Abstract

   Cardiac-cerebral vascular disease (CCVD), is primarily induced by
   atherosclerosis, and is a leading cause of mortality. Numerous studies
   have investigated and attempted to clarify the molecular mechanisms of
   atherosclerosis; however, its pathogenesis has yet to be completely
   elucidated. Two expression profiling datasets, [37]GSE43292 and
   [38]GSE57691, were obtained from the Gene Expression Omnibus (GEO)
   database. The present study then identified the differentially
   expressed genes (DEGs), and functional annotation of the DEGs was
   performed. Finally, an atherosclerosis animal model and neural network
   prediction model was constructed to verify the relationship between hub
   gene and atherosclerosis. The results identified a total of 234 DEGs
   between the normal and atherosclerosis samples. The DEGs were mainly
   enriched in actin filament, actin binding, smooth muscle cells, and
   cytokine-cytokine receptor interactions. A total of 13 genes were
   identified as hub genes. Following verification of animal model, the
   common DEG, Tropomyosin 2 (TPM2), was found, which were displayed at
   lower levels in the atherosclerosis models and samples. In summary,
   DEGs identified in the present study may assist clinicians in
   understanding the pathogenesis governing the occurrence and development
   of atherosclerosis, and TPM2 exhibits potential as a promising
   diagnostic and therapeutic biomarker for atherosclerosis.

   Keywords: cardiac-cerebral vascular diseases, atherosclerosis,
   differentially expressed genes, protein-protein interaction,
   bioinformatics analysis

INTRODUCTION

   Cardiac-cerebral vascular diseases (CCVDs) have a high prevalence,
   disability and mortality rates, and are a serious threat to human
   health, especially to the health of the populace aged over 50 years
   [[39]1]. Each year, 15 million people die from CCVDs, which are
   primarily induced by atherosclerosis, and are the leading cause of
   mortality in the world [[40]2]. Numerous studies [[41]3–[42]7] have
   demonstrated that the pathophysiological process for the development of
   atherosclerosis are closely associated with the mutation and abnormal
   expression of genes, which include fms-like tyrosine kinase-1 (Flt-1),
   tumor necrosis factor (TNF)-α, apolipoprotein A-I (apoA-I), vascular
   endothelial growth factor (VEGF) and angiogenin. A previous study
   demonstrated that the low-expression of Flt-1 may predict the
   occurrence of endothelial injury, which subsequently results in the
   occurrence of atherosclerosis [[43]3]. The stronger the proliferation
   competence of endothelial progenitor cell (EPC) is, the less feeble
   endothelial injury may be, and the overexpression of TNF-α may damage
   the process of EPC development [[44]4]. In addition, the mutation of
   Apolipoprotein A-I (apoA-I), an anti-atherogenic gene, has been
   hypothesized to accelerate the apoptosis of vascular endothelial cells
   and downregulate the levels of eNOS and heme oxygenase-1, which
   culminates in atherosclerosis plaque formation [[45]5]. It is well
   established that the expression of VEGF and ANG stimulates the renewal
   of vascular endothelial cells. Therefore, abnormal inactivation of VEGF
   and angiogenin expression may exert a pivotal function in the
   occurrence and development of atherosclerosis [[46]6, [47]7]. However,
   due to the lack of timely detection, dynamic monitoring and effective
   control of the occurrence and progression of atherosclerotic stenosis
   and vulnerable plaques, the occurrence and recurrence of ischemic CCVDs
   still cannot be effectively controlled, hence why research
   investigating ischemic cardiac-cerebral vascular diseases is one of the
   principal areas of research at home and abroad [[48]8]. Therefore, it
   is imperative to explore the accurate molecular targets included in the
   hyperplasia and recidivation of atherosclerosis, in order to make a
   contribution to the diagnosis and treatment of atherosclerotic
   diseases.

   Since the 21^st century, bioinformatics technology has been
   increasingly used to excavate the potential genetic targets of
   diseases, which has assisted researchers in authenticating the
   differentially expressed genes (DEGs) and underlying pathways that are
   associated with the occurrence and recurrence of atherosclerosis
   [[49]9–[50]12].

   However, it is difficult to acquire credible results when using the
   independent microarray technology owing to the higher false-positive
   rates [[51]13]. Therefore, the present study downloaded and analyzed
   two human expression profiling datasets from the Gene Expression
   Omnibus (GEO) Dataset, and identified the DEGs between
   non-atherosclerotic tissues and atherosclerotic tissues. The molecular
   mechanisms of the occurrence and development of atherosclerosis were
   subsequently explored via enrichment analysis of functions and
   pathways, protein-protein interaction (PPI) network analyses and
   co-expression network analyses, and a total of 234 DEGs and 13 hub
   genes were identified and authenticated.

   In addition, in order to verify the results of the bioinformatics
   analysis, an animal experiment using two groups (a control and
   atherosclerosis model group) was implemented. The data of animal
   experiment was digged via a proteomics assay, and differentially
   expressed proteins were identified between the control and
   atherosclerosis groups. Then after comparing the bioinformatics result
   and proteomic data, Tropomyosin 2 (TPM2) was identified as a commonly
   differentially expressed gene, which exhibits potential as a molecular
   target or biomarker for atherosclerosis. A series of low flux
   experiments were subsequently performed to verify the role and function
   of TPM2 in atherosclerosis.

RESULTS

Validation of the datasets

   To further validate the intra-group data repeatability, we employed the
   Pearson’s correlation test and principal component analysis (PCA).
   Based on the Pearson’s correlation test, we found that in the
   [52]GSE43292 dataset there were strong correlations among the samples
   in the control group and that there were also strong correlations among
   the samples in the atherosclerosis group ([53]Figure 1A). Based on the
   PCA the intra-group data repeatability for [54]GSE43292 was acceptable.
   The distances between per samples in the control group were close and
   the distances between per samples in the atherosclerosis group were
   also close in the dimension of principal component-1 (PC1) ([55]Figure
   1B). Based on Pearson’s correlation test, we found that for
   [56]GSE57691 there was a strong correlation among the samples in the
   control group and a strong correlation among the samples in the
   atherosclerosis group ([57]Figure 1C). The PCA showed the intra-group
   data repeatability to be acceptable in the [58]GSE57691 dataset. The
   distances between per samples in the control group were close, and
   distances between per samples in the atherosclerosis group were also
   close in the dimension of PC1 ([59]Figure 1D).

Figure 1.

   [60]Figure 1
   [61]Open in a new tab

   (A) Pearson’s correlation analysis of samples from the [62]GSE43292
   dataset. The color reflects the intensity of the correlation. When
   0<correlation<1, there exists a positive correlation. When
   -1<correlation<0, there exists a negative correlation. The larger the
   absolute value of a number the stronger the correlation. (B) PCA of
   samples from the [63]GSE43292 dataset. In the figure, principal
   component 1 (PC1) and principal component 2 (PC2) are used as the
   X-axis and Y-axis, respectively, to draw the scatter diagram, where
   each point represents a sample. In such a PCA diagram, the farther the
   two samples are from each other, the greater the difference is between
   the two samples in gene expression patterns. (C) Pearson’s correlation
   analysis of samples from the [64]GSE57691 dataset. The color reflects
   the intensity of the correlation. (D) PCA of samples from the
   [65]GSE57691 dataset. (E) The volcano plot illustrates the differences
   between control and atherosclerotic tissues after analysis of the
   [66]GSE43292 dataset with GEO2R. (F) The volcano plot illustrates the
   difference between control and atherosclerotic tissues after analysis
   of the [67]GSE57691 dataset with GEO2R. (G) Overlap between differently
   expressed gene lists of [68]GSE43292 and [69]GSE57691 only at the gene
   level, where purple curves link identical genes; (H) Overlap between
   differently expressed gene lists of [70]GSE43292 and [71]GSE57691 not
   only at the gene level, but also at the shared term level, where blue
   curves link genes that belong to the same enriched ontology term. The
   inner circle represents gene lists, where hits are arranged along the
   arc. Genes that hit multiple lists are colored in dark orange, and
   genes unique to a list are shown in light orange. (I) The Venn diagram
   could demonstrate that 234 genes were contained in the [72]GSE43292 and
   [73]GSE57691 datasets simultaneously.

DEGs identified between control and atherosclerotic tissues

   Following the analysis of the [74]GSE43292 and [75]GSE57691 datasets
   with GEO2R, the differences between control and atherosclerotic tissues
   were presented in volcano plots ([76]Figure 1E and [77]1F). The Circos
   present the overlap between differently expressed gene lists of
   [78]GSE43292 and [79]GSE57691 not only at the gene level ([80]Figure
   1G), but also at the shared term level ([81]Figure 1H). These results
   were standardized, and a total of 936 DEGs in the [82]GSE43292 dataset,
   and 4,133 DEGs in the [83]GSE57691 dataset were identified. A Venn
   diagram was constructed, and revealed that 234 genes were
   simultaneously contained within the 2 examined datasets ([84]Figure
   1I).

Construction of co-expression modules by weighted gene co-expression network
analysis (WGCNA)

   One key step is selection of the soft-thresholding power in the
   procedure of performing a WGCNA. The network topology analysis was
   performed for thresholding powers and identified the relatively
   balanced scale independence and mean connectivity. The power value 9,
   which was the lowest power for the scale-free topology fit index on
   0.9, was selected to produce a hierarchical clustering tree
   (dendrogram) of 936 genes. We set the MEDissThres as 0.25 to merge
   similar modules, and 3 important modules were generated ([85]Figure
   2A). The network heatmap plot shows that there was no significant
   difference in interactions among different modules, indicating a
   high-scale independence degree among these modules ([86]Figure 2B).
   Similar results were demonstrated by the eigengene adjacency heatmap
   ([87]Figure 2C). The turquoise module was the most positively
   correlated with status of atherosclerosis, and the blue module was the
   most negatively correlated with status of atherosclerosis ([88]Figure
   2D).

Figure 2.

   [89]Figure 2
   [90]Open in a new tab

   (A) Construction of co-expression modules by weighted gene
   co-expression network analysis (WGCNA) package in R. The cluster
   dendrogram of genes in [91]GSE43292. Each branch in the figure
   represents one gene, and every color below represents one co-expression
   module. (B) Interaction relationship analysis of co-expression genes.
   Different colors of horizontal axis and vertical axis represent
   different modules. The brightness of yellow in the middle represents
   the degree of connectivity of different modules. There was no
   significant difference in interactions among different modules,
   indicating a high-scale independence degree among these modules. (C)
   Heatmap plot of the adjacencies in the hub gene network. (D) Heatmap of
   the correlation between module eigengenes and the disease status of
   atherosclerosis. The turquoise module was the most positively
   correlated with status, and the blue module was the most negatively
   correlated with status.

Functional annotation of DEGs by GO and KEGG analyses

   The DAVID results of the GO analysis revealed that there were main
   variations in biological processes (BP), including muscle contraction,
   actin filament organization, and so on ([92]Figure 3A). The variations
   in cell components (CC) of DEGs were markedly enriched in the actin
   filament, and cytosol ([93]Figure 3B). The variations in molecular
   function (MF) were markedly enriched in actin filament binding,
   structural constituent of muscle, and actin binding ([94]Figure 3C).
   Analysis of the KEGG pathway revealed that the all DEGs were primarily
   enriched in dilated cardiomyopathy, adrenergic signaling in
   cardiomyocytes, and hypertrophic cardiomyopathy (HCM) ([95]Figure 3D).

Figure 3.

   [96]Figure 3
   [97]Open in a new tab

   (A–C) Detailed information relating to changes in the biological
   processes (BP), cellular components (CC), and molecular functions (MF)
   of DEGs in atherosclerosis and control tissues through the GO
   enrichment analyses. (D) The KEGG pathway analysis of DEGs. KEGG, Kyoto
   Encyclopedia of Genes and Genomes; GO, Gene Ontology; DEGs,
   differentially expressed genes. (E) Heatmap of enriched terms across
   input differently expressed gene lists, colored by p-values, via the
   Metascape. (F) Network of enriched terms colored by cluster identity,
   where nodes that share the same cluster identity are typically close to
   each other. (G) Network of enriched terms colored by p-value, where
   terms containing more genes tend to have a more significant p-value.

   GSEA was used to perform GO and KEGG analysis to explore the function
   and pathways of DEGs. GO enrichment analysis show that 2710/4412 gene
   sets were upregulated in atherosclerosis, 822 gene sets were
   significantly enriched at nominal p-value < 0.05, and 235 gene sets are
   significantly enriched at nominal p-value < 0.01. In addition,
   1702/4412 gene sets are downregulated in atherosclerosis, 308 gene sets
   are significantly enriched at nominal p value <5%, and 89 gene sets are
   significantly enriched at nominal p-value < 0.01. The enrichments for
   both up- and down-regulated gene sets in the significant order (size of
   NES) are listed in [98]Table 1, such as “GO_SKELETAL_MUSCLE_
   CONTRACTION”, “GO_NEGATIVE_REGULATION _OF_SMOOTH_MUSCLE_CONTRACTION”,
   “GO_ ACTIN_FILAMENT_POLYMERIZATION”, “GO_
   ACTIN_FILAMENT_BUNDLE_ORGANIZATION”, “GO_SMOOTH_MUSCLE_CONTRACTION”,
   and “GO_MUSCLE_CONTRACTION”. GSEA also revealed that upregulated gene
   sets in atherosclerosis were mainly associated with muscle contraction,
   and actin filament. Furthermore, KEGG enrichment analysis indicated
   that 121/174 gene sets are upregulated in atherosclerosis compared to
   normal artery samples, 40 gene sets are significantly enriched at
   nominal p value <5%, and 16 gene sets are significantly enriched at
   nominal p-value < 1%. 53/174 gene sets are downregulated in
   atherosclerosis, 10 gene set is significantly enriched at nominal p
   value <5%, and 2 gene sets are significantly enriched at nominal
   p-value < 1%. We respectively displayed important 9 gene sets
   correlated with atherosclerosis according to NES in [99]Table 2, such
   as “KEGG_EPITHELIAL_CELL_ SIGNALING_IN_HELICOBACTER_PYLORI_INFECTION”,
   “KEGG_BASE_EXCISION_REPAIR”, “KEGG_ DILATED_CARDIOMYOPATHY”, and so on.

Table 1. Functional enrichment analysis of DEGs in atherosclerosis using
GSEA.

   Gene set name SIZE ES NES Rank at max
   Upregulated
   GO_SKELETAL_MUSCLE_CONTRACTION 29 0.316 0.939 2942
   GO_NEGATIVE_REGULATION_OF_SMOOTH_MUSCLE_CONTRACTION 15 0.360 0.902 3373
   GO_ACTIN_FILAMENT_POLYMERIZATION 20 0.393 1.205 2007
   GO_ACTIN_FILAMENT_BUNDLE_ORGANIZATION 47 0.302 1.064 1726
   GO_CYTOSOLIC_CALCIUM_ION_TRANSPORT 48 0.435 1.218 1988
   GO_CYTOSOLIC_PART 192 0.256 1.011 3378
   Downregulated
   GO_SMOOTH_MUSCLE_CONTRACTION 44 −0.580 −1.577 2784
   GO_MUSCLE_CONTRACTION 220 −0.514 −1.573 2897
   GO_REGULATION_OF_SMOOTH_MUSCLE_CONTRACTION 60 −0.505 −1.422 2121
   GO_ACTIN_FILAMENT_ORGANIZATION 158 −0.308 −1.195 2174
   GO_CYTOSOLIC_TRANSPORT 188 −0.237 −1.144 2287
   GO_ENDOPLASMIC_RETICULUM_TO_CYTOSOL_TRANSPORT 21 −0.347 −1.104 27
   [100]Open in a new tab

   ES: Enrichment Score; NES: Normalized Enrichment Score.

Table 2. Pathway enrichment analysis of DEGs in atherosclerosis using GSEA.

   Gene set name SIZE ES NES Rank at max
   Upregulated
   KEGG_EPITHELIAL_CELL_SIGNALING_IN_HELICOBACTER_ PYLORI_INFECTION 61
   0.563 1.525 3156
   KEGG_BASE_EXCISION_REPAIR 31 0.459 1.522 6082
   KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION 57 0.653 1.519 2554
   KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY 101 0.605 1.510 3648
   KEGG_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY 88 0.702 1.508 2235
   KEGG_PATHOGENIC_ESCHERICHIA_COLI_INFECTION 45 0.499 1.505 4454
   Downregulated
   KEGG_DILATED_CARDIOMYOPATHY 86 −0.524 −1.434 1865
   KEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM 80 −0.499 −1.407 2006
   KEGG_LONG_TERM_POTENTIATION 65 −0.390 −1.300 1772
   KEGG_ADHERENS_JUNCTION 72 −0.425 −1.272 2780
   [101]Open in a new tab

   ES: Enrichment Score; NES: Normalized Enrichment Score.

   What’s more, the enrichment analysis of metascape also demonstrates
   that the DEGs between control and atherosclerosis were markedly
   enriched in the actin filament-based process ([102]Figure 3E–[103]3G).

PPI network construction, module analysis, hub gene selection and analysis

   The PPI network of DEGs was constructed ([104]Figure 4) and the most
   significant module and network of hub genes was identified using
   Cytoscape software ([105]Figure 5A). A total of 13 genes were
   identified as hub genes with a degree of ≥13. Hierarchical clustering
   demonstrated that the hub genes could effectively differentiate the
   atherosclerotic samples from the non-atherosclerotic samples
   ([106]Figure 5B and [107]5C). Among these genes, MYH10, ACTC1, FLNC and
   TPM2 exhibited the lower expression in the atherosclerosis samples,
   while the expressions of CXCL2, ITGAX, SELL, CCR2, LCP2, FPR2, NLRP3,
   IL1B, and CXCL8 were up-regulated in the atherosclerosis samples,
   suggesting that they may exert pivotal functions in the occurrence or
   progression of atherosclerosis.

Figure 4.

   [108]Figure 4
   [109]Open in a new tab

   The protein-protein interaction (PPI) network of differentially
   expressed genes (DEGs).

Figure 5.

   [110]Figure 5
   [111]Open in a new tab

   (A) The hub genes were identified from the PPI network. (B, C)
   Hierarchical clustering demonstrated that the hub genes could
   effectively differentiate the atherosclerotic samples from the
   non-atherosclerotic samples in the [112]GSE57691 and [113]GSE43292
   datasets. The upregulated genes are marked in pink, the downregulated
   genes are marked in blue. (D) The GO enrichment analyses of DEGs of our
   private dataset. (E) The KEGG pathway analysis of DEGs of our private
   dataset.

Verification of the animal model and proteomics

   After the construction of animal model, proteomic analysis was
   performed. The results of GO analysis of DEGs from our animal model
   exhibited the most similarity with the [114]GSE43292 and [115]GSE57691
   datasets. The detailed information of changes in the BP, CC, and MF,
   are presented in the [116]Figure 5D. KEGG pathway analysis for our own
   dataset displayed that DEGs were primarily enriched in the lysosome,
   phagosome, rheumatoid arthritis, staphylococcus aureus infection, and
   so on. ([117]Figure 5E). Hierarchical clustering presented in
   [118]Figure 6A revealed the expression situation of DEGs between
   control and atherosclerotic samples in our private dataset. The
   upregulated genes are marked in red, and the downregulated genes are
   marked in green. The right legends of [119]Figure 6A represent Protein
   ID, and the “157787195” presents TPM2.

Figure 6.

   [120]Figure 6
   [121]Open in a new tab

   (A) Hierarchical clustering revealed the expression situation of DEGs
   between control and atherosclerotic samples in our private dataset.
   Upregulated genes are marked in red, downregulated genes are marked in
   green. The right legends represent Protein ID, where “157787195”
   represents TPM2. (B) The receiver operator characteristic curve,
   indicating that the expression level of TPM2 in the [122]GSE43292 could
   predict atherosclerosis sensitively and specifically. (C) The
   pathological observation of artery of the control and atherosclerosis
   groups through the HE staining. (Gross appearance, 40x, 100x). HE:
   hematoxylin-eosin.

Common DEG between public and private datasets

   According to comparison between public and private datasets, the common
   DEG was identified, which is TPM2. The present study identified that
   TPM2 in the atherosclerotic tissues was displayed at significantly
   lower levels when compared with the matched normal tissues (P<0.05).
   The receiver operator characteristic curve indicates that the
   expression level of TPM2 in the [123]GSE43292 could predict
   atherosclerosis sensitively and specifically (area under the curve for
   intimal thickness, 0.837; p<0.001) ([124]Figure 6B).

Association between TPM2 and atherosclerosis by Pearson correlation test and
univariate linear regression

   Pearson correlation coefficient displayed that the status of
   atherosclerosis was significantly correlated with the expression of
   TPM2 (ρ=-0.567, p<0.001). The natural logarithmic atherosclerosis
   remained related to the TPM2 (β=-0.461 p<0.001) in the univariate
   linear regression model ([125]Table 3).

Table 3. The correlation and linear regression analysis between
atherosclerosis and relevant gene expression.

   Gene symbol Atherosclerosis
   Pearson’s correlation coefficient Univariate linear regression
   ρ^a p-value β^b P-value
   TPM2 ‒0.567 <0.001* ‒0.461 <0.001*
   MYH10 ‒0.512 <0.001* ‒0.329 <0.001*
   ACTA1 ‒0.503 <0.001* ‒0.236 <0.001*
   IL1B 0.406 0.001* 0.211 0.001*
   ITGAX 0.518 <0.001* 0.250 <0.001*
   SELL 0.504 <0.001* 0.422 <0.001*
   CCR2 0.400 0.001* 0.241 0.001*
   LCP2 0.576 <0.001* 0.405 <0.001*
   CXCL8 0.373 0.002* 0.149 0.002*
   NLRP3 0.584 <0.001* 0.480 <0.001*
   FLNC ‒0.575 <0.001* ‒0.430 <0.001*
   CXCL2 0.358 0.004* 0.213 0.004*
   FPR2 0.383 0.002* 0.206 0.002*
   [126]Open in a new tab

   ^aPearson’s correlation coefficient between atherosclerosis and
   relevant characteristics; ρ: Pearson’s correlation coefficient.

   ^bUnivariate linear regression analysis, β: parameter estimate.

   *Significant variables: P<0.05

An independent risk factor for atherosclerosis based on univariate and
multivariate logistic regression: TPM2

   [127]Table 4 presents the univariate odd ratios (ORs) and 95%
   confidence intervals (95%CI) for atherosclerosis of hub genes. The OR
   for atherosclerosis was 0.090 (95% CI, 0.028-0.293, p<0.001) in the
   group with high expression of TPM2 compared with low expression of TPM2
   ([128]Table 4). In order to effectively control the influence of
   confounding factors, multivariate factors were incorporated into the
   multivariate logistic regression model simultaneously, which can also
   predict the most independent risk characteristic. [129]Table 5 showed
   the result of multivariate logistic proportional regression analysis.
   The higher expression of TPM2 has, the significantly greater benefit
   there has, and the OR of high TPM2 is 0.140 (95% CI, 0.020-0.988;
   p=0.049) ([130]Table 5).

Table 4. Correlative parameters’ effect on atherosclerosis based on
univariate logistic proportional regression analysis.

   Parameters                AS
                    OR      95% CI      P
   TPM2  Low  29    1                <0.001*
         High 35  0.090  0.028–0.293
   MYH10 Low  26    1                0.001*
         High 38  0.138  0.044–0.433
   ACTC1 Low  35    1                <0.001*
         High 29  0.127  0.041–0.391
   IL1B  Low  29    1                0.026*
         High 35  3.215  1.150–8.987
   ITGAX Low  27    1                <0.001*
         High 37  8.273  2.622–26.100
   SELL  Low  30    1                <0.001*
         High 34  13.000 3.935–42.950
   CCR2  Low  33    1                0.007*
         High 31  4.200  1.478–11.936
   LCP2  Low  24    1                0.001*
         High 40  7.892  2.409–25.857
   CXCL8 Low  33    1                0.026*
         High 31  3.182  1.145–8.841
   NLRP3 Low  29    1                <0.001
         High 35  11.074 3.418–35.878
   FLNC  Low  35    1                <0.001*
         High 29  0.062  0.018–0.214
   CXCL2 Low  34    1                0.048*
         High 30  2.790  1.011–7.698
   FPR2  Low  39    1                0.006*
         High 25  4.592  1.542–13.671
   [131]Open in a new tab

   OR, odds ratio; 95% CI, 95% confidence interval. * P < 0.05

Table 5. Correlative genes’ effect on AS based on multiple logistic
proportional regression analysis.

   Genes            AS
          OR      95% CI      P
   TPM2  0.140 0.020–0.988  0.049*
   IL1B  1.105 0.181–6.730  0.914
   CCR2  0.819 0.152–4.419  0.817
   CXCL8 1.142 0.125–10.477 0.906
   CXCL2 0.486 0.046–5.171  0.550
   FPR2  2.658 0.467–15.111 0.270
   NLRP3 6.032 0.880–41.363 0.067
   MYH10 2.611 0.222–30.678 0.445
   [132]Open in a new tab

   AS: atherosclerosis; OR, odds ratio; 95% CI, 95% confidence interval. *
   P < 0.05

Effects of the atherosclerosis on the abdominal aorta in the aspect of
intima-media thickness and smooth muscle cells

   [133]Figure 6C presents the status of abdominal aorta from the
   microcosmic appearances, which showed that intima-media thickness was
   incrassated in the atherosclerosis group, compared with the control
   group ([134]Figure 6C). Through the immunohistochemical staining
   ([135]Figure 7A) and immunofluorescence assay ([136]Figure 7B), the
   artery wall of the atherosclerosis group was more deficient in smooth
   muscle cells than the control group.

Figure 7.

   [137]Figure 7
   [138]Open in a new tab

   (A) The detection of SMC of the artery wall by immunohistochemical
   staining with anti-SMC monoclonal antibody. (Gross appearance, 30x,
   100x). (B) The detection of SMC content of the artery wall by
   immunofluorescence assay. (Gross appearance, 30x). (C) Relative
   expression of TPM2 by RT-qPCR analysis. *p< 0.05, compared with
   control. (D) Western blotting expression of TPM2 in the control (Con)
   and atherosclerosis (AS) groups. (E) Quantitative comparison of TPM2
   expression between the two groups. (F) The linear correlation between
   intima-media thickness and the relative expression of TPM2. (G) The
   linear correlation between intima-media thickness and the relative
   expression of SMC. (H) The linear correlation between the relative
   expression of TPM2 and the relative expression of SMC. SMC: smooth
   muscle cell.

Verification of the expression of TPM2

   According to the above expression analysis, TPM2 were markedly
   down-regulated in atherosclerosis samples. Results of RT-qPCR and
   western blotting analysis showed that the relative expression level of
   TPM2 was significantly lower in atherosclerosis samples, compared with
   the control groups (p<0.05) ([139]Figure 7C–[140]7E). The result
   demonstrated that TPM2 might be considered as biomarkers for
   atherosclerosis.

Strong associations between the intima-media thickness, the relative
expression of TPM2 and SMC

   The intima-media thickness was negatively associated with the relative
   expression of TPM2 (Pearson Rho=-0.922, P<0.001) ([141]Figure 7F), and
   the relative expression of SMC (Pearson Rho=-0.976, P<0.001)
   ([142]Figure 7G). And there is a positive association between the
   relative expression of TPM2 and the relative expression of SMC (Pearson
   Rho=0.941, P<0.001) ([143]Figure 7H). The receiver operator
   characteristic curve indicates that the expression level of TPM2 in our
   own experiment also could predict atherosclerosis sensitively and
   specifically (area under the curve for intimal thickness, 0.966;
   p<0.05) ([144]Figure 8A).

Figure 8.

   [145]Figure 8
   [146]Open in a new tab

   (A) The receiver operator characteristic curve, indicating that the
   expression level of TPM2 in the own experiment could predict
   atherosclerosis sensitively and specifically. (B) The neural network
   prediction model of atherosclerosis intima-media thickness. The best
   training performance is 0.0064465 at epoch 2000. (C) The final training
   model of neural network prediction model, and the relativity is
   0.99133. (D) Verify the predicted value of the data against the actual
   value. (E) Verify the data error percentage curve. (F) The high-risk
   warning range of atherosclerosis intima-media thickness at the level of
   the planform. (G) The high-risk warning range of atherosclerosis
   intima-media thickness at the level of the three dimensional
   stereogram. The color represents the intima-media thickness: the “red”
   represents “high”, the “blue” represents “low”.

The neural network prediction model and high-risk warning range of
atherosclerosis intima-media thickness

   After training, the neural network prediction model reached the best
   effect, in which mean squared error is 0.0064465 at epoch 2000
   ([147]Figure 8B), and the relativity is 0.99133 ([148]Figure 8C).
   Through verifying the predicted value of the data against the actual
   value, we found that there are only small differences ([149]Figure 8D,
   [150]8E). Based on the above result, we could speculate that the
   expression of TPM2 and smooth muscle cell might be the predictive
   indexes of atherosclerosis intima-media thickness.

   Through the cubic spline interpolation algorithm, we find the high-risk
   warning indicator of atherosclerosis intima-media thickness: 0.226 <
   TPM2 < 0.455, and 0.3 < expression of smooth muscle cell< 0.44
   ([151]Figure 8F). Furthermore, the three dimensional stereogram could
   present the warning range well ([152]Figure 8G).

DISCUSSION

   Due to the health complications associated with aging, the incidence of
   ischemic cardiovascular and cerebrovascular diseases is increasing, and
   is now the leading cause of disability and mortality worldwide recently
   [[153]14, [154]15]. Atherosclerosis is a chronic inflammatory disease,
   that can be induced by a plethora of factors, including environment,
   diet, polygenetic inheritance among other factors [[155]16].
   Atherosclerosis is the principal pathological cause for coronary heart
   disease, myocardial infarction, cerebral infarction, cerebral
   hemorrhage and other cardiovascular and cerebrovascular diseases
   [[156]17]. Previous studies [[157]18, [158]19] have demonstrated that
   autophagy and p62 serve a significant role in the development of
   atherosclerosis. In the early stage of atherosclerosis, an appropriate
   increase in the expression of p62 and its ubiquitination may reinforce
   the stability of plaques and delay the development of atherosclerosis.
   In the middle and late stage of atherosclerosis, the process of
   autophagy has been revealed to impede p62 aggregation, inhibit relevant
   pathways, and attenuate plaque instability [[159]20]. Activated
   NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome
   was observed to transform caspase-1 precursor into active caspase-1,
   promote the maturation and secretion of interleukin-1 beta and IL-18,
   and then trigger the inflammatory response of the body [[160]21]. NLRP3
   is closely associated with the formation of atherosclerosis. A large
   proportion of patients with atherosclerosis are not diagnosed in the
   early stage of disease, and therefore are not appropriate candidates
   for treatment, which is one of the principal causes for unfavorable
   patient outcomes. Therefore, the identification and investigation of
   underlying biomarkers for high-efficiency therapy and diagnosis are
   urgently required. Bioinformatics technology permits researchers to
   probe and investigate the genetic changes in atherosclerosis, and has
   been demonstrated to be a valid method to distinguish novel biomarkers
   in other diseases [[161]9–[162]12].

   Following the analysis of the 2 microarray datasets in the current
   research, DEGs between atherosclerotic and non-atherosclerotic tissues
   were identified. A total of 234 DEGs were contained in the 2 datasets
   simultaneously, and the DEGs’ interactions were explored via KEGG and
   GO analyses, The DEGs were primarily enriched in actin filament, actin
   binding, smooth muscle cells, and cytokine-cytokine receptor
   interactions. Previous researches have reported that the maladjustment
   of cellular process and cellular component organization or biogenesis
   has a substantial impact on the occurrence and development of
   atherosclerosis [[163]22–[164]24]. In addition, recent research has
   hypothesized that atherosclerosis exerts an accelerating role for actin
   binding and catalytic activity [[165]25]. In addition,
   cytokine-cytokine receptor interactions and cell adhesion molecules
   have also been reported to serve an important role in the stability of
   plaque formation which is frequently altered in atherosclerosis
   [[166]26, [167]27]. In brief, the results presented herein are
   consistent with the aforementioned studies.

   Following the analysis of the public and private datasets, the
   expression of TPM2 in the atherosclerotic tissues was observed to be
   lower than in normal tissues. TPM2 is a subtype of tropomyosin
   [[168]28]. Tropomyosin (TPM), a thin filament-associated protein, and
   has been hypothesized to serve a crucial role in the regulation of
   muscle contraction [[169]29]. TMP is widely distributed in various
   eukaryotic cells in the form of a large number of isomers, and four
   gene subtypes of TPM have been identified in mammals, which are
   respectively named TPM1, TPM2, TPM3 and TPM4 [[170]30]. TPM is
   associated with cellular migration, morphogenesis and the regulation of
   actin filaments [[171]28]. TPM2 is located on chromosome 9p13.2-p13.1,
   and the CDS sequence is 1120bp, and encoding protein mass is 36000.
   TPM2 contains 11 exons in rats, among which exons 3, 4, 5, 7 and 8 are
   expressed in all known isomers. The gene encodes, in addition to
   skeletal muscle beta TPM, TPM1 isomers of smooth muscle beta TPM and
   fibroblasts. The gene encodes not only β-TPM of skeletal muscle, but
   also β-TPM of smooth muscle and TPM1 isomers of fibroblasts. Despite
   sequence differences, all the β-TPM isomers are composed of 284 amino
   acids. The chicken beta gene encodes a type of fibroblast, TPM-3b
   [[172]31].

   TPM regulates the dynamic changes of actin filaments in the cell
   migration, morphogenesis, and cytoplasmic migration [[173]32]. The
   study of Cui et al. discovered that TPM2 appears to be commonly
   silenced by aberrant DNA methylation in colon cancer. TPM2 loss is
   associated with Ras homolog gene family member A (RhoA) activation and
   tumor proliferation and transfer [[174]33]. In addition, the study of
   Zhang et al. identified that TPM2 expression is downregulated in breast
   cancer cells when compared with that in normal breast cells [[175]28].
   In the process of tumor cell metastasis, cancer cells break away from
   tumor tissue and move to target organs. These processes are inseparable
   from cell movement [[176]34]. Therefore, TPM2 may also be involved in
   the process of tumor formation and metastasis via this mechanism. In
   addition, we hypothesize that TPM2 is closely associated with the
   process of cell formation and metastasis in atherosclerotic tissue.

   Clarkson [[177]35] found that TPM2, actin, and troponin are mainly
   related to congenital myopathy. Bailey’s research [[178]36]
   demonstrated that TPM is the molecular target leading to
   high-temperature superconducting (HTS) efforts. Bartelt [[179]37] found
   that HTS could prevent the formation and development of
   atherosclerosis. On the one hand, HTS can reduce residual lipoproteins
   that are involved in atherosclerosis. On the other hand, HTS can also
   improve high-density lipoprotein (HDL) remodeling and promote
   cholesterol clearance. Moreover, in terms of lipid deposition, HTS can
   also increase the apoptosis of macrophages and reduce the formation of
   foam cells. What’s more, endothelial function remains an important role
   in the formation of atherosclerosis. Abnormal expression of myosin
   microfilaments could damage endothelial cells, provide a binding point
   for lipids and calcium, and also stimulate fibroblast proliferation and
   exacerbates fibrosis. However, the role of the TPM family is to
   stabilize myosin filaments and endothelial function [[180]38]. This
   suggests that the low expression of TPM2 in the diseased arteries might
   damage the function of endothelial cells, and lead to the occurrence of
   atherosclerosis. Also, abnormal function of smooth muscle cell is still
   a factor causing atherosclerosis. TPM could maintain the stability of
   actin of smooth muscle, and indirectly promote the vasoconstriction
   function. Therefore, the lack of TPM might limit vasoconstriction
   [[181]39]. Excessive dilation of blood vessels will lead to rupture and
   degeneration of the middle artery, which will induce the formation of
   atherosclerosis [[182]40].

   Previous studies have demonstrated that in atherosclerotic tissue, the
   number of vascular smooth muscle cells in the middle membrane is
   decreased, the middle membrane becomes thinner, and the intimal plaque
   is rich in macrophages [[183]41]. In addition, the results of the
   present study revealed that the expression of TPM2 in atherosclerotic
   tissues was significantly reduced, and TPM2, as one subtype of TPM, was
   abundant in skeletal muscle, smooth muscle and fibroblasts. TPM2 is
   also involved in muscle contraction, cell movement and other biological
   process [[184]28, [185]29, [186]31]. This has culminated in the
   hypothesis that, in the development of atherosclerosis, the
   down-expression of TPM2 leads to disorders in the formation and
   movement of vascular smooth muscle cells. The middle membrane of blood
   vessel subsequently becomes thinner and brittle, which could cause the
   weak contractility. Subsequently, macrophages may phagocytose
   ineffective smooth muscle cells or fragile fibroblasts of the vascular
   middle membrane, and migrate to the vascular intima to participate in
   the formation of atherosclerotic plaques.

Limitations

   Despite the rigorous bioinformatics analysis in this study, there are
   still some limitations. First, the sample size of our study is small,
   which might result in some deviations in the results. Secondly, we only
   conducted bioinformatics mining and animal experimental verification,
   which could demonstrate that TPM2 plays an important role in the
   occurrence and development of atherosclerosis to some extent. However,
   there are still differences between animals and human. Currently, it is
   difficult to cut vascular tissue of healthy human, which refers to the
   ethical issue and informed consent. Therefore, the research has not
   obtained the evidence from human.

Future directions

   In the next stage of research, we will try to obtain the ethical
   approval documents and informed consent so that the further study could
   perform verification from the human. And multicenter randomized
   controlled clinical trial should be performed, and recruit more
   subjects to verify the relationship between expression level of TPM2
   and formation of atherosclerosis.

   In the present research, a total of 234 DEGs and 13 hub genes were
   identified and authenticated between atherosclerotic tissues and
   non-atherosclerotic tissues. Following verification of animal model,
   the common DEG, Tropomyosin 2 (TPM2), was found, which were displayed
   at lower levels in the atherosclerosis models and samples. In summary,
   DEGs identified in the present study may assist clinicians in
   understanding the pathogenesis governing the occurrence and development
   of atherosclerosis, and TPM2 exhibits potential as a promising
   diagnostic and therapeutic biomarker for atherosclerosis.

METHODS

Access to public data

   GEO ([187]http://www.ncbi.nlm.nih.gov/geo) [[188]42] is an open
   functional genomics database of high-throughput resource that includes
   microarrays, gene expression data and chips. Two expression profiling
   datasets [[189]GSE43292 [[190]43] and [191]GSE57691 [[192]44]], which
   all identify genes and pathways involved in the formation of
   atherosclerotic plaques from normal individuals, were obtained from the
   GEO ([193]GPL6244 platform, [HuGene-1_0-st] Affymetrix Human Gene 1.0
   ST Array [transcript (gene) version], and [194]GPL10558 platform,
   Illumina HumanHT-12 V4.0 expression beadchip). The probes were
   transformed into the homologous gene symbol by means of the platform’s
   annotation information. The [195]GSE43292 dataset contained 32
   non-atherosclerotic arterial tissues samples and 32 atherosclerotic
   arterial tissues samples. The [196]GSE57691 dataset contained 10
   non-atherosclerotic arterial tissues samples and 9 atherosclerotic
   arterial tissues samples.

Intra-group data repeatability test

   The Pearson’s correlation test was performed to verify intra-group data
   repeatability in the per group. The R programming language was used to
   provide the software and operating environment for statistical analysis
   and drawing of graphs. Correlations between all samples from the same
   dataset were visualized using heat maps which were also drawn using R.
   Principal component analysis (PCA) is a commonly used method for sample
   clustering, and is often used for gene expression, diversity analysis,
   resequencing, and other sample clustering based on various variable
   information. The intra-group data repeatability of the dataset was
   tested by sample clustering analysis.

DEGs identified by GEO2R

   The GEO2R ([197]http://www.ncbi.nlm.nih.gov/geo/geo2r), is an online
   data analysis tool, and was used to screen the DEGs between
   non-atherosclerotic and atherosclerotic tissues. After establishing the
   differential experimental groups for one GEO series, GEO2R could
   execute a command to compare the differential classifications so that
   the DEGs would be identified. When the gene symbol corresponded to
   probe sets, the data was considered valuable and would be reserved. The
   values for statistical significance were set as P-value≤0.01 and Fold
   change (FC)≥1.5 [[198]45]. Volcano maps were drawn using the volcano
   plotting tool ([199]https://shengxin.ren). The DEGs were then screened
   by introducing the two datasets into the FunRich (functional enrichment
   analysis tool) ([200]http://www.funrich.org/). Venn diagrams were
   delineated using an online Venn tool
   ([201]http://bioinformatics.psb.ugent.be/webtools/Venn/), which could
   then be used to visualize common DEGs shared between the two datasets.
   The overlaps between differently expressed gene lists are performed by
   the Circos [[202]46]. Another useful representation is to overlap genes
   based on their functions or shared pathways. The overlaps between gene
   lists can be significantly improved by considering overlaps between
   genes sharing the same enriched ontology term. Only ontology terms that
   contain less than 100 genes are used to calculate functional overlaps
   to avoid linking genes using very general annotation.

Construction of weighted gene co-expression network analysis (WGCNA)

   The R package (version 3.5.0) was used to preprocess and normalize the
   raw data of [203]GSE43292. Ranked by P-value from small to large
   (including control and atherosclerosis samples), a total of 936 DEGs in
   the [204]GSE43292 were chosen for WGCNA. WGCNA is a systematic
   biological method used to describe the gene association modes among
   different samples, and it can be used to identify gene sets with highly
   synergistic changes, and identify alternate biomarkers or therapeutic
   targets based on the coherence of gene sets and the correlation between
   gene sets and phenotypes. The WGCNA could use thousands of the most
   varied genetic information to identify the sets of genes of interest
   and to conduct significant association analysis with phenotypes. The
   procedure of WGCNA consists of four steps, including (a) identification
   of modules: hierarchical clustering analysis was conducted based on
   weighted correlation, and different gene modules were obtained
   according to the results of segmentation and clustering according to
   the set standards, which were represented by branches and different
   colors of the cluster tree; (b) construction of the gene co-expression
   network; (c) studying module relationships; (d) calculation of the
   correlation between gene modules and phenotypes, and the modules
   related to clinical traits were identified

Functional annotation of DEGs by DAVID and metascape analyses

   DAVID ([205]https://david.ncifcrf.gov/home.jsp) (version 6.8), is an
   online analysis tool suite with the function of Integrated Discovery
   and Annotation, that provides typical batch annotation and gene-Gene
   Ontology (GO) term enrichment analysis to highlight the most relevant
   Gene Ontology (GO) terms associated with a given gene list
   [[206]47–[207]49]. The Kyoto Encyclopedia of Genes and Genomes (KEGG)
   ([208]https://www.kegg.jp/) is one of the most commonly used biological
   information databases worldwide, and aims to understand advanced
   functions and biological systems. At a molecular level, KEGG integrates
   large numbers of practical program database resources from
   high-throughput experimental technologies [[209]50]. GO is an ontology
   database widely used in bioinformatics, which covers three aspects of
   biology, including cellular components, molecular functions and
   biological processes [[210]51]. Finally, OmicShare
   ([211]http://www.omicshare.com/tools), an open data analysis platform,
   was used to visualize the enrichment analysis [[212]52]. The
   [213]metascape.org
   ([214]http://metascape.org/gp/index.html#/main/step1) was used to
   perform enrichment analysis for DEGs again. P<0.05 was considered to
   indicate a statistically significant difference.

Enrichment analysis by Gene Set Enrichment Analysis (GSEA)

   Gene Set Enrichment Analysis (GSEA) could analyze all sequenced genes
   in two sample groups, its input is a gene expression matrix in which
   genes are divided into two groups, and all genes are first sequenced
   and then used to indicate the changing trend of expression level of
   genes between the two groups. GSEA analyzes whether all genes of a
   priori defined set are enriched at the top or bottom of this sequence
   list. GO and KEGG pathway enrichment analysis were performed for
   identified DEGs using GSEA analysis. GSEA analysis was conducted on all
   sequenced genes of atherosclerosis tissues and normal artery tissues
   through GSEA software, after importing gene annotation files, reference
   function sets, and all gene data of both atherosclerosis tissues and
   normal artery tissues, the software would perform analysis and sequence
   the genes according to algorithm, thus a gene sequence list is
   obtained, then it will analyze position of all genes in the sequence
   list and accumulate them to get the enrichment score (ES), after
   standardizing the ES, we could get a comprehensive understanding of
   biological function of gene through enrichment of function sets. P<0.05
   was set as the cut-off criterion.

Construction and analysis of protein-protein interaction (PPI) network and
the analysis and mining of hub genes

   After importing the common DEGs into the Search Tool for the Retrieval
   of Interacting Genes (STRING; [215]http://string-db.org) (version 10.5)
   [[216]53], the online tool predicted and outlined the PPI network. The
   analysis of interactions between various proteins provide novel
   hypothesizes for the pathophysiological mechanisms governing the
   development of AS. In the present study the STRING database was used to
   construct the PPI network of DEGs, and the minimum required interaction
   score was a medium confidence of >0.4 [[217]45]. The score > 0.4 is an
   important restriction of the network. If the rule of score changed, the
   PPI maybe different. Cytoscape (version 3.6.1) is a free visualization
   software and was applied to visualize the PPI networks [[218]54,
   [219]55]. Based on the topology principles, the Molecular Complex
   Detection (MCODE) (version 1.5.1), a plug-in of Cytoscape, could
   discover the tightly coupled region [[220]56]. The Cytoscape software
   initially constructed the PPI network map. Secondly, MCODE identified
   the most important module of the network map. The criteria of MCODE
   analysis was a degree cut-off=2, MCODE scores >5, Max depth=100,
   k-score=2, and node score cut-off=0.2. When the degrees were set
   (degrees≥13) [[221]45], the hub genes were excavated. The clustering
   analysis of hub genes was performed using OmicShare.

The animal model construction

   A total of 20, 3-month-old New Zealand white rabbits were acquired from
   the Institute of Laboratory Animal Sciences, Chinese Academy of Medical
   Sciences (CAMS) & Peking Union Medical College. The rabbits were
   randomly divided into two groups. In the control group (CON group,
   n=10), rabbits were fed with standard rabbit chow (0% cholesterol), and
   did not undergo abdominal aortic balloon injury. In the atherosclerosis
   group (n=10), the rabbits were fed a high fat diet (6% bean oil and 1%
   cholesterol) for eight weeks, and underwent abdominal aortic balloon
   injury. The Animal Care and Use Committee of the Institute of
   Laboratory Animal Sciences, Chinese Academy of Medical Sciences and
   Peking Union Medical College (CAMS∆PUMC) authorized the experimental
   ethics agreement.

   With fasting 12h before surgery and unlimited drinking, the rabbits of
   atherosclerosis group were operated after anesthesia with 3% sodium
   pentobarbital solution (3mg/kg of animal body weight; provided by
   institute of laboratory animal sciences, CAMS&PUMC, Beijing, China).
   Then, along the right femoral artery, the surgeon opened the skin,
   separated the subcutaneous tissue layer by layer and freed the femoral
   artery(2-3cm). Furthermore, punctured the femoral artery, put in a 4F
   vessel sheath, expanded and pulled back the balloon for 3 times,
   ligatured the right femoral artery, and sutured skin. Finally, animals
   were taken 40,000 U penicillin after surgery for 5 days.

   Following euthanasia of rabbits with 3% pentobarbital solution
   (300mg/kg of animal body weight; provided by institute of laboratory
   animal sciences, CAMS∆PUMC, Beijing, China) and air embolization, and
   the indicators of judging death include breathing and cardiac arrest,
   pupil dilation, and nerve reflex disappeared. Then, the abdominal
   aortic tissues were dissected. The establishment of model animals is
   groping for ourselves. To evaluate atherosclerosis model, gross
   examination of abdominal aorta was performed by HE staining.

Proteomic analysis

   Rabbit abdominal aortic tissues were ground in liquid nitrogen and
   lysed by protein extraction buffer (8M urea, 0.1%SDS) containing
   additional protease inhibitor cocktail (Roche, Switzerland) and 1mM
   phenylmethylsulfonyl fluoride (Beyotime Biotechnology, China) on ice
   for 30 min and then centrifuged at 14,000 rpm for 15 min at 4°C. The
   supernatant was gathered and the proteins’ concentration was measured
   with BCA assay (Pierce, USA). The cell lysis was stored at -80 degrees
   Celsius before further processing. Samples are tested for quality by
   using BCA quantitative kit. iTRAQ (AB Sciex, USA) with different
   reporter ions (113-121 Da) were applied as isobaric tags for relative
   quantification. iTRAQ labeling was conducted on the basis of the
   instructions of manufacturer. C18 chromatographic column sample
   classification was carried out. A total of 40 fractionations of labeled
   peptides were further concatenated into 20 fractions, vacuum dried and
   deposited at -80 degrees Celsius until further LC-MS analysis. And the
   LC-MS/MS analysis was performed by Q Exactive mass spectrometer (Thermo
   Scientific, USA). The NCBI s RefSeq rabbit protein sequence database
   was searched by the Sequest algorithms with Proteome discoverer
   software (version 1.4) (Thermo Scientific, USA). And the DEGs were
   identified in our own data. The rule for statistical significance was
   that adj. P-value≤0.01 and Fold change (FC) ≥1.5.

The level of smooth muscle cell (SMC)

   Paraffin sections were made using the tissue samples of the abdominal
   artery. The paraffin sections were deparaffinized with water, sealed
   with hydrogen peroxide, then washed with double distilled water. SMC
   were detected by using immunohistochemistry after antigen retrieval.
   Anti-SMC monoclonal antibody was used to detect macrophagocytes.
   According to the instructions of the VECTASTAIN Elite ABC Kit (Vector
   Laboratories, Burlingame, CA, USA), the specific detection steps were
   performed as follows. First, antigen-fixed paraffin sections were
   washed with phosphate-buffered saline (PBS) two to three times (5
   min/times) and blocked with 10% goat serum (TransGen Biotech, Beijing,
   China) at 37°C for 20minutes. Second, removed the serum by using filter
   paper, and added YAP or TAZ Rabbit polyclonal antibody (Abcam,
   Cambridge, UK) dropwise, then incubated overnight at 4°C. Third, the
   sections were washed with PBS three times (5 min/time) and incubated
   with goat anti-rabbit monoclonal antibody at 37°C for 1 hour. Fourth,
   color development was performed with diaminobenzidine. Each paraffin
   section was photographed at 6 fields and counted. Also,
   immunofluorescence technology was made for detecting SMC level.

RT-qPCR assay

   Total RNA was extracted from atherosclerosis samples and control artery
   samples by the RNAiso Plus (Trizol) kit (Thermofisher, Massachusetts,
   America), and reverse transcribed to cDNA. RT-qPCR was performed using
   a Light Cycler® 4800 System with specific primers for TPM2. [222]Table
   6 presents the primer sequences used in the experiments. The RQ values
   (2−ΔΔCt, where Ct is the threshold cycle) of each sample were
   calculated, and are presented as fold change in gene expression
   relative to the control group. GAPDH was used as an endogenous control.

Table 6. Primers and their sequences for PCR analysis.

   Primer        Sequence (5′–3′)
   β-actin-hF CGCAGAAACGAGACGAGATTG
   β-actin-hR  GATGCTCGCTCCAACGACTG
   TPM2-hF    TCCACCAAGGAGGACAAATACG
   TPM2-hR    GTTGTTGAGTTCCAGCAGGGTC
   [223]Open in a new tab

Western blot analysis

   Abdominal aortic tissues were frozen in liquid nitrogen. Total protein
   was isolated in a lysis buffer, resolved by 10% sodium dodecyl sulfate
   polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto
   polyvinylidene fluoride (PVDF) membranes by electroblotting. TPM2
   protein was detected using an anti-TPM2 polyclonal antibody (1:500
   dilution, Bioss, Beijing, China). The bands were visualized with an
   enhanced chemiluminescence kit (Millipore, Billerica, MA) and analyzed
   with Image-Pro Plus 6.0 (Media Cybernetics, US).

The construction of neural network model

   The training group was randomly divided into calibration data and
   training data according to the proportion of 1:2.46. There were 13
   samples in the calibration data, and 32 samples in the training data.
   We used Matlab (version 8.3) to accomplish the normalization processing
   of variable values, network simulation, network training, and network
   initialization. The number of input neurons in input layer is same as
   the number of input variables, and the number is two. The hidden layer
   is designed as 1 layer, and the output layer is also designed for 1
   layer. One output variable is intima-media thickness. When training to
   2000 steps after repeated training, the falling gradient is 0, and the
   training speed is uniform. At the same time, the training error is
   0.0064465, and the R (relativity) value reached 0.99133.

Statistical analysis

   The results are presented as the mean ± standard error of the mean.
   When two groups were compared, an unpaired Student’s t-test was
   performed to determine statistical significance. The Pearson-rho test
   was executed to compare the expression of hub genes and status of
   atherosclerosis for the correlation analysis. When any analytic results
   reached a liberal statistical threshold of p < 0.2 for each comparison,
   the risk factors were forced into the univariable linear regression
   model to confirm independent risk factors for the status of
   atherosclerosis. Univariate and multivariate logistic regression
   analysis was used to calculate the odds ratios (ORs) of each hub gene
   expression for the status of atherosclerosis. The receiver operator
   characteristic (ROC) curve analysis was performed to determine the
   usefulness of TPM2 for predicting AS. The Pearson-rho test was executed
   to compare intima-media thickness, the relative expression of TPM2 and
   SMC level for the correlation analysis. The cubic spline interpolation
   algorithm was implemented to analyze the high-risk warning range of
   atherosclerosis. The statistical analyses were conducted using SPSS
   software, version 21.0 (IBM Corp., Armonk, NY, USA). P<0.05 was
   considered to indicate a statistically significant difference.

Ethics approval

   All experiments were approved by Animal Care and Use Committee of the
   Institute of Laboratory Animal Sciences, Chinese Academy of Medical
   Sciences (CAMS) & Peking Union Medical College. All institutional and
   national guidelines for the care and use of laboratory animals were
   followed.

ACKNOWLEDGMENTS