteonecrosis of the femoral head in Japan,” Journal of Orthopaedic Science, vol. 21, no. 4, pp. 407–413, 2016.

Y. Sun, C. Q. Zhang, S. B. Chen, J. G. Sheng, D. X. Jin, and B. F. Zeng, “Treatment of femoral head osteonecrosis in patients with systemic lupus erythematosus by free vascularised fibular grafting,” Lupus, vol. 18, no. 12, pp. 1061–1065, 2009.

L. Durcan, T. O'Dwyer, and M. Petri, “Management strategies and future directions for systemic lupus erythematosus in adults,” Lancet, vol. 393, no. 10188, pp. 2332–2343, 2019.

L. Lyons, M. ELBeltagy, G. Bennett, and P. Wigmore, “The effects of cyclophosphamide on hippocampal cell proliferation and spatial working memory in rat,” PLoS One, vol. 6, no. 6, p. e21445, 2011.

S.-H. Liu, W.-C. Chuang, W. Lam, Z. Jiang, and Y.-C. Cheng, “Safety surveillance of traditional Chinese medicine: current and future,” Drug Safety, vol. 38, no. 2, pp. 117–128, 2015.

H. Tang, S. He, X. Zhang et al., “A network pharmacology approach to uncover the pharmacological mechanism of XuanHuSuo powder on osteoarthritis,” Evidence-based Complementary and Alternative Medicine, vol. 2016, Article ID 3246946, 10 pages, 2016.

S. H. Venkatesha, B. M. Berman, and K. D. Moudgil, “Herbal medicinal products target defined biochemical and molecular mediators of inflammatory autoimmune arthritis,” Bioorganic & Medicinal Chemistry, vol. 19, no. 1, pp. 21–29, 2011.

X.-X. Zhao, C. Peng, H. Zhang, and L.-P. Qin, “Sinomenium acutum: a review of chemistry, pharmacology, pharmacokinetics, and clinical use,” Pharmaceutical Biology, vol. 50, no. 8, pp. 1053–1061, 2012.

Y. Tang, Y. Zhang, L. Li, Z. Xie, C. Wen, and L. Huang, “Kunxian capsule for rheumatoid arthritis: inhibition of inflammatory network and reducing adverse reactions through drug matching,” Frontiers in Pharmacology, vol. 11, 2020.

Q. Xing, L. Fu, Z. Yu, and X. Zhou, “Efficacy and safety of integrated traditional Chinese medicine and Western medicine on the treatment of rheumatoid arthritis: a meta-analysis,” Evidence-based Complementary and Alternative Medicine, vol. 2020, Article ID 4348709, 15 pages, 2020.

L. Chen, H.-M. Zhu, Y. Li et al., “Arsenic trioxide replacing or reducing chemotherapy in consolidation therapy for acute promyelocytic leukemia (APL2012 trial),” Proceedings of the National Academy of Sciences of the United States of America, vol. 118, no. 6, 2021.

E. L. Dent, E. B. Taylor, H. R. Turbeville, and M. J. Ryan, “Curcumin attenuates autoimmunity and renal injury in an experimental model of systemic lupus erythematosus,” Physiological Reports, vol. 8, no. 13, p. e14501, 2020.

S. Jin, C. Meng, Y. He et al., “Curcumin prevents osteocyte apoptosis by inhibiting M1-type macrophage polarization in mice model of glucocorticoid-associated osteonecrosis of the femoral head,” Journal of Orthopaedic Research, vol. 38, no. 9, pp. 2020–2030, 2020.

A. Türkoğlu, “Effect of intraperitoneal curcumin instillation on postoperative peritoneal adhesions,” Medical Principles and Practice, vol. 24, no. 2, pp. 153–158, 2015.

J. Zhao, J. Wang, M. Zhou, M. Li, M. Li, and H. Tan, “Curcumin attenuates murine lupus via inhibiting NLRP3 inflammasome,” International Immunopharmacology, vol. 69, pp. 213–216, 2019.

A. Daina, O. Michielin, and V. Zoete, “SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules,” Nucleic Acids Research, vol. 47, no. W1, pp. W357–w364, 2019.

S. S. Fang, L. Dong, L. Liu et al., “HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine,” Nucleic Acids Research, vol. 49, no. D1, pp. D1197–d1206, 2021.

J. Ru, P. Li, J. Wang et al., “TCMSP: a database of systems pharmacology for drug discovery from herbal medicines,” Journal of Cheminformatics, vol. 6, no. 1, 2014.

R. Xue, Z. Fang, M. Zhang, Z. Yi, C. Wen, and T. Shi, “TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis,” Nucleic Acids Research, vol. 41, no. Database issue, pp. D1089–D1095, 2013.

J. S. Amberger and A. Hamosh, “Searching Online Mendelian Inheritance in Man (OMIM): a knowledgebase of human genes and genetic phenotypes,” Current Protocols in Bioinformatics, vol. 58, no. 1, 2017.

S. Wei, X. Zhou, M. Niu et al., “Network pharmacology exploration reveals the bioactive compounds and molecular mechanisms of Li-Ru-Kang against hyperplasia of mammary gland,” Molecular Genetics and Genomics, vol. 294, no. 5, pp. 1159–1171, 2019.

J. Zhao, C. Lv, Q. Wu et al., “Computational systems pharmacology reveals an antiplatelet and neuroprotective mechanism of Deng-Zhan-Xi-Xin injection in the treatment of ischemic stroke,” Pharmacological Research, vol. 147, p. 104365, 2019.

J. Zhou, Q. Wang, Z. Xiang et al., “Network pharmacology analysis of traditional Chinese medicine formula Xiao Ke Yin Shui treating type 2 diabetes mellitus,” Evidence-based Complementary and Alternative Medicine, vol. 2019, Article ID 4202563, 15 pages, 2019.

J. M. Ortiz-Rodriguez, C. Ortega-Ferrusola, M. C. Gil et al., “Transcriptome analysis reveals that fertilization with cryopreserved sperm downregulates genes relevant for early embryo development in the horse,” PLoS One, vol. 14, no. 6, p. e0213420, 2019.

D. Szklarczyk, A. Franceschini, S. Wyder et al., “STRING v10: protein-protein interaction networks, integrated over the tree of life,” Nucleic Acids Research, vol. 43, no. D1, pp. D447–D452, 2015.

J. L. Deng, Y. H. Xu, and G. Wang, “Identification of potential crucial genes and key pathways in breast cancer using bioinformatic analysis,” Frontiers in Genetics, vol. 10, 2019.

D. Szklarczyk, J. H. Morris, H. Cook et al., “The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible,” Nucleic Acids Research, vol. 45, no. D1, pp. D362–d368, 2017.

A. Martin, M. E. Ochagavia, L. C. Rabasa, J. Miranda, J. Fernandez-de-Cossio, and R. Bringas, “BisoGenet: a new tool for gene network building, visualization and analysis,” BMC Bioinformatics, vol. 11, no. 1, p. 91, 2010.

X. Liu, Z. Hong, J. Liu et al., “Computational methods for identifying the critical nodes in biological networks,” Briefings in Bioinformatics, vol. 21, no. 2, pp. 486–497, 2020.

M. Li, J. Wang, X. Chen, H. Wang, and Y. Pan, “A local average connectivity-based method for identifying essential proteins from the network level,” Computational Biology and Chemistry, vol. 35, no. 3, pp. 143–150, 2011.

J. Wang, M. Li, H. Wang, and Y. Pan, “Identification of essential proteins based on edge clustering coefficient,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 4, pp. 1070–1080, 2012.

Y. Tang, M. Li, J. Wang, Y. Pan, and F.-X. Wu, “CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks,” Biosystems, vol. 127, pp. 67–72, 2015.

M. A. Alamri and M. A. Alamri, “Pharmacophore and docking-based sequential virtual screening for the identification of novel Sigma 1 receptor ligands,” Bioinformation, vol. 15, no. 8, pp. 586–595, 2019.

D. Seeliger and B. L. de Groot, “Ligand docking and binding site analysis with PyMOL and Autodock/Vina,” Journal of Computer-Aided Molecular Design, vol. 24, no. 5, pp. 417–422, 2010.

J. Lv, Y.-X. Zhu, Y.-Q. Liu, and X. Xue, “Distinctive pathways characterize A. actinomycetemcomitans and P. gingivalis,” Molecular Biology Reports, vol. 42, no. 2, pp. 441–449, 2015.

G. A. Pavlopoulos, P. I. Kontou, A. Pavlopoulou, C. Bouyioukos, E. Markou, and P. G. Bagos, “Bipartite graphs in systems biology and medicine: a survey of methods and applications,” Gigascience, vol. 7, no. 4, pp. 1–31, 2018.

D. Bu, Z. Su, J. Zou, M. Meng, and C. Wang, “Study of the mechanism underlying therapeutic effect of Compound Longmaining on myocardial infarction using a network pharmacology-based approach,” Biomedicine & Pharmacotherapy, vol. 118, p. 109234, 2019.

J. Y. Zhang, C. L. Hong, H. S. Chen et al., “Target identification of active constituents of Shen Qi Wan to treat kidney yang deficiency using computational target fishing and network pharmacology,” Frontiers in Pharmacology, vol. 10, 2019.

Y. Zhang, Z. Li, M. Yang et al., “Identification of GRB2 and GAB1 coexpression as an unfavorable prognostic factor for hepatocellular carcinoma by a combination of expression profile and network analysis,” PLoS One, vol. 8, no. 12, article e85170, 2013.

P. M. Chen and G. C. Tsokos, “T cell abnormalities in the pathogenesis of systemic lupus erythematosus: an update,” Current Rheumatology Reports, vol. 23, no. 2, 2021.

J. Liu, X. Wang, X. Yang, Q. Yan, S. Wang, and W. Han, “Investigating the role of angiogenesis in systemic lupus erythematosus,” Lupus, vol. 24, no. 6, pp. 621–627, 2015.

T. Liu, M. Son, and B. Diamond, “HMGB1 in systemic lupus erythematosus,” Frontiers in Immunology, vol. 11, 2020.

J.-M. Wang, W.-D. Xu, Z.-C. Yuan, Q. Wu, J. Zhou, and A.-F. Huang, “Serum levels and gene polymorphisms of angiopoietin 2 in systemic lupus erythematosus patients,” Scientific Reports, vol. 11, no. 1, 2021.

B. Yousefi, M. Rastin, M. R. Hatef, J. Shariati, R. Alimohammadi, and M. Mahmoudi, “In vitro modulatory effect of dehydroepiandrosterone sulfate on apoptosis and expression of apoptosis-related genes in patients with systemic lupus erythematosus,” Journal of Cellular Physiology, vol. 234, no. 8, pp. 12676–12684, 2019.

B. Boezio, K. Audouze, P. Ducrot, and O. Taboureau, “Network-based approaches in pharmacology,” Molecular Informatics, vol. 36, no. 10, 2017.

S. Lee, B. Elenbaas, A. Levine, and J. Griffith, “p53 and its 14 kDa C-terminal domain recognize primary DNA damage in the form of insertion/deletion mismatches,” Cell, vol. 81, no. 7, pp. 1013–1020, 1995.

L. J. Ko and C. Prives, “p53: puzzle and paradigm,” Genes & Development, vol. 10, no. 9, pp. 1054–1072, 1996.

Z. Gu, J. Jiang, W. Tan et al., “p53/p21 Pathway Involved in Mediating Cellular Senescence of Bone Marrow- Derived Mesenchymal Stem Cells from Systemic Lupus Erythematosus Patients,” Clinical & Developmental Immunology, vol. 2013, article 134243, pp. 1–13, 2013.

S. Xiang, Z. Li, and X. Weng, “The role of lncRNA RP11-154D6 in steroid-induced osteonecrosis of the femoral head through BMSC regulation,” Journal of Cellular Biochemistry, vol. 120, no. 10, pp. 18435–18445, 2019.

F. Zhang, W. Peng, J. Zhang et al., “P53 and Parkin co-regulate mitophagy in bone marrow mesenchymal stem cells to promote the repair of early steroid-induced osteonecrosis of the femoral head,” Cell Death & Disease, vol. 11, no. 1, 2020.

W. Ma, K. Xin, K. Chen et al., “Relationship of common variants in _VEGFA_ gene with osteonecrosis of the femoral head: A Han Chinese population based association study,” Scientific Reports, vol. 8, no. 1, p. 16221, 2018.

J. Ramírez-Bello, D. Cadena-Sandoval, J. M. Fragoso et al., “The VEGFA -1154G/A polymorphism is associated with reduced risk of rheumatoid arthritis but not with systemic lupus erythematosus in Mexican women,” The Journal of Gene Medicine, vol. 20, no. 6, p. e3024, 2018.

J. N. Stannard and J. M. Kahlenberg, “Cutaneous lupus erythematosus: updates on pathogenesis and associations with systemic lupus,” Current Opinion in Rheumatology, vol. 28, no. 5, pp. 453–459, 2016.

J. Wenzel, “Cutaneous lupus erythematosus: new insights into pathogenesis and therapeutic strategies,” Nature Reviews Rheumatology, vol. 15, no. 9, pp. 519–532, 2019.

C.-M. Huang, C.-H. Tsai, C.-L. Chen, C.-P. Chang, C.-C. Lai, and F.-J. Tsai, “Epidermal growth factor receptor (EGFR) gene Bsr I polymorphism is associated with systemic lupus erythematosus,” Lupus, vol. 13, no. 10, pp. 773–776, 2004.

F. Yang, Z. W. Lin, T. Y. Huang et al., “Ligustilide, a major bioactive component of Angelica sinensis, promotes bone formation via the GPR30/EGFR pathway,” Scientific Reports, vol. 9, no. 1, 2019.

H. Yu, P. Liu, W. Zuo et al., “Decreased angiogenic and increased apoptotic activities of bone microvascular endothelial cells in patients with glucocorticoid-induced osteonecrosis of the femoral head,” BMC Musculoskeletal Disorders, vol. 21, no. 1, p. 277, 2020.

S. Grässel, “Expression profile of matrix metalloproteinase-2 and -9 and their endogenous tissue inhibitors in osteonecrotic femoral heads,” International Journal of Molecular Medicine, vol. 26, no. 1, pp. 127–133, 2010.

A. B. Khalid and S. A. Krum, “Estrogen receptors alpha and beta in bone,” Bone, vol. 87, pp. 130–135, 2016.

Y. K. Jung, Y. M. Kang, and S. Han, “Osteoclasts in the inflammatory arthritis: implications for pathologic osteolysis,” Immune Netw, vol. 19, no. 1, 2019.

M. N. Drehmer, D. Andrade, I. A. Pereira et al., “Estrogen receptor alpha gene (ESR1) polymorphism can contribute to clinical findings in systemic lupus erythematosus patients,” Lupus, vol. 26, no. 3, pp. 294–298, 2017.

G. G. Song, J.-H. Kim, Y. H. Seo, S. J. Choi, J. D. Ji, and Y. H. Lee, “Associations between interleukin 1 polymorphisms and susceptibility to systemic lupus erythematosus: a meta-analysis,” Human Immunology, vol. 75, no. 1, pp. 105–112, 2014.

Y. Rupasree, “Association of estrogen receptor 1 (ESR1) haplotypes with risk for systemic lupus erythematosus among south Indians,” Indian Journal of Experimental Biology, vol. 53, no. 11, pp. 714–718, 2015.

H. W. Yang, M. Chung, T. Kudo, and T. Meyer, “Competing memories of mitogen and p53 signalling control cell-cycle entry,” Nature, vol. 549, no. 7672, pp. 404–408, 2017.

J. Belasco and N. Wei, “Psoriatic arthritis: what is happening at the joint?” Rheumatology and Therapy, vol. 6, no. 3, pp. 305–315, 2019.

Y.-M. Mao, C.-N. Zhao, L.-N. Liu et al., “Increased circulating interleukin-8 levels in systemic lupus erythematosus patients: a meta-analysis,” Biomarkers in Medicine, vol. 12, no. 11, pp. 1291–1302, 2018.

K. Abe, A. Cox, N. Takamatsu et al., “Gain-of-function mutations in a member of the Src family kinases cause autoinflammatory bone disease in mice and humans,” Proceedings of the National Academy of Sciences of the United States of America, vol. 116, no. 24, pp. 11872–11877, 2019.

H. Kim, “Significant associations of PAI-1 genetic polymorphisms with osteonecrosis of the femoral head,” BMC Musculoskeletal Disorders, vol. 12, no. 1, p. 160, 2011.

S. Foxley, M. Zamora, B. Hack et al., “Curcumin aggravates CNS pathology in experimental systemic lupus erythematosus,” Brain Research, vol. 1504, pp. 85–96, 2013.