Hindawi Journal of Healthcare Engineering Volume 2021, Article ID 6402206, 11 pages https://doi.org/10.1155/2021/6402206

Research Article Autophagy-Related in Atherosclerosis

Yuankun Chen ,1,2 Ao Zeng ,1 Shumiao He ,1,3 Siqing He ,1 Chunmei Li ,1,2 Wenjie Mei ,3 and Qun Lu 1,2,3

1School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou, China 2Guangdong Province Key Laboratory of Pharmaceutical Bioactive Substances, Guangdong Pharmaceutical University, Guangzhou, China 3Guangdong Province Engineering and Technology Center for Molecular Probe and Bio-medicine Imaging, Guangzhou, China

Correspondence should be addressed to Qun Lu; [email protected]

Received 7 May 2021; Revised 2 June 2021; Accepted 22 June 2021; Published 2 July 2021

Academic Editor: Hasse`ne Gritli

Copyright © 2021 Yuankun Chen et al. +is 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. Background. Atherosclerosis (AS) is a common chronic vascular inflammatory disease and one of the main causes of cardio- vascular/cerebrovascular diseases (CVDs). Autophagy-related genes (ARGs) play a crucial part in pathophysiological processes of AS. However, the expression profile of ARGs has rarely been adopted to explore the relationship between autophagy and AS. +erefore, using the expression profile of ARGs to explore the relationship between autophagy and AS may provide new insights for the treatment of CVDs. Methods. +e differentially expressed ARGs of the GSE57691 dataset were obtained from the Human Autophagy Database (HADb) and the Expression Omnibus (GEO) database, and the GSE57691 dataset contains 9 aortic atheroma tissues and 10 normal aortic tissues. +e differentially expressed ARGs of the GSE57691 dataset were analyzed by -protein interaction (PPI), analysis (GO), and Kyoto Encyclopedia of Genes and Genomes analysis (KEGG) and were chosen to explore related miRNAs/transcriptional factors. Results. +e GSE57691 dataset had a total of 41 differentially expressed ARGs. +e GO analysis results revealed that ARGs were mainly enriched in autophagy, autophagosome, and protein serine/threonine kinase activity. KEGG analysis results showed that ARGs were mainly enriched in autophagy-animal and longevity regulating signaling pathways. Expressions of ATG5, MAP1LC3B, MAPK3, MAPK8, and RB1CC1 were regarded as focus in the PPI regulatory networks. Furthermore, 11 related miRNAs and 6 related transcription factors were obtained by miRNAs/transcription factor target network analysis. Conclusions. Autophagy and ARGs may play a vital role in regulating the pathophysiology of AS.

1. Introduction Studies have shown that autophagy may be involved in regulating cell survival and death during the occurrence and Autophagy, a capacity of maintaining cellular homeostasis, development of AS [8, 9]. In the early stage of atherosclerotic is a process of damaging cytosolic material and delivering to lesions, autophagy can inhibit the apoptosis of vascular lysosomes for degradation, leading to the turnover of cell endothelial cells and delay the development of atheroscle- material and providing macromolecular precursors [1, 2]. rotic plaques. In the late stage of atherosclerotic lesions, Autophagy deficiency is closely related to multiple diseases, excessive activation of autophagy leads to autophagic death such as cancers, CVDs, and immune disorders [3, 4]. of vascular cells, decreased collagen synthesis, and weak CVDs still remain a prevalent cause of mortality and fibrous caps that cause plaque rupture. Vascular smooth morbidity worldwide, affecting 16.7 millions of individuals muscle cells (VSMCs) play a significant role in atherogen- each year [5]. One of main causes of acute cardiovascular esis. Autophagy in VSMCs protects cells in AS plaque by death is AS. AS is a common chronic inflammatory disease suppressing oxidative stress, inhibiting mitochondrial de- characterized by atherosclerotic plaque and vascular stenosis polarization and degrading damaged substances [10]. Fur- [6, 7]. thermore, successful autophagy inhibits VSMCs senescence, 2 Journal of Healthcare Engineering whereas defective autophagy accelerates atherogenesis. +e combined score >0.4 was considered to be a statistically role of autophagy in AS remains undefined though a series of significant interaction. +en, the PPI regulatory network studies have reported that autophagy is activated in AS. analysis results are loaded into Cytoscape for visualization In the past few years, bioinformatics and microarray [22, 23]. technologies have been widely used to excavate the genetic targets of multiple diseases, which have helped researchers in 2.6. miRNAs/Transcription Factor Networks Construction. identifying the differentially expressed genes and potentially +e miRNAs/transcription factor was analyzed by Web- different signaling pathways. However, few research studies Gestalt (http://www.webgestalt.org/) [24]. +e correlation applied to explore the relationship between autophagy and AS. between the miRNA/transcription factor and ARGs in In this study, we used ARGs to explore the relationship significant clustered modules was analyzed by overrepre- between AS and autophagy. +e differentially expressed sentation enrichment analysis (http://amp.pharm.mssm. ARGs were screened, and the relevant data were processed edu/Enrichr/) [25]. Finally, Cytoscape was used to visual- by GO and KEGG analysis. +e complex interaction between ize the miRNAs/transcription factor network [24]. miRNAs/transcriptional factors and genes was predicted. +is main aim of study is to explore the relationship between 3. Results autophagy and AS. 3.1. Repeatability Verification of Dataset. We used the PCA 2. Methods and Pearson’s correlation test to validate the GSE57691 dataset repeatability. According to the PCA, the intragroup repeat- 2.1. Data Collection. A total of 232 ARGs were gathered ability of the GSE57691 dataset was acceptable. In the control from the HADb (http://www.autophagy.lu/index.html) [11]. group and the AS group, the distances between per sample GEO (http://www.ncbi.nlm.nih.gov/geo) is a high- were close in the dimension of principal component 1. In throughput resource functional genomics database that addition, the distances between the control group and the AS includes chips, microarrays, and gene expression data [12]. group were far (Figure 1(b)). Pearson’s correlation test revealed GSE57691 dataset was obtained from the GEO database, that there was a strong correlation among the samples in the which contains 9 aortic atheroma tissues and 10 normal control group and there was a strong correlation among the aortic tissues. Meanwhile, the probe is converted into a samples in the AS group of the GSE57691 dataset. Moreover, homologous gene symbol using the annotation information sample in the control group and sample in the AS group exist a on the platform. negative correlation (Figure 1(a)).

2.2. Differentially Expressed ARGs. +e limma method in R 3.2. Differentially Expressed ARGs Identified between the AS was used to analyze the differential expression of ARGs Group and Control Group. In the GSE57691 dataset, 41 between the AS group and control group [13]. +e threshold differentially expressed ARGs, including 20 upregulated was the adjusted P value <0.05 and | log2 fold change (FC) | ARGs (TBK1, BID, GNAI3, PTK6, and so on) and 21 >2 [14]. downregulated ARGs (ITGA3, WIPI2, MAPK3, FADD, and so on), were identified in the AS group, as shown in heatmap 2.3. Data Repeatability Test. +e intragroup data repeat- (Figure 2(a)). In addition, the differentially expressed ARGs ability of each group was verified by Pearson’s correlation. R between the control group and AS group are shown in programming language provides operating environment volcano plot (Figure 2(b)). and software for drawing of graphs and statistical analysis [15]. Use heatmaps to visualize correlations between samples 3.3. Functional and Pathway of Differentially Expressed ARGs in the same dataset and using R to draw the heatmap. EnrichmentAnalysis. Based on the GO analysis, we found that Principal component analysis (PCA) is a sample clustering the biological processes of differentially expressed ARGs were method for gene expression, diversity analysis, and rese- markedly enriched in autophagy, process utilizing autophagic quencing [16]. +e sample cluster analysis method is used to mechanism, and macroautophagy. +e cell components of verify the intragroup data repeatability of the dataset. differentially expressed ARGs were markedly enriched in autophagosome, vacuolar membrane, and phagophore as- 2.4. GO and KEGG Online Enrichment Analysis. +e en- sembly site membrane. +e molecular function of differentially richment analysis of GO [17] and KEGG [18] in ARGs were expressed ARGs was markedly enriched in protein serine/ performed through DAVID (version 6.8, Database for threonine kinase activity, MAP kinase activity, and ubiquitin Annotation, Visualization, and Integrated Discovery, protein ligase binding (Figure 3(a)). +e results of the KEGG https://david.ncifcrf.gov/) [19]. +e visual GO and KEGG analysis showed that differentially expressed ARGs were pri- enrichment plots of annotation results were analyzed marily enriched in the autophagy-animal signaling pathway through “digest” and “GO plot” packages in R [20]. and longevity regulating signaling pathway (Figure 3(b)).

2.5. PPI Regulatory Network Construction. Evaluate the in- 3.4. PPI Regulatory Network and Module Analysis, Hub Gene teraction between ARGs through the STRING database Identification and Analysis. +e PPI regulatory network of (version 11.0, https://string-db.org/) [21]. In addition, a differentially expressed ARGs was constructed (Figure 4(a)). ora fHatcr Engineering Healthcare of Journal GSM1386834 GSM1386832 GSM1386836 GSM1386833 GSM1386839 GSM1386835 GSM1386838 GSM1386840 GSM1386837 GSM1386843 GSM1386850 GSM1386846 GSM1386847 GSM1386844 GSM1386849 GSM1386841 GSM1386842 GSM1386848 GSM1386845 −0.79 −0.69 −0.64 −0.44 −0.57 −0.44 −0.51 −0.75 −0.56 −0.03 0.73 0.91 0.78 0.78 0.71 0.78 0.8 0.6

1 GSM1386845 −0.76 −0.62 −0.49 −0.35 −0.64 −0.56 −0.64 −0.81 −0.59 −0.06 0.71 0.73 0.74 0.59 0.81 0.82 0.84 0.91

1 GSM1386848 −0.37 −0.69 −0.65 −0.68 −0.79 −0.64 −0.07 −0.7 −0.6 −0.4 0.66 0.84 0.77 0.69 0.84 0.86 0.84 0.73

1 GSM1386842 −0.82 −0.65 −0.81 −0.77 −0.57 −0.61 −0.79 −0.7 −0.7 0.15 0.74 0.86 0.79 0.68 0.88 0.86 0.82 0.8

1 GSM1386841 −0.85 −0.79 −0.65 −0.46 −0.75 −0.67 −0.58 −0.86 −0.71 0.16 0.63 0.79 0.71 0.55 0.88 0.84 0.81 0.78

1 GSM1386849 −0.63 −0.53 −0.34 −0.43 −0.45 −0.42 −0.55 −0.53 −0.45 −0.21 0.71 0.79 0.83 0.55 0.68 0.69 0.59 0.6

1 GSM1386844 −0.52 −0.52 −0.67 −0.46 −0.23 −0.72 −0.62 −0.56 −0.37 −0.6 0.71 0.83 0.71 0.79 0.77 0.74 0.78 0.7

1 GSM1386847 −0.62 −0.48 −0.59 −0.61 −0.55 −0.61 −0.76 −0.7 −0.7 0.07 0.82 0.71 0.79 0.79 0.86 0.84 0.73 0.71

1 GSM1386846 Figure −0.71 −0.64 −0.59 −0.58 −0.48 −0.42 −0.47 −0.57 −0.54 0.71 0.63 0.74 0.66 0.71 0.78 0.82 0.1 0.7

1 GSM1386850 −0.15 −0.13 −0.05 −0.23 −0.06 −0.23 −0.21 −0.07 −0.06 −0.03 :Continued. 1: −0.1 0.01 0.05 0.07 0.16 0.15 0.1 (a) 0 1 GSM1386843 −0.06 −0.54 −0.62 −0.46 −0.45 −0.71 −0.65 −0.64 −0.59 −0.56 0.78 0.72 0.72 0.62 0.64 0.74 0.69 0.6

1 GSM1386837 −0.67 −0.53 −0.86 −0.82 −0.79 −0.81 −0.75 −0.57 −0.7 0.86 0.76 0.71 0.78 0.05 0.89 0.78 0.6 0.5

1 GSM1386840 −0.47 −0.61 −0.52 −0.55 −0.58 −0.68 −0.64 −0.51 −0.7 0.51 0.43 0.15 0.45 0.74 0.71 0.71 0.6

1 0 GSM1386838 −0.15 −0.42 −0.55 −0.52 −0.42 −0.67 −0.65 −0.56 −0.44 −0.7 0.62 0.58 0.43 0.47 0.92 0.71 0.76 0.69

1 GSM1386835 −0.48 −0.61 −0.45 −0.75 −0.79 −0.69 −0.64 −0.57 −0.1 −0.6 0.76 0.72 0.55 0.54 0.92 0.74 0.86 0.74

1 GSM1386839 −0.23 −0.58 −0.59 −0.37 −0.43 −0.46 −0.61 −0.37 −0.35 −0.44 0.64 0.74 0.59 0.54 0.47 0.45 0.64 0.5

1 GSM1386833 −0.05 −0.59 −0.48 −0.56 −0.34 −0.65 −0.57 −0.49 −0.64 −0.4 0.82 0.59 0.55 0.43 0.15 0.62 0.8 0.6

1 GSM1386836 −0.13 −0.64 −0.62 −0.53 −0.79 −0.77 −0.62 −0.69 −0.7 −0.6 0.92 0.82 0.74 0.72 0.58 0.43 0.78 0.72

1 GSM1386832 −0.71 −0.76 −0.72 −0.63 −0.85 −0.81 −0.76 −0.79 −0.7 0.92 0.64 0.76 0.62 0.51 0.89 0.72 0.01 0.8

1 GSM1386834 −1 −0.8 −0.6 −0.4 –0.2 0 0.2 0.4 0.6 0.8 1 3 4 Journal of Healthcare Engineering

2

Con 0 A PCA2

−2

−4

−5 0510 PCA1

Group A Con

(b)

Figure 1: Intragroup data repeatability of the GSE57691 dataset verified by Pearson’s correlation and PCA analysis. (a) Pearson’s correlation analysis of intragroup data from the GSE57691 dataset. +e color represents the degree of correlation. 0< correlation <1 indicates a positive correlation, and −1< correlation <0 indicates a negative correlation. When the absolute value of a number is large, there exists a strong correlation. (b) PCA analysis of intragroup data from the GSE57691 dataset. In the scatter diagram, PC1 and PC2 represent X-axis and Y- axis, respectively, where each point is a sample. +e distance between the two samples represent the difference in gene expression patterns.

+e PPI regulatory networks contain 35 nodes and 135 and 16 regulatory pairs were identified between down- interacting pairs. +en, the most significant module and hub regulated genes and miRNA. In the transcription factor genes in the network were screened by Cytoscape networks, FREAC2 was predicted to target 3 upregulatory (Figures 4(b) and 4(c)). A total of 10 hub genes with degrees genes (CDKN1B, FOXO1, and GRID2) and 5 down- ≥13 were screened: PTEN, FOXO3, RPS6KB1, MAP1LC3A, regulatory genes (FOXO3, KLHL24, MAPK3, ULK1, and ULK1, RB1CC1, MAPK8, MAPK3, MAP1LC3B, and ATG5. WIPI2). CREB was predicted to target 3 upregulatory genes In addition, subnet modules A and B were selected in the PPI (ATG5, EEF2, and ST13) and 2 downregulatory genes regulatory network. Module A contains 6 upregulated nodes (MAP1LC3A and RAB24). (WDFY3, ATF4, ATG4D, and so on), 10 downregulated nodes (MAP1LC3A, MAPK3, MAPK8, and so on), and 56 interacting pairs. Module B contains 3 upregulated nodes 4. Discussion (HSPAB, RB1CC1, and ATG5), 1 downregulated nodes Mounting evidence indicates that autophagy participates in (MAP1LC3A), and 5 interacting pairs. +e genes in the two maintaining cardiovascular health. Dysfunction of vascular modules are given in Table 1. autophagy is related to the initiation of CVDs [26]. Addi- tionally, ARGs affect AS through regulation of VSMCs 3.5. miRNAs/Transcription Factor Target Networks Analysis. phenotypic switching, lipid metabolism, and other biological 11 miRNAs (miR-181 family had the most targets) and 6 processes [27, 28]. transcription factors (CREB, ATF, FREAC2, ATF3, Gene microarray technology is a new method to explore CREBP1CJUN, and AP1) were predicted, and 92 regulatory new biomarkers of diseases. Huang et al. screened 98 pairs of miRNA and transcription factor networks were differentially expressed genes from AS macrophages constructed (Figure 5). +ere were 13 upregulated genes and through DNA microarray analysis and then identified 16 downregulated genes in the miRNAs/transcription factor KDELR3, CD55, and DYNC2H1 as key genes through a networks. In the miRNA networks, 13 regulatory interac- series of analysis [29]. Another study predicted that miR- tions were found between upregulated genes and miRNA, 126 may be a biomarker of AS through gene microarray Journal of Healthcare Engineering 5

Type 1 Type ATF4 C WDFY3 T PTK6 0.5 TBK1 GNAI3 PRKAB1 BNIP1 0 BID FOXO1 APOL1 −0.5 LAMP2 DAPK2 CCR2 ATG5 −1 RB1CC1 GRID2 HSPA8 ST13 CDKN1B ATG4D FADD FOXO3 MAPK8 ARSB CAPN1 SAR1A ATG14 BIRC6 ULK1 PTEN RAB24 RPS6KB1 WIPI2 ITGA3 MAPK3 PEA15 RAB11A MAP1LC3B MAP1LC3A ATG4A

(a)

10

8

6

−log10 ( P value) 4

2

0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 logFC (b)

Figure 2: Differential expressions of ARGs between the control group and AS group. (a) +e 41 differentially expressed ARGs from the GSE57691 dataset. C indicates the control group and T indicates the AS group. (b) Volcano plot of differentially expressed ARGs. Red indicates high expression genes, green indicates low expression genes, and black indicates that there is no difference in these genes between the AS group and control group. technology, and its overexpression may prevent the oc- In the current study, in order to determine the rela- currence and development of AS. In addition, lncRNA tionship between ARGs and AS, microarray analysis was microarray was also used to study AS. LncRNA-FA2H-2 used to identify differentially expressed ARGs from the AS may improve AS by affecting autophagy and inflammation group and the control group. First, we screened 41 differ- [28]. entially expressed ARGs between the AS group and control 6 Extrinsic component of organelle membrane Cellular response to extracellular stimulus Process utilizing autophagic mechanism Protein serine/threonine activity kinase Cellular response to external stimulus Phagophore assembly site membrane Ubiquitin-like protein binding ligase Extrinsic component of membrane Cellular response to nutrient levels Ubiquitin protein binding ligase Cellular response to starvation Purine ribonucleosidePurine binding Ficolin-1-rich granule lumen Autophagosome membrane Response to nutrient levels Phagophore site assembly Death receptorDeath binding Response tostarvation Response Ficolin-1-rich granule Phosphatase binding Vacuolar membrane MAP kinase activityMAP kinase Selective autophagySelective Chaperone binding Macroautophagy Autophagosome GTPase activity Inclusion body GTP binding Autophagy 0 Figure 1 5 :Continued. 3: (a) 01 52 ora fHatcr Engineering Healthcare of Journal 0

MF CC BP P adjust 0.025 0.020 0.015 0.010 0.005 Journal of Healthcare Engineering 7

Autophagy-animal Longevity regulating pathway Shigellosis Longevity regulating pathway-multiple species Mitophagy-animal Alzheimer disease FoxO signaling pathway Autophagy-other Apoptosis-multiple species AMPK signaling pathway Apoptosis Measles P adjust Human immunodefciency virus 1 infection Human cytomegalovirus infection 0.001 Prostate cancer 0.002 Spinocerebellar ataxia 0.003 Hepatitis B Human papillomavirus infection 0.004 Insulin resistance 0.005 Kaposi sarcoma-associated herpesvirus infection Sphingolipid signaling pathway RIG-I-like receptor signaling pathway Insulin signaling pathway EGFR tyrosine kinase inhibitor resistance ErbB signaling pathway Cellular senescence IL-17 signaling pathway Ferroptosis Protein processing in endoplasmic reticulum PI3K-Akt signaling pathway 0 510 (b)

Figure 3: GO and KEGG enrichment analysis of 41 differentially expressed ARGs. (a) Histogram of GO enrichment. (b) Histogram of KEGG enrichment. group. Based on GO and KEGG analysis, we found that Shen-Yuan-Dan capsule treatment reduces foam cell for- differentially expressed ARGs involve multiple biological mation by activating autophagy via affecting the PI3K/AKT/ processes and signaling pathways, including autophagy, mTORC1 signaling pathway [32]. +e KEGG analysis autophagosome, and autophagy-animal signaling pathway. showed that the autophagy-animal signaling pathway might +en, a total of 10 hub genes (PTEN, FOXO3, RPS6KB1, play a role in AS-related autophagy. +e AMPK signaling MAP1LC3A, ULK1, RB1CC1, MAPK8, MAPK3, pathway and mTOR signaling pathway are two major MAP1LC3B, and ATG5) were identified through the PPI pathways in the autophagy-animal pathway. Wu et al. re- regulatory network. Finally, the miRNAs/transcription ported that paeonol inhibits the excessive proliferation of factor networks were constructed, including 11 miRNAs and VSMCs by inducing AMPK phosphorylation, reducing 6 transcription factors. mTOR phosphorylation, and upregulating autophagy [33]. In the BP analysis, most genes were enriched in auto- A total of 10 hub targets were identified in the PPI phagy. Osonoi et al. found that the degree of autophagy regulatory network, including ATG5, MAP1LC3B, MAPK3, increased in the atherosclerotic smooth muscle cells of MAPK8, and RB1CC1. Previous study reported that ATG5 human and rabbit by transmission electron microscopy [30]. is involved in autophagosomes formation [34]. In addition, In the CC analysis, most genes were enriched in autopha- ursolic acid exerted antiatherosclerosis effects and protected gosome. +e main role of autophagosomes is to transfer human umbilical vein endothelial cells (HUVECs) from ox- organelles and to lysosomes for degradation. Zhang LDL induced cytotoxicity. +e underlying mechanism is et al. reported that CAV-1 controls autophagic flux and the associated with increased SIRT1 expression, reduced acet- formation of autophagosomes by affecting the cellular lo- ylation of lysine residue on Atg5, and enhanced autophagy calization of autophagosomes in lipid rafts, thus influencing [35]. +ese suggest that ATG5 may play an important role in the development of AS [31]. In the MF analysis, protein autophagy associated with AS. MAP1LC3B is indirectly serine/threonine kinase activity enriched the most genes. related to plaque instability, which may prevent athero- AKT, the downstream target of PI3K, is serine/threonine sclerotic plaque instability by promoting basic autophagy protein kinase. PI3K/AKT is one of the main pathways of activity. Meanwhile, Bhairavi Swaminathan et al. found that autophagy regulation. Previous studies have reported that low expression of MAP1LC3B in carotid atherosclerotic 8 Journal of Healthcare Engineering

MAP1LC3A LAMP2 MAP1LC3B HSPA8

MAPK3 GRID2

MAPK8 GNAI3

PEA15 FOXO3

PRKAB1 FOXO1

PTEN FADD

PTK6 EEF2

RAB11A DAPK2

RAB24 CDKN1B

RB1CC1 CCR2

RPS6KB1 CAPN1

ST13 BID

TBK1 ATG5

ULK1 ATG4D

WDFY3 ATG4A

WIPI2 ATF4 ATG14

(a)

MAPK3 FOXO1 ATF4

CDKN1B MAPK8 ATG5

PTEN MAP1LC3A RB1CC1 HSPA8

FOXO3 ATG4D

MAP1LC3B RPS6KB1 WDFY3

WIPI2 LAMP2

ULK1 ATG14 ATG4A

(b) (c)

Figure 4: PPI regulatory network and subnet module analysis of differentially expressed ARGs. +e nodes represent the ARGs, and the lines indicate the interaction of two ARGs. +e size and color of nodes are positively correlated with the degree and closeness centrality. (a) PPI regulatory networks. (b), (c) Subnet module analysis of PPI regulatory networks. Journal of Healthcare Engineering 9

Table 1: Submodule ARGs and degree of PPI regulatory networks. Module A Module B Node Description Degree Node Description Degree WDFY3 Up 8 HSPA8 Up 9 ATG4D Up 8 RB1CC1 Up 15 CDKN1B Up 8 MAP1LC3B Down 19 ATG4A Down 9 ATG5 Up 22 ATF4 Up 9 FOXO1 Up 10 WIPI2 Down 11 LAMP2 Up 11 ATG14 Down 12 PTEN Down 13 FOXO3 Down 13 RPS6KB1 Down 14 MAP1LC3A Down 14 ULK1 Down 14 MAPK8 Down 16 MAPK3 Down 18

MIR-193B MIR-193A

MIR-198 MIR-190 CREB CDKN1B GRID2 ATG5

TBK1 GNAI3 FOXO1 MIR-216 MIR-181D RB1CC1 ST13 WDFY3 CREBP1CJUN ATF3 DAPK2 LAMP2 EEF2 PRKAB1 MIR-486 MIR-181C

SAR1A FOXO3 RAB11A FREAC2 ATF MIR-506 MIR-181B PEA15 MAPK3 ULK1 RAB24RPS6KB1 WIPI2

ITGA3 BIRC6 CAPN1 AP1 MIR-524 MIR-181A KLHL24 PTENMAP1LC3A ATG14

MIR-527 MIR-135B MIR-135A Figure 5: miRNAs/transcription factor target networks. +e red circles indicate the upregulated ARGs, and the green circles indicate the downregulated ARGs. +e violet triangle indicates miRNAs, and the yellow quadrilateral indicates transcription factors. plaques did not induce related autophagy. +is may cause involved in protein synthesis, cell proliferation and migra- dead cells to accumulate at the site of the lesion and sub- tion, differentiation, and cell cycle processes [42]. However, sequently lead to cerebrovascular events [36]. MAPK3 RB1CC1 is rarely reported on CVDs. (ERK1) and MAPK8 (JNK1) are members of the mitogen- Based on the analysis of miRNAs/transcription factor activated protein kinase family (MAPK). MAPK signaling target networks, we found that miR-506 had 7 targeted pathway plays a vital role in the pathogenesis of CVDs [37]. ARGs, which exerted the most obvious target interaction Babaev et al. found that the reduction of JNK1 in macro- among the 10 analyzed miRNAs. In particular, miR-506 phages protects them from apoptosis and increases cell regulates the hub gene RB1CC1, and the highly expressed survival rate, thus accelerating early AS [38]. In addition, RB1CCI in the network is downregulated by miR-506. Zheng et al. reported that protocatechuic acid improved RB1CC1 is essential for the formation of autophagosomes. vulnerable lesions in mice, which may be caused by the In addition, the low expression of CAPN1 in the network upregulation of MERTK to normalize arterial inflammation was upregulated by miR-506. Calpain is involved in in- and inhibit MAPK3/1 in lesional macrophages [39]. flammation and AS [43]. Yin et al. reported that the RB1CC1 is involved in the composition of ULK1-ATG13- downregulation of Calpain-1 and inactivation of Calpain RB1CC1/RB1CC1-ATG101 complex, which is crucial for the might be closely related to the anti-inflammatory and formation of autophagosome [40, 41]. RB1CC1 is also antiatherosclerosis effects of simvastatin [44]. +erefore, the 10 Journal of Healthcare Engineering axes of miR-506-CAPN1 and miR-506-RB1CC1 may be cells via autolysosome destabilization,” Autophagy, vol. 12, closely related to AS and autophagy [45, 46]. pp. 2213–2229, 2016. [4] K. G. Lyamzaev, A. V. Tokarchuk, A. A. Panteleeva, A. Y. Mulkidjanian, V. P. Skulachev, and B. V. Chernyak, 5. Conclusion “Induction of autophagy by depolarization of mitochondria,” In summary, our current study has evaluated the expression Autophagy, vol. 14, pp. 921–924, 2018. [5] D. C. Goff, A. G. Bertoni, H. Kramer et al., “Dyslipidemia of ARGs in AS based on the GEO database and HADb. We prevalence, treatment, and control in the Multi-Ethnic Study found that ARGs were involved in the occurrence and of Atherosclerosis (MESA): gender, ethnicity, and coronary development of AS through multiple biological processes artery calcium,” Circulation, vol. 113, pp. 647–656, 2006. and signaling pathways, such as autophagy, process utilizing [6] P. C. Evans and B. R. Kwak, “Biomechanical factors in car- autophagic mechanism, macroautophagy, and autophagy- diovascular disease,” Cardiovascular Research, vol. 99, animal signaling pathway. In addition, we also found that the pp. 229–231, 2013. axes of miR-506-CAPN1 and miR-506-RB1CC1 may be [7] G. Sirimarco, P. Amarenco, J. Labreuche et al., “Carotid closely related to AS and autophagy. Taken all of these, ARGs atherosclerosis and risk of subsequent coronary event in may play a vital role in AS. outpatients with atherothrombosis,” Stroke, vol. 44, pp. 373–379, 2013. [8] E. Marzetti, A. Csiszar, D. Dutta, G. Balagopal, R. Calvani, and Data Availability C. Leeuwenburgh, “Role of mitochondrial dysfunction and +e data used to support the findings of this study have not altered autophagy in cardiovascular aging and disease: from mechanisms to therapeutics,” American Journal of Physiology- been made available because authors were not given per- Heart and Circulatory Physiology, vol. 305, pp. H459–H476, mission to share the data. 2013. [9] M. Ouimet, H. Ediriweera, M. S. Afonso et al., “microRNA-33 Disclosure regulates macrophage autophagy in atherosclerosis,” Arte- riosclerosis,

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