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. 2023 Jul 28;102(30):e34362. doi: 10.1097/MD.0000000000034362

Explore the mechanism of ursolic acid acting on atherosclerosis through network pharmacological and bioinformatics methods

Nan Huang a, Qichang Xing a, Wencan Li a, Qingzi Yan a, Renzhu Liu a, Xiang Liu a, Zheng Liu a,*
PMCID: PMC10378903  PMID: 37505165

Abstract

To explore the deep mechanisms of ursolic acid (UA) for treating atherosclerosis based on network pharmacology and bioinformatics. UA target genes were derived from traditional Chinese medicine system pharmacology, BATMAN-TCM, and SwissTargetPrediction databases. Atherosclerosis-related genes were derived from genecards, NCBI genes, and OMIM databases. The protein interaction network was constructed through the STRING database, and the hub network was extracted by using the Cytoscape software MCODE app. The enrichment analysis of gene ontology and Kyoto encyclopedia of genes and genomes was performed by the R software clusterProfiler package, and the expression and prognostic value of the hub genes were verified on the data set. Screen the genes for expression and prognosis conclusions, conduct methylation analysis, and ceRNA construction. UA had 145 targets in the treatment of atherosclerosis. The top 7 gene ontology (biological process, molecular function, and cellular component) and pathways related to atherosclerosis were screened out. It is principally involved in biological processes, including response to lipopolysaccharide and regulation of inflammatory response. The main signaling pathways incorporated the TNF signaling pathway and the AGE–RAGE signaling pathway. Androgen receptor (AR) and interleukin-1 beta gene (IL1B) were further screened as core target genes. Methylation analysis demonstrated that the AR methylation level was elevated in the atherosclerotic group. On the contrary, the IL1B methylation level was lower in the atherosclerotic group. The results of the ceRNA analysis indicated that there were 43 targeted miRNAs in AR and 3 miRNAs in IL1B. We speculate that the target genes of UA regulating atherosclerosis are AR and IL1B. The mechanism may be that UA regulates the expression of target genes by regulating the methylation of target genes.

Keywords: atherosclerosis, bioinformatics, network pharmacological, treatment targets, ursolic acid

1. Introduction

Atherosclerosis is a multigene, multifactor, and multistage disease, which is related to immune response, lipid metabolism, and cell apoptosis,[1] mainly leading to high mortality diseases such as coronary heart disease, cerebral infarction, and myocardial infarction.[2,3] It was reported that about 55.2% of cerebral infarction is connected to the activity of atherosclerotic plaque.[4]

The disorder of lipid metabolism leads to the formation of lipid rafts in vascular endothelial cell, lipid rafts lead to an inflammatory reaction, further recruit macrophages, and produces inflammatory factors and chemokines.[5,6] The lipid loading of macrophages further aggravates the local inflammatory reaction to form a specific atherosclerotic microenvironment.[7] Stimulated by inflammatory factors and chemokines, smooth muscle cells in the middle membrane migrate to the inner membrane and differentiate from telescopic type to value-added type.[8] Endothelial cells, macrophages, and smooth muscle cells gradually form plaques during lipid loading and inflammatory reaction.[9] Some smooth muscle cells calcify to form the fibrous cap of the plaque.[10] The fibrous cap falls off leading to the rupture of the whole plaque, hence leading to serious cardiovascular and cerebrovascular diseases, such as myocardial infarction, cerebral infarction, and so on.[11] Consequently, anti-inflammatory and plaque stabilization are important measures to prevent acute events of coronary heart disease.

Ursolic acid (UA) is a type of pentacyclic triterpenoids with antioxidant, anti-inflammatory, and other biological activities.[12] However, poor water solubility, low oral bioavailability, short plasma half-life, and nonspecific distribution of UA in vivo are considered to be obstacles to the therapeutic development of this compound.[13] To our delight, a growing number of studies have reported these works in improving the clinical treatment of UA.[14,15] Therefore, it is still important to reveal the therapeutic value of UA. It was discovered that UA has many biological effects, such as antitumor,[16,17] hypoglycemic,[18] and anti-atherosclerotic.[19] Nevertheless, the mechanism of UA against atherosclerosis is not clear. In this study (Fig. 1), we used network pharmacology and bioinformatics methods to comprehensively evaluate the mechanism of UA in atherosclerosis.

Figure 1.

Figure 1.

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The workflow of the integrative network pharmacological and bioinformatics analyses.

2. Materials and methods

2.1. Collection of UA target genes

The SMILES format of UA was searched in the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The SMILES, the UA target genes were predicted in the SwissTargetPrediction database (http://www.swisstargetprediction.ch/). Other target genes were searched in traditional Chinese medicine system pharmacology (https://www.tcmsp-e.com/) and BATMAN-TCM (http://bionet.ncpsb.org.cn/batman-tcm/index.php) databases. The above target genes were merged and excluded duplicates.

2.2. Identification of atherosclerosis related genes

The atherosclerosis related genes were extracted in the GeneCards database (https://www.genecards.org/), the NCBI gene database (https://www.ncbi.nlm.nih.gov/), and the OMIM database (https://www.omim.org/). The above atherosclerosis-related genes were merged and excluded duplicates.

2.3. Screening of UA target genes in atherosclerosis

The UA target genes and the atherosclerosis related genes were entered into the Venn module in the Hiplot database (https://hiplot.com.cn/basic/venn). The intersection of the two as the target gene of UA in atherosclerosis. The Venn maps of the 2 gene sets were visualized.

2.4. The protein–protein interaction analysis of the UA target genes and extracting the hub network

The UA target genes were input into STRING database (https://cn.string-db.org/), selecting “Homo sapiens.” The protein-protein interaction network was constructed. The “TSV” file was downloaded to extract the hub network. Afterwards, the hub network was extracted through the Molecular Complex Detection (MCODE) app[20] in Cytoscape (v3.8.0) software. The cluster with the highest MCODE score was used as the hub network. The degree value of the hub genes was displayed in the form of histogram through R 4.1.2.

2.5. Gene ontology analysis and KEGG pathway enrichment analysis

The hub genes were executed gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis through clusterprofiler 4.0 package,[21] and the top 10 results were visualized in R 4.1.2 software.

2.6. Kaplan–Meier analysis, expression verification and extraction of core targets

GSE100927,[22] GSE43292,[23] GSE125771,[24] GSE28829,[25] and GSE21545[26] data sets were downloaded from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/) database. To explore the prognostic significance of the hub genes, Kaplan–Meier analysis was utilized using GSE21545 (plaque group) data set. The data sets (GSE100927, GSE43292, GSE125771, and GSE28829) were merged, removed the batch effects and standardized for the difference analysis of the hub genes. The genes consistent with the conclusions of Kaplan–Meier analysis and differential expression analysis were selected as the core targets of UA in atherosclerosis.

2.7. The methylation analysis of the core targets

The human disease methylation database, DiseaseMeth version 2.0 (http://bio-bigdata.hrbmu.edu.cn/diseasemeth/help.html),[27] which is a web that focusing on the aberrant methylomes of human diseases. The methylation levels of the core targets were analyzed on the web and visualized with Hiplot web.

2.8. Immune infiltration analysis

Based on the GEO expression dataset, R package CIBERSORT[28] was employed to compute the abundance of 22 immune cells. In addition, the correlation between core targets and immune cells was calculated.

2.9. ceRNA network construction and visualization

The miRNA targets prediction for the core targets (androgen receptor [AR] and interleukin-1 beta gene [IL1B]) was achieved using miRDB (http://mirdb.org/),[29] miRTarBase (https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2022/php/index.php)[30] and TargetScan (http://www.targetscan.org/vert_80/),[31] and the final target miRNA of the core targets was the intersection of 3 databases.

2.10. Statistical analysis

For comparisons between the 2 groups, a Wilcoxon rank sum test was performed, P < .05 was considered statistically significant. Kaplan–Meier analysis was implemented through “tinyarray” package in R version 4.1.2, log-rank P < .05 was considered statistically significant.

3. Results

3.1. Prediction of UA target genes in atherosclerosis

The chemical structure of UA was obtained from the PubChem database as shown in Figure 2A. Based on the structure, a total of 199 UA target genes were obtained from traditional Chinese medicine system pharmacology, BATMAN, and SwissTargetPrediction databases (Table S1, Supplemental Digital Content, http://links.lww.com/MD/J351). Four thousand seven hundred nineteen atherosclerosis-related genes were retrieved from the GeneCards database, 1068 atherosclerosis-related genes were obtained from the NCBI database, and 185 atherosclerosis-related genes were obtained from the OMIM database. After deletion, 4774 atherosclerosis related genes were screened (Table S2, Supplemental Digital Content, http://links.lww.com/MD/J352). Lastly, a total of 145 potential target genes of UA in atherosclerosis were visualized through the Venn diagram of UA target genes and atherosclerosis related genes (Figure 2B, Table S3, Supplemental Digital Content, http://links.lww.com/MD/J353).

Figure 2.

Figure 2.

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UA target genes in atherosclerosis. (A) The chemical structure of UA. (B) Venn diagram of UA target genes and atherosclerosis related genes. UA = ursolic acid.

3.2. The protein–protein interaction networks analysis of the UA target genes and hub networks extraction

In order to explore the relationship between 145 UA target genes, protein–protein interaction (PPI) networks were evaluated through the STRIING database. PPI network, containing 145 nodes and 1449 edges, was constructed, and the protein interaction was enriched (P < 1.0e−16), with an average local clustering coefficient of .57 (Fig. 3A). Subsequently, the hub networks were extracted in Cytoscape software through the MCODE app. This hub network with 37 nodes and 540 edges had a MCODE score: 30 (Figure 3B and Table 1). Based on the degree value, the genes with high weight in the hub network included JUN, CASP3, TP53, ALB, STAT3, IL6, BCL2L1, MMP9, PTGS2, and CCND1 (Fig. 3C).

Figure 3.

Figure 3.

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PPI network and hub network of the UA. (A) PPI network of the UA. (B) Hub network of the UA (the darker the red, the higher the degree value). (C) Histogram of hub genes of UA in atherosclerosis. PPI = protein–protein interaction, UA = ursolic acid.

Table 1.

Topological analysis of the hub genes of UA in atherosclerotic.

Name Degree MCODE score
JUN 36 18.980
CASP3 36 18.980
TP53 36 18.980
ALB 36 18.980
STAT3 36 18.980
IL6 36 18.980
BCL2L1 35 18.980
MMP9 35 18.980
CCND1 34 20.179
TNF 34 19.918
PTGS2 34 19.776
MAPK3 33 19.776
MAPK8 33 19.117
NFKBIA 32 18.544
IL1B 31 19.977
RELA 31 19.611
CDKN1A 31 19.500
ESR1 30 19.923
CASP8 30 19.556
CREB1 30 18.828
CASP9 29 20.289
MDM2 29 18.803
MMP2 29 18.519
MCL1 28 20.834
PPARG 28 17.793
ICAM1 26 17.118
AR 25 18.174
CDK4 25 18.036
FGF2 24 17.417
PGR 23 18.324
NOS3 23 17.105
FASLG 22 19.000
CASP1 22 18.589
NR3C1 21 17.377
BAX 20 16.732
CDK6 19 16.901
PPARA 18 16.732
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3.3. GO and KEGG enrichment analysis

Go and KEGG enrichment analysis was utilized to analyze the biological function of hub genes. As shown in Figure 4A–C, the enriched GO annotations included response to lipopolysaccharide, response to molecule of bacterial origin, regulation of inflammatory response in the biological process category; nuclear receptor activity, ligand-activated transcription factor activity, drug binding in the molecular function category; and membrane raft, membrane microdomain, membrane region in the cellular component category. Figure 4D and Table 2 indicated that hub genes were markedly enriched in advanced glycation end products (AGE)–receptor of AGE (RAGE) signaling pathway, TNF signaling pathway and apoptosis.

Figure 4.

Figure 4.

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GO and KEGG enrichment analysis. (A–C) Bubble chart of GO (BP, MF, CC) enrichment analysis. (D) Histogram of KEGG enrichment analysis. BP = biological process, GO = gene ontology, KEGG = Kyoto encyclopedia of genes and genomes, MF = molecular function, CC = cellular component.

Table 2.

The top 10 significant enriched KEGG pathways for hub genes.

Description P-value Count Gene ID
Kaposi sarcoma-associated herpesvirus infection 3.16231E−24 20 TP53/STAT3/RELA/PTGS2/MAPK8/MAPK3/NFKBIA/JUN/IL6/ICAM1/FGF2/CREB1/CDKN1A/CDK6/CDK4/CASP9/CASP8/CASP3/CCND1/BAX
Human cytomegalovirus infection 7.45134E−23 20 TP53/TNF/STAT3/RELA/PTGS2/MAPK3/NFKBIA/MDM2/IL6/IL1B/CREB1/CDKN1A/CDK6/CDK4/CASP9/CASP8/CASP3/CCND1/BAX/FASLG
Measles 1.28144E−21 17 TP53/STAT3/RELA/MAPK8/NFKBIA/JUN/IL6/IL1B/CDK6/CDK4/CASP9/CASP8/CASP3/BCL2L1/CCND1/BAX/FASLG
Hepatitis B 1.91422E−20 17 TP53/TNF/STAT3/RELA/MAPK8/MAPK3/NFKBIA/MMP9/JUN/IL6/CREB1/CDKN1A/CASP9/CASP8/CASP3/BAX/FASLG
Epstein-Barr virus infection 2.09412E−20 18 TP53/TNF/STAT3/RELA/MAPK8/NFKBIA/MDM2/JUN/IL6/ICAM1/CDKN1A/CDK6/CDK4/CASP9/CASP8/CASP3/CCND1/BAX
AGE-RAGE signaling pathway in diabetic complications 2.15974E−20 15 TNF/STAT3/RELA/MAPK8/MAPK3/NOS3/MMP2/JUN/IL6/IL1B/ICAM1/CDK4/CASP3/CCND1/BAX
Hepatitis C 5.55195E−19 16 TP53/TNF/STAT3/RELA/MAPK3/PPARA/NFKBIA/CDKN1A/CDK6/CDK4/CASP9/CASP8/CASP3/CCND1/BAX/FASLG
TNF signaling pathway 7.55871E−18 14 TNF/RELA/PTGS2/MAPK8/MAPK3/NFKBIA/MMP9/JUN/IL6/IL1B/ICAM1/CREB1/CASP8/CASP3
Influenza A 1.00128E−16 15 TNF/RELA/MAPK3/NFKBIA/IL6/IL1B/ICAM1/CDK6/CDK4/CASP9/CASP8/CASP3/CASP1/BAX/FASLG
Apoptosis 1.26053E−16 14 TP53/TNF/RELA/MAPK8/MAPK3/NFKBIA/MCL1/JUN/CASP9/CASP8/CASP3/BCL2L1/BAX/FASLG
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3.4. Prognostic value and expression analysis of hub genes

In order to clarify the prognostic value of hub genes, Kaplan–Meier analysis was performed in GSE21545 data sets. 12 hub genes (IL1B, FASLG, CDK4, MCL1, NR3C1, IL6, PPARG, MMP2, CREB1, CASP3, ICAM1, and AR) with prognostic value were identified (Fig. 5A). Among them, the differentially expressed genes were FASLG, IL1B, AR, PPARG, and ICAM1 between normal and atherosclerotic tissues (Fig. 5B). Based on the consistent conclusion of prognostic value and differential expression, we finally screened AR, IL1B, and ICAM1 as the core targets of UA.

Figure 5.

Figure 5.

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Prognostic value and expression analysis of hub genes. (A) Kaplan–Meier curve of hub genes. (B) Box plot of hub genes differential expression (NC: normal control, AS: atherosclerosis).

3.5. Methylation analysis of the core targets

In order to reveal the deep mechanism of UA regulating atherosclerosis, we further evaluated the methylation levels of AR and IL1B. The results showed that the methylation level of AR was significantly increased in atherosclerosis. On the contrary, the methylation level of IL1B was significantly decreased (Fig. 6, Table S4, Supplemental Digital Content, http://links.lww.com/MD/J354). Based on the above results, we speculate that UA may regulate AR and IL1B methylation.

Figure 6.

Figure 6.

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The methylation levels of AR, IL1B, and ICAM1. (A) Heatmap of AR and IL1B methylation levels. (B) Violin plot of AR, IL1B, and ICAM1 methylation levels.

3.6. Diagnostic value of AR and IL1B

To explore the diagnostic value of AR, IL1B, and ICAM1, we employed lasso analysis to construct a regression model based on GEO data. The model fit had the highest effect when variables were taken two (Figure 7A and B). The formula was: Riskscore = 2.40 − 0.668 * ARexpression + 0.447 * IL1Bexpression. The results of difference analysis showed that the arteriosclerosis group had higher Riskscore (Fig. 7C). ROC curve suggests the Riskscore has certain diagnostic value (Fig. 7D).

Figure 7.

Figure 7.

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Diagnostic value of AR and IL1B combination. (A) The box diagram of Riskscore in 2 groups (NC and AS). (B) The ROC curve of Riskscore diagnostic in atherosclerosis. NC = normal control, AS = atherosclerosis.

3.7. Comparison of the immune infiltration between subgroups

According to the median of Riskscore, the AS group was divided into 2 groups (low Riskscore group and high Riskscore group). Immune cell abundance was calculated in the AS group by the Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm. The proportion histogram, heatmap, and correlation of immune cells was shown in Figure 8A–C, respectively. Interestingly, macrophage M0 abundance was significantly increased, while macrophage M2 abundance was significantly decreased in the high Riskscore group (Fig. 8D). In addition, we investigated the correlation between AR, IL1B, and 22 immune cells. We discovered that AR had the largest positive correlation with B cells naive and the largest negative correlation with macrophase M0. The result of IL1B was just the opposite (Fig. 8E–H).

Figure 8.

Figure 8.

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Immune landscape analysis in subgroups. (A) Immune cell proportion histogram. (B) Immune cell infiltration heatmap. (C) Immune cell correlation heatmap. (D) The immune cell infiltration profile between the 2 AS risk groups. (E and F) Scatter plot of correlation between AR and immune cells. (G and H) Scatter plot of correlation between IL1B and immune cells. AR = androgen receptor, AS = atherosclerosis.

3.8. Construction of ceRNA network

In addition, we also explored the target miRNAs of AR and IL1B. By searching the target miRNAs of AR and IL1B in miRDB, miRTarBase, and TargetScan databases and taking the intersection, we obtained 43 target miRNAs of AR and 3 target miRNAs of IL1B (Fig. 9, Table S5, Supplemental Digital Content, http://links.lww.com/MD/J355).

Figure 9.

Figure 9.

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ceRNA network of the core targets.

4. Discussion

Atherosclerosis is a chronic cardiac metabolic disease, which is still the main cause of death all over the world.[32] Because the clinical manifestation is not obvious, it can only be diagnosed after myocardial infarction or stroke, resulting in very high mortality. At present, statins are widely used in the treatment of atherosclerosis, but they do not improve their high incidence rate and mortality.[33,34] This suggests that the current orthodox therapy methods are still inadequate. Therefore, more effective methods need to be developed by researchers and clinicians to deal with the current treatment dilemma. Traditional Chinese medicine have garnered increasing attention because of its low toxicity, effectiveness, and low cost.[35] It is of great clinical value to actively explore the role of natural traditional Chinese medicine in anti-atherosclerosis treatment.

UA is a pentacyclic triterpene compound with molecular formula of C30H48O3 and relative molecular weight of 456.68 Da. It is extracted from the hawthorn, bear fruit, Ligustrum lucidum, Rosa multiflora, Hedyotis diffusa, Plantago asiatica, and other plants.[19] These traditional Chinese medicines are frequently used in China to treat cardiovascular diseases. UA has a wide range of biological effects. Its pharmacological effects such as anti-atherosclerosis and blood lipid reduction have been verified.[36] Although, an increasing number of literatures have reported that UA can resist atherosclerosis through different mechanisms, such as anti-inflammatory,[37] regulation of lipid metabolism,[38] enhancing macrophage autophagy,[39] and inhibition of smooth muscle proliferation,[40] its deep-seated mechanism is still unclear. It is imperative to further explore the mechanism of UA against atherosclerosis.

In our research, we constructed the drug–disease–targets interaction network through the network pharmacology method. PPI and MCODE methods were utilized to build protein interaction networks and extract hub networks. The results showed that JUN, CASP3, TP53, ALB, STAT3, IL-6, BCL2L1, MMP9, PTGS2, and CCND1 were comparatively important genes.

Atherosclerosis is a chronic inflammatory disease of the vessel wall.[41] Its occurrence and development are accompanied by inflammatory response. Human IL-6, which gene has been located on chromosome 7p21, consists of 212 amino acids, including a 28 amino acid signal peptide. It is a cytokine with pleiotropic activity. The expression level of IL-6 increases with the aging of the vascular system in atherosclerosis, which increases the risk of cardiovascular disease.[42] Studies have indicated that IL-6 can form a complex by binding with soluble IL-6R.[43] The complex combined with the IL-6R subunit-β (also known as gp130) on cell membrane can activate JAK and SATA3, and then cause extensive chronic inflammation.[44] This process is called the IL-6 trans-signaling pathway. It wIL-6R subunit-β (also known as gp130, ng) pathway could reduce AS.[45] In addition, IL-6 could promote the maturation of megakaryocytes, resulting in the release of platelets, which is one of the reasons for the formation of emboli in blood vessels.[46] As described above, IL-6 and SATA3 are involved in the occurrence and development of atherosclerosis.

Apoptosis is a form of programmed cell death, which occurs in multicellular organisms to maintain homeostasis. Numerous studies have shown that apoptosis of endothelial cells, vascular smooth muscle cells (VSMCs), and macrophages, which participated in and played essential roles in atherosclerosis, make different roles in atherosclerosis.[47–49] Endothelial cell apoptosis can lead to intimal injury, thereby recruiting monocytes and transforming them into macrophages. Macrophages absorb oxidized low density lipoprotein to form foam cells and induce inflammation, which promotes the development of atherosclerosis. Apoptosis of VSMCs is closely related to the thinning fibrous cap and rupture of atherosclerotic plaque.[11] First, VSMCs apoptosis can lead to atherosclerotic plaque calcification, inflammatory response, and atherosclerotic stenosis. Second, VSMCs apoptosis can induce adjacent VSMCs apoptosis, which aggravates the inflammatory response and promotes atherosclerosis. Macrophage apoptosis plays different roles in each stage of atherosclerosis.[50] One study demonstrated that macrophage apoptosis can inhibit the development of atherosclerosis in early atherosclerosis.[51] Nonetheless, with the weakening of the ability to clear apoptotic cells, macrophage apoptosis promotes the formation of necrotic core in advanced atherosclerosis.[52] This necrotic core releases a large amount of matrix metalloproteinase, which leads to VSMCs apoptosis, thinning the fibrous cap and instability of atherosclerotic plaque. JUN, CASP3, STAT3, BCL2L1, and MMP9 have been proved to be involved in apoptosis signaling pathway.[53–55] In our study, these genes are important targets of UA. It indicates that UA may regulate apoptosis in atherosclerosis.

In order to explore the mechanism, GO and KEGG enrichment analysis was carried out for genes in the hub network. Human cytomegalovirus infection, AGE–RAGE signaling pathway and TNF signaling pathway were markedly enriched.

Studies showed that some foreign peptides from pathogens such as cytomegalovirus had been proposed as atherosclerosis-relevant antigens.[56] Furthermore, Adam et al[57] showed that there were higher antibodies of human cytomegalovirus (HCMV) in the atherosclerosis group. Another study indicated that patients with high HCMV antibody levels had a higher risk of coronary heart disease.[58] These results show that HCMV infection plays an important role in the pathogenesis of atherosclerosis.

An increasing body of evidence shown that AGEs play an important role in cardiovascular diseases.[59,60] AGEs binding to the RAGE, a multiligand transmembrane receptor expressed by endothelial cells, inflammatory cells, VSMCs, and cardiomyocytes, activated AGE/RAGE signaling pathway. The activation of AGE/RAGE signaling pathway leads to the multiple changes in a variety of biological functions, such as mitochondrial dysfunction, oxidative stress, dysregulation of calcium and abnormal cytoskeleton. These changes run through almost all stages of atherosclerosis.[61]

The TNF signaling pathway plays an important role in various physiological and pathological processes, including cell proliferation, differentiation, apoptosis, modulation of immune responses, and induction of inflammation. A study showed that tumor necrosis factor was a multifunctional pro-inflammatory cytokine, with effects on lipid metabolism, coagulation, and endothelial function.[62] TNF alpha presumably activates a variety of signaling pathways, including PI3K/AKT signaling pathway and NF kappaB pathway, and induces endothelial cell injury and inflammation during development of atherosclerosis.[63,64]

Kaplan–Meier analysis shows that 12 hub genes (IL1B, FASLG, CDK4, MCL1, NR3C1, IL6, PPARG, MMP2, CREB1, CASP3, ICAM1, and AR) have prognostic value. These data sets from GEO were further used to verify the differential expression of 12 hub genes. Finally, we gained 2 core genes (IL1B and AR).

The IL1B gene, which is shown to locate at chromosome 2q14, encodes IL-1β protein. IL-1β, one of the two agonists in the IL-1 family, participates in the progression of coronary atherosclerotic heart disease, including local lesion formation, vascular inflammatory reaction, and vulnerable plaque rupture.[65] The protein and mRNA levels of IL-1β in atherosclerosis patients increased significantly compared with normal subjects.[66,67] Studies have shown that IL-1β is an effector of NLRP3 mediated pyroptosis.[68] The increase of IL-1β indicates the activation of pyroptosis signaling pathway, which induces inflammation in endothelial cells and leads to intimal injury. Another research revealed that knockdown of IL-1β significantly reduced proprotein convertase subtilisin/kexin type 9 secretion through mitogen-activated protein kinase signaling pathway,[69] suggesting that IL-1β is involved in the lipid metabolism process. In addition, IL-1β also stimulates the proliferation and differentiation of VSMCs, the activation of macrophages and the secretion of various inflammatory mediators. Moreover, IL-1β induces the generation of matrix metalloproteinase, which is closely related to the rupture of atherosclerotic fibrous cap plaque.[70]

AR, a nuclear transcription factor, plays a role through direct binding with androgen.[71] But so far, the role of AR in atherosclerosis is still contentious. Animal experiments demonstrated that knockout of AR could substantially promote the development of atherosclerosis in mice.[72,73] However, the results of cell experiments practically suggested the opposite. The suppression of AR activity inhibited the synthesis and accumulation of cholesterol in HepG2 cells.[74] At the same time, silencing AR could enhance the proliferation of intima and promoted the proliferation, migration and calcification of smooth muscle cells.[75–77] In addition, a study indicated that the expression of AR in coronary artery of patients with coronary heart disease was significantly reduced compared with the control group.[78] The above complex effects of AR may be related to participating in the regulation of various signaling pathways in vivo, including AR signaling, PI3K/AKT signaling, and mitogen-activated protein kinase signaling.[79,80] Additionally, some scholars have reported that AR plays an anti-AS role by governing ADTRP transcription.[79] Our research revealed that the expression of AR in the atherosclerotic plate was downregulated, and the downregulation of AR was a risk factor for ischemic stroke in atherosclerotic patients. UA may regulate atherosclerosis by targeting AR.

In order to reveal the mechanism of differential expression of IL1B and AR, we analyzed the methylation levels of IL1B and AR in atherosclerosis. The results indicated that the IL1B DNA methylation level was lower in AS, while AR is the opposite. Consequently, we speculate that the differential expression of IL1B and AR may be related to methylation in AS.

Macrophages polarization plays an important role in the occurrence and development of AS. Macrophages M1 were characterized by pro-inflammatory, whereas macrophages M2 were the opposite. Macrophages M1 was enriched in diseased plaques, while more macrophages M2 was enriched in regressed plaques.[81] Further research showed that macrophages M1 were pro-atherogenic and promoted an unstable plaque, while M2 macrophages promoted tissue repair and plaque stability.[82] In our study, we discovered that macrophage M2 enrichment decreased in the high Riskscore group, which was consistent with previous studies.

5. Summary

To sum up, we screened two core targets (IL1B and AR) of UA through network pharmacology and bioinformatics. After consulting the literature, many researchers had proved that IL1B was the regulatory target gene of UA,[37,83,84] which is just consistent with our result. Nevertheless, for the first time, we discovered that AR may be a potential target gene of UA in atherosclerosis. At the same time, IL1B and AR have superior diagnostic and prognostic value. In addition, we speculate that the expression of IL1B and AR has certain value in guiding clinical treatment of patients, and patients with high expression of IL1B and low expression of AR may be more suitable for the treatment of UA.

Acknowledgments

We thank openbiox community and Hiplot team (https://hiplot.com.cn) for providing technical assistance and valuable tools for data analysis and visualization.

Author contributions

Data curation: Wencan Li, Renzhu Liu.

Methodology: Qingzi Yan.

Software: Zheng Liu.

Validation: Zheng Liu.

Visualization: Qichang Xing.

Writing – original draft: Nan Huang.

Writing – review & editing: Xiang Liu, Zheng Liu.

Supplementary Material

medi-102-e34362-s001.xlsx (11.7KB, xlsx)
medi-102-e34362-s002.xlsx (68.7KB, xlsx)
medi-102-e34362-s003.xlsx (10.8KB, xlsx)
medi-102-e34362-s004.xlsx (13.5KB, xlsx)

Abbreviations:

AGEs
advanced glycation end products
AR
androgen receptor
GEO
Gene Expression Omnibus database
GO
gene ontology
HCMV
human cytomegalovirus
IL1B
interleukin-1 beta gene
KEGG
Kyoto encyclopedia of genes and genomes
MCODE
molecular complex detection
PPI
protein–protein interaction
RAGE
receptor of AGE
UA
ursolic acid
VSMCs
vascular smooth muscle cells

Supplemental Digital Content is available for this article.

The datasets generated during and/or analyzed during the current study are publicly available.

The authors have no conflicts of interest to disclose.

This work was supported by Xiang Tan Medical Research Project (2020xtyx-23), Scientific research program of traditional Chinese medicine in Hunan Province (2021169), Hunan Natural Science Foundation Project (No. 2022JJ70127).

How to cite this article: Huang N, Xing Q, Li W, Yan Q, Liu R, Liu X, Liu Z. Explore the mechanism of ursolic acid acting on atherosclerosis through network pharmacological and bioinformatics methods. Medicine 2023;102:30(e34362).

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medi-102-e34362-s001.xlsx (11.7KB, xlsx)
medi-102-e34362-s002.xlsx (68.7KB, xlsx)
medi-102-e34362-s003.xlsx (10.8KB, xlsx)
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