https://orcid.org/0000-0002-2951-2443
Abstract
As the main pathological basis for the development of cardiovascular and cerebrovascular diseases, atherosclerosis (AS) seriously affects human health. The key targets of biological information analysis of AS can help exploit therapeutic targets. The expression data of early and progressive atherosclerotic tissues were downloaded from the Gene Expression Omnibus (GEO) database. Based on GSE28829 and GSE120521, 74 key genes were obtained through differential expression analysis and weighted correlation network analysis (WGCNA) analysis, which were mainly enriched in the regulating of inflammatory response, chemokine signalling pathway, apoptosis, lipid and AS, Toll-like receptor signalling pathway and so on according to the results of the enrichment analysis. Cytoscape software was applied to screen four pivotal genes (TYROBP, ITGB2, ITGAM and TLR2) based on PPI. The results of the correlation analysis showed that the expression level of pivotal genes was positively related to macrophages M0, and was negatively related to T cells follicular helper. In addition, the expression of ITGB2 was positively related to Tregs. In this study, bioinformatics was applied to screen pivotal genes affecting the progress of AS, which were significantly related to immune-related biological functions and signal pathways of atherosclerotic tissues and the infiltration level of immune cells. Therefore, pivotal genes were expected to become therapeutic targets for AS.
Introduction
Atherosclerosis (AS) is a process of forming atherosclerotic plaques inside of the endarterium accumulated by lipid. With the disease developing, atherosclerotic plaques gradually develop into fibrosis and calcification. There are usually no obvious symptoms in the early stage, while in the advanced stage, plaques gradually erode in the vascular cavity, hindering the normal blood flow in the vessels [Citation1,Citation2], which can cause acute ischaemia of cardio cerebral vascular, then inducing myocardial infarction and cerebral ischaemia, and can lead to severe complications or even death. Statistics reveal that the incidence of AS is extremely high, and advanced AS is the main risk factor resulting in death around the world [Citation3]. In summary, investigating the transformation from early AS to advanced AS can provide the theoretical basis for the treatment of AS.
With the continuous development of high-throughput sequencing technology, many studies have discovered the development of AS, exploring the role of immunity, autophagy, ferroptosis and other biological processes (BPs) on AS [Citation4–6], further promoting its research. Wu et al. [Citation5] confirmed that ferroptosis-related gene HMOX1 can be used as a diagnostic marker for AS, which played an important role in the process of AS. An analysis of bioinformatics revealed the infiltration of immune cells and the expression of immune-related pathways participating in the development of carotid arteriosclerosis, further clarifying the activation of memory CD4 T cells can promote its development [Citation7]. Bioinformatics will play an essential role in the in-depth study of the biological mechanisms of AS.
Weighted correlation network analysis (WGCNA) can analyse the correlation relationship to build a genetic set expressed by the weighted commonly expressed network appraisal, and can analyse the genetic set and phenotype data to discover the potential pivotal genes in the development of the disease [Citation8,Citation9]. Yao et al. explored the genetic signatures and molecular mechanisms shared between systemic lupus erythematosus and pulmonary hypertension based on WGCNA [Citation10]. Studies have used WGCNA to explore the pivotal genes of patients with diabetes combined with coronary heart disease, and found that 19 pivotal genes play an important role in the process of the disease by regulating lysosome and apoptosis [Citation11]. Deshpande et al. utilized Apob(tm2Sgy) Ldlr(tm1Her)J mice to construct a atherosclerotic animal model and conduct high-throughput sequencing for AS tissues, WGCNA was applied to clarify the early expression of autophagy, endoplasmic reticulum stress, inflammation and lipid metabolic related genes in the early stage of AS based on sequencing data [Citation12]. Nowadays, there are no researches on screening pivotal genes for early and advanced AS using WGCNA.
We applied WGCNA and protein–protein interaction (PPI) network based on expression data in this study to screen pivotal genes transformed from early AS to advanced stage, and to explore the correlation between the infiltration of immune cells in atherosclerotic tissues and the pivotal genes, further to discover the possible mechanism of pivotal genes in the progress of AS base on gene set enrichment analysis (GSEA), which aims to provide theoretical basis for the treatment of advanced AS.
Materials and methods
Data collection and pre-processing
Early and later atherosclerotic genetic spectrum data were derived from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database. “atherosclerosis” was set as a retrieval keyword to obtain atherosclerotic expression data (GSE28829 and GSE120521) and platform data (GPL570 and GPL16791). There were 13 early-stage and 16 advanced-stage cases of atherosclerotic plaque expression data in GSE28829. GSE12521 contained four stable and four unstable cases of atherosclerotic plaque expression data. The 13 early-stage and four stable AS were collectively referred to as early AS. The 16 advanced-stage and four unstable AS were collectively referred to as later AS.
Differential expression analysis
The downloaded matrix data obtained through R was standardized. “limma” package was applied to perform differential expression analysis of the standardized spectrum, the screening criteria were set as |log2FC| > 1 and p < .05, so as to screen out differential genes, and then volcano plot and heatmap were drawn based on “gplot2” package and “pheatmap” package, respectively.
Weighted correlation network analysis
Using “WGCNA” package in R to draw the sample tree, so as to check the outbound values, further to build a WGCNA of atherosclerotic expression matrix, the adjacent matrix was transformed into the topology overlapping matrix (TOM). R2 = 0.90 was set as the standard, and Dynamic Tree Cut was utilized to generate modules, set deepSplit = 2, minModuleSize = 50. Finally, we calculated the correlation between different modules and the progress of AS, in which the modules with the strongest relativity were obtained (P <0.05).
Key gene acquisition and enrichment analysis
Custom Venn diagrams were calculated and drawn using online analysis tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) to obtain an intersection of the genes attaining log2FC > 1 in the differential expression analysis and gene sets with a positive relation to AS in WGCNA. Moreover, a Venn diagram was drawn, and gene sets related to AS progress were thereby obtained. Gene ontology (GO) analysis of gene sets and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway analysis were conducted through the “ClusterProfiler” package in R. Enrichment results of key genes in molecular function (MF), cellular components (CC) and BP were obtained by GO enrichment analysis. The signal pathway enrichment results of the pivotal genes were obtained through KEGG enrichment analysis. The screening conditions were set as p value < .05, q value < 0.05.
Construction of PPI of key genes
STRING database (http://string-db.org/) and Cytoscape software were used in the process of drawing the PPI networks of AS related genes. Screen pivotal genes using the “Degree” plugin of Cytoscape software. Gene sets obtained by Venn analysis were inserted into STRING to generate PPI diagram, and the degree plug-in was applied to screen pivotal genes. Moreover, pivotal genes with the strongest interaction in PPI were dug out.
Clarification of the correlation of pivotal genes and the progress of atherosclerosis and its diagnostic value
GEO data were divided into pivotal genes of the high expression group and the low expression group, and a scattered point diagram was drawn. Whether pivotal genes were clearly related with the progress of AS was based on linear correlation analysis. In order to clarify the diagnostic value of pivotal genes, the receiver operating characteristic (ROC) curve was drawn based on GEO data and the area under the curve (AUC) was calculated to evaluate the predictive effectiveness of the pivotal genes.
Gene set enrichment analysis
Biological function enrichment analysis of gene sets was conducted by GO enrichment analysis, and signal pathway enrichment analysis was finished by KEGG enrichment analysis. Gene set enrichment analysis (http://software.broadinstitute.org/gsea/msigdb/index.jsp) was applied to perform GO and KEGG enrichment analysis on pivotal genes of the high and the low expression group, whose functions were derived by analysing the gene sets, and both biological functions and signal pathways enriched in the high and low expression group were obtained.
The correlation between pivotal genes and atherosclerotic tissues immune cell infiltration
The infiltration level of immune genes in AS tissues was analysed by single-sample gene set enrichment analysis (ssGSEA), and the expression of 29 immune-related genes in early atherosclerotic and later atherosclerotic tissues were clarified. The CIBERSORT algorithm can evaluate the infiltration level of immune cells, and clarify the infiltration of early atherosclerotic and later atherosclerotic tissues. Based on correlation analysis, the correlation of the expression of pivotal genes and the infiltration of immune cells can be obtained.
Construction of the atherosclerosis mouse model
The AS mouse model feed with high fat and high cholesterol ApoE–/– mouse (early AS ones were fed with high fat for 12 weeks, later AS ones were fed with high fat for 24 weeks) was constructed. After the construction of the model, the mouse was dissected to obtain atherosclerotic tissues from the aortic root. Some tissues are used for oil red O staining and haematoxylin–eosin (H&E) staining, and others are placed in a −80 °C refrigerator for quantitative real-time polymerase chain reaction (qPCR) and western bolt (WB) experiment.
H&E staining
The atherosclerotic tissues in mouse were treated with 4% buffered paraformaldehyde (Beyotime, Shanghai, China), and then embedded into paraffin. The slides were stained with H&E staining solution (Jiancheng Biotechnology Institute, Nanjing, China), properly dried in a fume hood, and sealed with Neutral balsam (Aladdin, Shanghai, China).
Oil red O staining
The Oil red O staining kit was used to stain the aorta (Jiancheng Biotechnology Institute, Nanjing, China). The frozen sections of aorta were removed from −20 °C, and oil red O dye solution was prepared according to the instructions at the ratio of reserve liquid:diluent = 5:2, and the frozen sections were placed in the Oil red O dye solution. The slices were put into the haematoxylin dye solution for nucleation, and finally sealed with water-based tablet for preservation.
qPCR
Total RNA was extracted and reverse-transcribed into cDNA using reverse transcription kit (Vazyme, Nanjing, China). According to the qPCR kit (Yeason, Shanghai, China) instructions, the reaction system (20 μL) was prepared and qPCR was performed using ABI7500 (Applied Biosystems, Foster City, CA). 2–ΔΔCt was used to calculate the relative expression level of Tyrobp, Itgb2, Itgam and Tlr2 mRNA.
Western blot
The total protein of the tissues was extracted and subjected to gel electrophoresis. Then, the proteins were transferred to PVDF membranes and incubated with 5% skim milk powder. Next, the primary antibodies were incubated, including TYROBP, ITGB2, ITGAM and TLR2, and the secondary antibodies successively. Finally, the ECL solution (Yeason, Shanghai, China) was used for imaging.
Results
Differential expression genes
After preprocessing the original data, with |log2FC| > 1 and p < .05 as the standards, 182 differential genes were obtained from data set GSE28829 (), including 157 up-regulated genes and 25 down-regulated genes (Supplementary Table 1). 1302 differential genes were obtained from the data set GSE120521 (), including 698 up-regulated genes and 604 down-regulated genes (Supplementary Table 2).
Figure 1. Differential expression analysis. (A) Volcanic map for differential expression analysis of GSE28829. (B) Heat map for differential expression analysis of GSE28829. (C) Volcanic map for differential expression analysis of GSE120521. (D) Heat map for differential expression analysis of GSE120521. Blue represents down-regulated genes, red represents up-regulated genes and black represents undifferentiated genes.
WGCNA
In order to identify the key modules in the progress of AS, WGCNA was used to analyse different stages in the progress of AS from the progressive data set (). Based on the average-linkage hierarchical clustering and soft thresholding, seven () and 11 () modules were determined in GSE28829 and GSE120521 data set, respectively. In GSE28829 and GSE120521, the most closely related to the progress of AS were turquoise (r = 0.79, p = 1.2e–77) () and cyan (r = 0.68, p = 1e–200) (), respectively. Among which, 358 genes were contained in the turquoise module (Supplementary Table 3), and 2329 genes were contained in the cyan module (Supplementary Table 4).
Figure 2. Key modules related to atherosclerosis progression were identified in the GEO data set through WGCNA. (A) The dendrogram of all genes in GSE28829 data set based on different metric clustering (1-TOM), in which each branch represents a gene, and the colour of each module represents a co-expression module. (B) The heatmap of the correlation between gene clustering module and the progress of atherosclerosis in GSE28829 data set, each module contains the correlation coefficient and p value. (C) The scatter diagram of the module with the most positive correlation in GSE28829 data set. (D) The dendrogram of all genes in GSE120521 data set based on different metric clustering (1-TOM), in which each branch represents a gene, and the colour of each module represents a co-expression module. (E) The heatmap of the correlation between gene clustering module and the progress of atherosclerosis in GSE120521 data set, each module contains the correlation coefficient and p value. (F) The scatter diagram of the module with the most positive correlation in GSE120521 data set.
GO and KEGG enrichment analysis of differential genes
Take the intersection between the genes with log2FC > 1 of differential expression analysis and those positively related to AS in WGCNA analysis in the two data sets (), and 74 genes related to AS progression were obtained (Supplementary Table 5). Results of GO enrichment analysis showed that these genes were mainly enriched in such biological functions as neutral activation immune response, leucocyte migration, regulation of inflammatory response, lipopeptide binding, chemokine activity and so on ((B)). KEGG enrichment results showed that these genes were mainly enriched in cytokine–cytokine receptor interaction, chemokine signalling pathway, apoptosis, lipid and AS, NF-kappa B signalling pathway, toll-like receptor signalling pathway ().
Figure 3. Screening of pivotal gene sets related with the progress of atherosclerosis. (A) Venn diagram demonstrates the intersection of differentially expressed genes and positively correlated module genes of WGCNA in GSE28829 and GSE120521 data sets. (B) GO enrichment analysis of pivotal genes related to atherosclerosis progression. (C) KEGG enrichment analysis of pivotal genes related to atherosclerosis progression.
Screening of pivotal genes in PPI
PPI of 74 key genes was constructed via STRING database (), on which basis, the top four pivotal genes were further screened through the degree plug-in of the Cytoscape software, which were TYROBP, ITGB2, ITGAM and TLR2 ().
Figure 4. PPI of pivotal genes related to atherosclerosis progression. (A) PPI of 74 pivotal genes. (B) PPI of pivotal genes whose degree score ranks high (the darker the colour is, the higher the ranking is).
Linear regression and ROC curve analysis of pivotal genes
Linear regression analysis showed that the expression level of pivotal genes was positively correlated with the progress of AS (, p < .05). Then, we used the guideline to distinguish between excellent accuracy (0.9 ≤ AUC < 1), good accuracy (0.7 ≤ AUC < 0.9) and non-informative accuracy (AUC = 0.5), whose results showed that pivotal genes had quite excellent efficiency in the diagnosis of advanced AS ().
Figure 5. Linear regression and ROC curve analysis. (A, B) Scatter diagram of the positive correlation between the expression level of pivotal genes and atherosclerosis progression. (E) Determination of the diagnostic efficiency of pivotal genes by ROC curve analysis.
GSEA enrichment analysis
GSEA was used to analyse GO and KEGG enrichment analysis of high and low expression groups of pivotal genes. GO enrichment results revealed that the high expression group of pivotal genes was mainly enriched in oxidative phosphorylation, positive regulation of apoptotic process, NADH dehydrogenase complex assembly and positive T cell selection (). KEGG enrichment results revealed that the high expression group of pivotal genes was mainly enriched in B cell receptor signal pathway, T cell receptor signal pathway, chemokine signal pathway, NOTCH signal pathway, etc. ().
Figure 6. GO and KEGG enrichment analysis based on GSEA. (A) GO enrichment results showed that the high expression group of TYROBP was mainly enriched in the electric transport chain, oxidative photosynthesis, oxidoreductase complex and electric transfer activity. (B) GO enrichment results showed that the high expression group of TLR2 was mainly enriched in ATP synthesis coupled electric transport, positive regulation of apoptotic process, ribosomal subunit and oxidoreductase activity on NADPH. (C) GO enrichment results showed that the high expression group of ITGAM was mainly enriched in NADH dehydrogenase complex assembly, cellular amino acid metabolic and structural constituent of ribosome. (D) GO enrichment results showed that the high expression group of ITGB2 was mainly enriched in positive T cell selection, RNA catabolic process, organellar ribosome and directed DNA polymerase activity. (E) KEGG enrichment results showed that the high expression group of TYROBP was mainly enriched in B cell receptor signal pathway, oxidative phosphorylation, proteasome and T cell receptor signal pathway. (F) KEGG enrichment results showed that the high expression group of TLR2 was mainly enriched in chemokine signal pathway, cytokine–cytokine receptor interaction, NOD like receptor signal pathway and spliceosome. (G) KEGG enrichment results showed that the high expression group of ITGAM was mainly enriched in NOTCH signal pathway, ribosome, RIG-I like receptor signal pathway and RNA polymerase. (H) KEGG enrichment results showed that the high expression group of ITGB2 was mainly enriched in DNA replication, nucleotide excision repair, pantothenate and COA biosynthesis and protein export.
The correlation between pivotal genes and the infiltration of immune cells in atherosclerotic tissues
GEO dataset based on CIBERSORT algorithm was involved, thus the infiltration of immune cells in atherosclerotic tissues at different stages was obtained (). The infiltration of immune genes in atherosclerotic tissues was obtained based on ssGSEA algorithm (), whose results showed that the expression level of immune-related genes in advanced atherosclerotic tissues was significantly up-regulated. The results of correlation analysis showed that the expression level of pivotal genes was positively correlated with the infiltration of macrophages M0, while was negatively correlated with T cells follicular helper. The expression of ITGB2 was positively correlated with the infiltration of T cells regulatory (Tregs), and the infiltration of plasma cells was negatively correlated with TYROBP, ITGB2 and ITGAM ().
Figure 7. Evaluation of the immune status of atherosclerotic tissues based on CIBERSORT algorithm and ssGSEA algorithm. (A) Based on CIBERSORT algorithm, the infiltration level of 22 kinds of immune cells in early and advanced atherosclerotic tissues was obtained. (B) Heat map of the expression level of 29 immune-related genes in early and advanced atherosclerotic tissues was obtained based on ssGSEA algorithm. (C) Heat map of the relationship between pivotal genes and the infiltration of immune cells in advanced atherosclerotic tissues.
Molecular biology experiment in mouse atherosclerosis model
HE staining showed larger atherosclerotic plaques and tissue rupture in later stages of AS compared to earlier stages of AS (). Oil red O staining showed more lipid deposition in later atherosclerotic plaques than in early atherosclerotic plaques (). The results of qPCR (Supplementary Table 6) showed that the mRNA level of Tyrobp, Itgb2, Itgam and Tlr2 in later AS tissues was higher than those in early AS tissues (). The results of Western blot ((G)) (Supplementary Table 7) showed that the expression level of the proteins of Tyrobp, Itgb2, Itgam and Tlr2 was higher in later atherosclerotic tissues.
Figure 8. Molecular biology experiment in mouse atherosclerosis model. (A) HE staining of atherosclerotic tissues at early stages. (B) HE staining of atherosclerotic tissues at later stages. (C) Oil red O staining of atherosclerotic tissues at early stages. (D) Oil red O staining of atherosclerotic tissues at later stages. (E) Histogram of qPCR results of mRNA level of Tyrobp, Itgb2, Itgam and Tlr2 in atherosclerotic tissues at different stages. (F) WB detection results of the protein level of Tyrobp, Itgb2, Itgam and Tlr2 in atherosclerotic tissues at different stages. (G) Histogram of WB results of Tyrobp, Itgb2, Itgam and Tlr2 in atherosclerotic tissues at different stages. Data were expressed as mean ± SEM, *p < .05, **p < .01, ***p < .001, n = 6 compared with early atherosclerosis group.
Discussion
Atherosclerosis is a slow progressing form of aortitis, and the clinically significant AS mainly occurs in the elderly, which is the main cause of death worldwide [Citation12]. Vascular embolism caused by the shedding of advanced unstable atherosclerotic plaques is the main cause of cardiovascular disease (CVD) and stroke, which may endanger the patient’s life in certain serious cases [Citation13]. It is quite important to slow the progression of AS and stabilize atherosclerotic plaque in the treatment of patients with AS. Atherosclerosis is a multi-step pathological process with various factors involved, while there is still a lack of effective methods. Finding specific therapeutic targets is the main focus of AS in recent studies.
Applying bioinformatics for studying the mechanism of AS based on high-throughput sequencing data can help to develop its treatment [Citation7,Citation14]. In this study, we used expression data of atherosclerotic tissues from GEO database to screen pivotal genes that affect the progress of AS through differential expression analysis, WGCNA and PPI. ssGSEA and CIBERSORT algorithms were used to explore the correlation between pivotal genes and the infiltration of immune cells. Finally, the mouse model was used to verify the expression level of pivotal genes in atherosclerotic tissues.
In this study, 74 genes related to the progress of AS were obtained through differential expression analysis. In order to explore the biological functions and the signal pathways involved, GO and KEGG enrichment analyses were carried out. GO enrichment analysis revealed that these genes were mainly enriched in the biological functions of leukocyte migration, regulation of inflammatory response, lipopeptide binding, etc. In AS, leukocyte migration is crucial for the infiltration of monocyte and the accumulation of macrophage, affecting the inflammatory response in tissues. Medina et al. found that the imbalance of monocyte subgroups in AS promoted the instability of atherosclerotic plaques [Citation15]. KEGG enrichment analysis presented that these genes were mainly enriched in lipid and atmosphere, apoptosis, NF-kappa B signal pathway and toll-like receiver signal pathway. Apoptosis played an important role in the progress of AS [Citation16]. Studies have shown that the deficiency of macrophage EP4 increased cell apoptosis, thus inhibiting early AS [Citation17]. Suppression of inflammation mediated by NF-κB signal pathway can inhibit the progress of AS. Oxidized LDL induced JAB1 in the formation of foam cells posed an effect on NF-κB-dependent inflammatory signal transduction [Citation18]. TRAF6 can attenuate apoptosis induced by oxidized low density lipoprotein in endothelial cells through TAK1/p38/NF kB signal pathways [Citation19]. Monaco et al. showed that MyD88 dependent TLR-2 signal pathway was essential for the inflammation and matrix degradation of AS [Citation20], and to block toll-like receptor signal pathway. The above studies confirmed that the AS related genes screened in this study do play an important role.
In order to further identify pivotal genes affecting the progress of AS, TYROBP, ITGB2, ITGAM and TLR2 were obtained through WGCNA and PPI methods combined with differential expression analysis. Analysis of expression data of porcine atherosclerotic tissues induced by high-fat factors showed that TYROBP, ITGB2 and ITGAM were pivotal genes affecting the progress of AS [Citation21]. Researches have shown that the up-regulation of ITGB2 can synergistically affect both adhesion and migration of leukocyte to vascular wall, so as to affect the progress of AS [Citation21]. The transcriptome sequencing analysis of endothelial cells treated with ox-LDL showed that ITGAM was a key gene affecting apoptosis of endothelial cells thus promoting AS [Citation22]. Apoptosis of macrophage in advanced AS was an essential process of plaque necrosis, including endoplasmic reticulum stress. Lipoprotein in macrophages under endoplasmic reticulum stress can trigger the apoptosis of TLR2-dependent cells [Citation23]. Studies have found that TLR2 signal pathway can promote AS in ApoE–/– mouse fed with high-fat diet, which was mediated by Sub1 [Citation24]. The above researches showed that the four pivotal genes played an important role in the progress of AS. We further explored the diagnostic efficiency of pivotal genes in advanced AS by ROC curve, whose results showed that TYROBP, ITGB2, ITGAM and TLR2 had excellent efficiency for advanced AS, among which TYROBP and TLR2 had the best effect.
In order to further explore the impact of pivotal genes on the progress of AS, GSEA was used to explore the biological functions and signal pathways. The results showed that NOTCH signal pathway was activated when ITGAM (CD11b) was over-expressed, and NOD like receptor signal pathway as well as chemokine signal pathway was activated when TLR2 was over-expressed. The co-culture experiment in vitro showed that CD11b+Jag2+ cells derived from bone marrow could induce epithelial-to-mesenchymal transition and metabolism in colorectal cancer through Notch dependent signal pathway. TLRs and NOD like receptors were two main forms of natural immune sensors. Studies have shown that in primary human monocyte-derived macrophages, pre-treating the ligand of Nod2 with muramyl dipeptide can significantly reduce the expression of TLR2 [Citation25]. There are also researches confirming that the high expression of TLR2 can promote the activation of chemokine signal pathway in testicular cancer, leading to poor prognosis to patients [Citation26].
Both innate and adaptive immune systems play a key role in driving chronic vascular inflammation related with AS [Citation27]. In order to explore the role of pivotal genes in the immunity of AS, the analysis results of ssGSEA used in this study showed that the expression level of immune-related genes in advanced atherosclerotic tissues was significantly up-regulated. The expression of ITGB2 (CD18) was positively correlated with the infiltration of Tregs, while the infiltration of plasma cells was negatively correlated with the expression of ITGAM. In the study of psoriasis, researchers found that the reduced expression of CD18 can weaken the contact between Tregs and DCs, TGF-beta-dependent suppressive function of Tregs requires wild-type levels of CD18 [Citation28]. Studies showed that the loss of CD11b during autoimmune process led to the increase of IgG Ab and plasma cells, which also confirmed that the infiltration of plasma cells was negatively related to the expression of CD11b [Citation29]. However, the mechanism of pivotal genes screened in this study on the infiltration of atherosclerotic immune cells still lacks in-depth researches, hence is needed to explore its specific mechanism in the future.
Conclusions
In conclusion, we screened four pivotal genes affecting the progress of AS as TYROBP, ITGB2, ITGAM and TLR2. Enrichment analysis revealed that these genes had an impact on the progress of AS in multiple ways. The results of ssGSEA and CIBERSORT showed that pivotal genes were significantly correlated with the infiltration level of immune cells, and had excellent diagnostic efficiency, which were expected to become therapeutic markers of AS in the future.
Author contributions
Jing Wen, Xin Jin and Jingwen Li designed the experiment and wrote the manuscript. Jinzhen Zheng, Xing Jiang and Yingxia Li were responsible for the data collection. Tong Ren, Xilin Jiang and Hongying Zhao conducted the experiments and analysed the data. All authors read and approved the final manuscript.
Ethical approval
The animal study was reviewed and approved by The Animal Experimentation Ethics Committee of Xiamen University.
Supplemental Material
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No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/).
Additional information
Funding
References
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