Exploring the common pathogenesis of calcific aortic valve disease and atherosclerotic cardiovascular disease through bioinformatics analysis

doi:10.21203/rs.3.rs-2718479/v1

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Exploring the common pathogenesis of calcific aortic valve disease and atherosclerotic cardiovascular disease through bioinformatics analysis

https://doi.org/10.21203/rs.3.rs-2718479/v1

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Background

There are many similarities in the pathogenesis of calcified aortic valve disease (CAVD) and atherosclerotic cardiovascular disease (ASCVD). In this study, we aim to find potential key genes and pathways connected with both diseases, using bioinformatics approaches, we hope to search for some new targets to treat the two diseases.

Methods

We download gene expression profiles of CAVD (GSE12644, GSE51472) and ASCVD(GSE100927) from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (co-DEGs) for CAVD and ASCVD, we perform enrichment analysis of these co-DEGs and construct a PPI network. Subsequently, the hub genes were screened, and we validated the hub genes in other datasets. Finally, we validated the diagnostic value of these genes. Besides, we also performed an immune infiltration analysis.

Results

We eventually identified 55 co-DEGs from the two disease datasets which will be used for subsequent analysis. Enrichment analysis of these common genes showed that both chemokines and cytokines play an important role in CAVD and ASCVD. By using Cytohubba and MCODE plug-ins, we identified 14 hub genes, and after validation in other datasets, "CCR1", "TREM1", "MMP9", "CCL5", "TYROBP", "LY86", "LAPTM5" were selected as the final hub genes for this study. These 7 hub genes have high value in the diagnosis of CAVD and ASCVD. In addition, immune infiltration analysis also revealed dysregulation of immune cells in both CAVD and ASCVD.

Conclusions

Our study found common key genes of CAVD and ASCVD, which may be closely related to the pathogenesis of the two diseases. Our study identified key genes common to CAVD and ASCVD, which may be closely related to the occurrence and development of both diseases

calcific aortic valve disease

atherosclerotic cardiovascular disease

immune cell infiltration

lipoprotein(a)

inflammation

In the aging population, Calcific aortic valve disease (CAVD) is the most common valve disease. The morbidity and mortality rate are high for CAVD, and the number of CAVD patients will increase with time development[1]. The pathological mechanism of CAVD is progressive fibrous calcification remodeling and thickening of the aortic valve leaflets, and over time, the valve lesions can lead to severe obstruction of the cardiac outflow tract, resulting in clinical manifestations such as heart failure, angina pectoris, and syncope[2; 3]. The increased incidence of CAVD is associated with risk factors such as mitral valve phenotype, dyslipidemia, hypertension, diabetes mellitus, and increased body mass index[4]. The pathogenesis of CAVD is complex and diverse, involving genetic factors, chronic inflammation, lipoprotein deposition and oxidation, immune dysfunction, these factor will lead the valve interstitial cells take more properties of myofibrillar/osteoblast-like, which in turn exacerbates the fibrous calcification of the valve[4].

Arteriosclerotic cardiovascular disease (ASCVD) is a general term that includes a series of heart and blood vessel diseases, these diseases are the leading cause of death for people around the world[5]. Atherosclerosis is the leading cause of ASCVD, it is an inflammatory lesion characterized by the formation of fibrofatty lesions in the arterial wall. Dysfunction of endothelial cells is an important cause of atherosclerosis formation. Other pathophysiological processes such as oxidative stress, apoptosis, and hypoxia are also involved[6; 7; 8; 9]. Its risk factors mainly include dyslipidemia, hypertension, and diabetes mellitus[10].

It is not hard to find many similarities between CAVD and ASCVD in terms of pathogenesis and risk factors. A previous study found that the severity of aortic stenosis corresponded to an increase in carotid plaque thickness and intima-media thickening[11]. That suggests a close association between CAVD and ASCVD. Most studies in recent years on the commonalities between these two diseases have focused on lipoprotein(a)(lp(a)) which serves as a common pathogenic factor in the lesions of the two diseases. During ASCVD lesions, Lp(a) is oxidized at the arterial intima and generates reactive oxygen species, and the generated reactive oxygen species induce inflammation through many mechanisms including the formation of cytokines and promotion of apoptosis. Beyond the initial stage, macrophages that have phagocytosed large amounts of oxidized LDL form foam cells to promote the formation of atherosclerotic plaques. In CAVD lesions, when Lp(a) concentrations are high, lp(a)-carried oxidized phospholipids accumulate in damaged vessels and aortic valve leaflets, eventually leading to the formation of valve calcification.[12]. Lp(a) is considered to be a common target for the treatment of both diseases.

In addition, immune responses, both intrinsic and adaptive immunity, play an important role in both CAVD and ASCVD. In ASCVD, macrophages of the innate immune system play a major role in the atherosclerotic process after stimulation by ox-LDL. In addition, neutrophils, natural killer (NK)cells and others are also involved in the process of intrinsic immunity. The adaptive immune system is mainly triggered by the release of antigens from atherosclerotic plaques and is dominated by cells expressing MHC-II classes and various types of T cells[13]. For CAVD, activation of monocytes, macrophages and neutrophils during the intrinsic immune phase promotes the initiation and development of CAVD, but it remains unclear what factors stimulate the initiation of the intrinsic immune process[14].

In the adaptive immune phase, T cells, as being characteristic of aging and lesioned valves, may play a major role[15]. There are studies hypothesizing that T cells may be stimulated by antigens which formed by chronic inflammation or dead cells, resulting in adaptive immunity, and the exact mechanism remains to be further investigated.

In summary, we believe that CAVD and ASCVD may share more common pathological mechanisms, and for this reason we tried to use bioinformatics analysis to find co-DEGs between the two diseases, hoping to discover more common pathogenesis of the two lesions with the help of difference genes. Meanwhile, multiple immune cells are involved in the development of both lesions, and to further explore whether the immune cell types involved in the two diseases are the same, we performed immune infiltration analysis to explore this.

Identification of co-expressed DEGs

Figure 1 shows the flow chart of this study. In the combined CAVD dataset, a total of 209 DEGs were obtained, including 142 upregulated genes and 67 downregulated genes. The volcano plot and heatmap of CAVD DEGs are shown in Figs. 2A, B. A total of 434 DEGs were found in the ASCVD dataset, among which 322 genes were upregulated and 112 genes were downregulated. Figures 2C, D are the heatmap and volcano plots of ASCVD DEGs, respectively. The intersection of DEGs was obtained by Venn diagram (Fig. 2E), and a total of 56 differential genes were obtained. After excluding genes with opposite expression trends in the two disease datasets, a total of 55 co-expressed DEGs were used for the next analysis.

Functional enrichment analyses of co-DEGs

To clarify the function of these co-DEGs, we performed GO functional analysis and KEGG pathway analysis on these DEGs. GO analysis results showed that the DEGs were mainly enriched in Serine-type peptidase activity, serine hydrolase activity, Serine-type endopeptidase activity, Chemokine receptor binding, and Cytokine activity (Fig. 3A). KEGG pathway analysis showed that the pathway was primarily enriched in the Cytokine- Cytokine receptor interaction, Viral protein interaction with cytokine and cytokine receptor, and Chemokine signaling pathway (Fig. 3B). These results suggest that cytokines and chemokines play a role in CAVD and ASCVD.

Establishment of PPI network and preliminary screening of hub genes

PPI network analysis of co-DEGs was performed using the STRING database, and then we use CYTOSCAPE software to visualize the PPI network. As shown in Fig. 4A, the network includes 43 nodes and 404 interaction pairs. Through the MCODE plug-in of Cytoscape, we got three gene modules and these modules included a total of 23 differential genes and 130 interaction pairs (Fig. 4B, C, D). Using the CYTOHUBBA plug-in, the top 20 hub genes were calculated by selecting six of the algorithms (Table 1), after intersecting the hub genes obtained by the six algorithms, a total of 15 hub genes were obtained (Fig. 4E). Finally, after taking the intersection of the above genes with the MCODE module genes again, 14 hub genes were identified (Fig. 4F). They are "CCR1", "TREM1", "CCR5", "CCR2", "CCL4", "TLR2", "MMP9", "CCL5", "TYROBP", "LY86", "LCP2", "FCER1G", " LAPTM5", "CTSS". Pathway and process enrichment analysis of these 14 key genes using the METASCAPE website revealed that these key genes were mainly associated with leukocyte migration, myeloid leukocyte migration, myeloid leukocyte activation, positive regulation of cytokine production, Neutrophil degranulation (Fig. 4G). Most of these processes are associated with inflammatory and immune responses.

Table 1

The top 20 hub genes rank of six algorithms in cytoHubba.
Rank	MCC	MNC	EPC	Closeness	Betweenness	Stress
1	CCR1	TYROBP	TLR2	TYROBP	MMP9	MMP9
2	TREM1	TLR2	TYROBP	MMP9	TYROBP	TYROBP
3	CCR5	MMP9	CCR1	TLR2	TLR2	CORO1A
4	CCR2	CCR1	CCR5	CCR1	CORO1A	TLR2
5	CCL4	CCR5	CCR2	CCR5	CTSS	LCP2
6	TLR2	LCP2	MMP9	CCR2	LCP2	CCR5
7	MMP9	CCR2	LY86	TREM1	CCL5	PLA2G7
7	CCL5	CCL5	TREM1	LCP2	PLA2G7	CCR2
9	IL1RN	TREM1	CCL4	CCL5	CCR5	CTSS
10	TYROBP	LY86	CCL5	LY86	CCR1	CCR1
11	LY86	CCL4	LCP2	CCL4	CCR2	CCL5
12	LCP2	FCER1G	FCER1G	CTSS	SPP1	CCL4
13	FCER1G	MNDA	MNDA	FCER1G	TREM1	TREM1
14	CD300A	IL1RN	IL1RN	MNDA	CCL4	SPP1
15	MNDA	CTSS	GZMA	IL1RN	CD3D	CD52
16	LAPTM5	SPP1	CD52	SPP1	LY86	LAPTM5
17	CXCL16	CD52	LAPTM5	CD52	CD52	LY86
18	CTSS	LAPTM5	CTSS	GZMA	LAPTM5	CD3D
19	CD52	GZMA	CD300A	LAPTM5	GZMA	IL1RN
20	GZMA	CD300A	CXCL16	CORO1A	FCER1G	FCER1G

Immune infiltration analysis

Since the hub genes screened above are involved in immune regulation, we performed immune cell infiltration analysis hoping to find similarities in immune regulation between CAVD and ASCVD. Figure 5A and Fig. 6A respectively show the percentage of immune cells in CAVD and ASCVD. Observation of Fig. 5B and Fig. 6B revealed that the expression of Macrophages M0, Macrophages M2, and T cells gamma delta was higher in the lesioned group than in the control group in both CAVD and ASCVD datasets. Figure 5C and Fig. 6C show the correlation between 22 immune cells in CAVD and ASCVD, respectively. In conclusion, we found certain similarities in the regulation of immune cells in CAVD and ASCVD, which may provide a novel idea for the future treatment of both diseases.

Validation and screening of hub genes

In order to further verify the expression level of these hub genes and do further screening, we chose two other datasets to validate their expressions. Figure 7 and Fig. 8 respectively show the expression levels of hub genes in CAVD and ASCVD datasets. The results showed that in the CAVD dataset, the expression levels of "CCR1", "TREM1", "MMP9", "CCL5", "TYROBP", "LY86", and "LAPTM5" were significantly higher in calcified aortic valves compared with normal aortic valve tissue. In ASCVD dataset, the expression levels of "CCR1", "TREM1", "CCR5", "CCR2", "TLR2", "MMP9", "CCL5", "TYROBP", "LY86", "LCP2", "FCER1G", "LAPTM5", "CTSS" were significantly increased in atherosclerotic lesions compared with normal vascular tissues. After intersecting the genes with elevated expression in both datasets, we finally identified 7 hub genes. Functional analysis was performed again for these genes. GO analysis showed that these genes were mainly enriched in phosphatidylinositol phospholipase C activity, phospholipase C activity, Phosphoric diester hydrolase activity, Phospholipase activity, and Lipase activity (Fig. 9A). KEGG analysis showed that these genes were enriched in Viral protein interaction with cytokine and cytokine receptors, TNF signaling pathway, Chemokine signaling pathway, Lipid and atherosclerosis, and Human cytomegalovirus infection (Fig. 9B). The results of enrichment analysis of these hub genes showed that lipids and inflammation disorders were common mechanisms in the two diseases.

Validation of diagnostic value of hub genes

To verify the diagnostic value of the seven hub genes obtained from the above analysis, we constructed ROC curves and calculated the corresponding AUC for the expression levels of these genes in CAVD and ASCVD datasets, respectively, as shown in Fig. 10 and Fig. 11. Our results suggest that the hub genes screened in this study have a high diagnostic value in both CAVD and ASCVD.

As common cardiovascular diseases, CAVD and ASCVD share some common risk factors and histopathological manifestations[26]. In addition, the pathological mechanisms involved in the initiation of calcific aortic valve stenosis are similar to atherosclerosis, involving endothelial damage, lipid deposition, and inflammatory responses[27]. These co-factors suggest the possibility of therapeutic commonalities between the two diseases, and many previous trials have attempted to treat CAVD with statins, which have had brilliant success in the treatment of atherosclerotic disease, but the results are unsatisfactory[28; 29]. But treatments that target the common causative factors between the two diseases are promising. proprotein convertase subtilisin/kexin type 9(PCSK9) inhibitors have achieved exciting results as a popular drug in the treatment of atherosclerotic disease. Surprisingly, a post-hoc analysis of the FOURIER trial showed a 50% reduction in the incidence of calcific aortic stenosis over a median follow-up of 2.2 (1.8–2.5) years in patients randomized to evolocumab[30]. (The FOURIER trial was a large clinical trial to determine whether the PCSK9 inhibitor evolocumab could prevent cardiovascular events[31].) This raises expectations for its future.

Our immune infiltration analysis showed elevated expression of Macrophages M0, Macrophages M2, and T cells gamma delta cells in both diseases, and whether these immune cells may be common targets for intervention in both diseases deserves further investigation.

We hope that the seven key genes screened in our study and the results of the immune infiltration analysis may provide potential targets for the treatment of both diseases, targeting the common pathogenic genes or the disordered immune responses, and may open a new page for the treatment of both diseases.

Our aim in this study was to investigate the co-DEGs between CAVD and ASCVD in the hope of identifying other possible pathogenesis and corresponding targets that may hopefully be useful for the treatment of both diseases. In addition, the hub genes we screened may have the ability to predict CAVD and ASCVD.

C-C chemokine ligand 5(CCL5) is a member of the mammalian chemokine system, and CCL5 has been shown to induce the in vitro migration and recruitment of T cells, dendritic cells, eosinophils, NK cells, mast cells, and basophils. Because of the wide variety of cells that express and mediate the action of CCL5, CCL5 is involved in a variety of biological processes, from pathogen control to enhancement of inflammation in several ‘sterile' disorders, such as cancer and atherosclerosis[32]. CCL5 plays an important role in the atherosclerotic process. It has been shown that CCR5 and its ligand CCL5 are the major chemokine receptor-ligand pair for CD4 T cells homing into atherosclerotic aortas in Apoe−/− mice exacerbates atherosclerosis[33]. One study showed that in patients with degenerative aortic valve disease without coronary atherosclerosis, circulating CCL5 levels increased[34]. This suggests that CCL5 may be a potent biomarker for CAVD and ASCVD, and our ROC curves confirm the diagnostic value of this indicator. Whether CCL5 is involved in the process of calcific aortic stenosis lesions still needs to be further explored.

CC Chemokine receptor 1 (CCR1) is mainly expressed in immune cells, it is a receptor for several chemokines, mainly CCL3 and CCL5, and its main functions are mainly to mediate cellular infiltration, activation, and inflammation[35]. In atherosclerotic lesions, lack of CCR1 expression in leukocytes leads to excessive IFN-γ production and promotes lesion progression and inflammation[36]. At present, there is no relevant study to show whether CCR1 is involved in the pathological process of CAVD. We hypothesized that CCR1 might be involved in the inflammatory process of calcified aortic valve initiation.

The triggering receptor expressed on myeloid cells-1 (TREM1) is an immune receptor expressed on innate immune cells. TREM1 is a major player in the pathophysiology of inflammatory diseases, probably because it promotes the amplification of inflammatory signals[37]. It was found that TREM-1 promotes the progression of inflammation in atherosclerotic lesions and leads to reduced plaque stability[38]. Blocking TREM1 in animal models significantly ameliorates the progression of atherosclerosis[39]. TREM1 is currently considered one of the targets for the treatment of atherosclerosis. The role of TREM1 in CAVD is unclear. We think TREM1 is highly likely to play a role in CAVD. It is interesting to see whether TREM1 can be a therapeutic target for CAVD.

Matrix Metalloproteinase9 (MMP9) is a secreted multidomain enzyme. MMP9 is usually produced by selected cell types, such as keratinocytes, monocytes, etc. The primary function of MMP9 is to regulate cellular matrix composition[40]. One study found MMP9 expression in unstable coronary atherosclerotic plaques[41]. In addition, MMP9 can play a pro-fibrotic role in atherosclerosis[42]. MMP9 was previously found to be expressed in nonrheumatic aortic stenosis lesions[43]. A subsequent study found that in developing aortic stenosis lesions, MMP9 may facilitate the valve remodeling process.[44]. MMP9 plays a role in both CAVD and ASCVD, therefore, it is worthwhile to expect whether targeting MMP9 as a therapeutic target can treat these two diseases. Further studies are still needed to determine whether MMP9 has a commonality in the regulation of both diseases.

Tyrosine kinase binding protein(TYROBP)is also known as DNAX activation protein of 12 kDa(DAP12). It is mainly expressed on the surface of bone marrow cells and NK cells and plays an important role in non-immune functions[45]. It has been demonstrated that pravastatin ameliorates atherosclerosis in mice by inhibiting TREM1/DAP12[46]. There is a lack of studies on TYROBP and CAVD. Since TYROBP is a receptor for TREM1[47], We speculate that it may be associated with the initiation stage of aortic valve calcification lesions, and the exact mechanism remains to be further investigated.

Lymphocyte antigen 86 (LY86), also known as myeloid differentiation protein 1 (MD1), is a secreted glycoprotein that usually forms a complex called MD1-RP105 with radioprotein 105 (RP105). This complex mainly acts as a physiological negative regulator of the TLR4 signaling pathway[48]. So far, no study has found a relationship between LY86 and CAVD or ASCVD. Its role in the pathologic process of both diseases remains unknown. We speculate that it may play a role in the early stages of both diseases, and the exact mechanism needs to be further investigated.

Lysosomal-associated protein transmembrane 5(LAPTM5) is mainly expressed in immune cells and interacts with Nedd4, a member of the E3 ubiquitin ligase family, and has been shown to play a positive regulatory role in inflammatory signaling by secreting cytokines in macrophages[49]. In addition, recent studies have found that LAPTM5 appears to be involved in cardiovascular disease-related signaling pathways.[50]. Although there is no clear study, LAPTM5 is possible to be involved in the pathological process of atherosclerosis and calcified aortic valve stenosis by regulating a certain inflammatory signaling pathway. The complex mechanism involved in this process needs to be further explored.

Our results of immune infiltration analysis showed that the expression of M2 macrophages was increased in both diseases, which may be involved in inflammatory compensation and other pathological mechanisms. Enrichment analysis showed that the hub genes we obtained were mainly involved in inflammation and lipid disorders, like previous studies, indicating that the most common mechanism of CAVD and ASCVD is inflammation and lipid disorders. The ROC curve we constructed showed that all the hub genes we selected had high diagnostic values, which may provide certain help for disease screening and risk stratification in the future. We hope that our study can provide potential targets for future targeted therapy and prevention of these two diseases.

In conclusion, we identified co-DEGs of CAVD and ASCVD. We believe that some of the common pathogenic mechanisms of CAVD and ASCVD may be related to the hub genes screened in this study. We hope this study may provide new insight into the common pathogenesis of CAVD and ASCVD and may provide a new target for the treatment of the two diseases.

Our study has some limitations. First, the validation set we selected was relatively small in sample size, so some meaningful hub genes may be missed, and these preliminarily selected genes should be further verified with larger samples. Second, the interactions between hub genes and immune regulation need to be further studied. Third, the samples of the ROC curve to verify the diagnostic value are the lesion sites, so whether their diagnostic value can be applied in clinical practice needs further verification. Finally, relevant basic or clinical trials need to be supplemented to validate these results.

Gene Data Collection

Three datasets were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo)[16] after searching for keywords such as CAVD, ASCVD, etc. The GSE12644 dataset contains gene expression profiles of 10 calcified aortic valves and 10 non-calcified aortic valves. Five calcified aortic valves and five noncalcified aortic valves were selected from the GSE51472 dataset, both from the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array). The GSE100927 contains 69 vessels with atherosclerotic lesions and 35 vessels without atherosclerotic lesions from the GPL170771 platform (Agilent-039494 SurePrint G3 Human GE v2 8x60K). Because our gene expression profiles were obtained from publicly available online databases, our study did not require ethical approval.

Data processing and identification of DEGs

The GSE12644 and GSE51472 datasets were merged in R software, and batch effects were removed using the SVA package[17] after data normalization and merging. DEGs between the diseased and normal groups were identified using the limma package[18]. The criteria for identifying differential genes were |log₂FC| > 1 and P value < 0.05. Upregulated genes mean genes with log₂FC > 0, otherwise genes with log₂FC < 0 were considered downregulated. Differential genes in CAVD and ASCVD were visualized by the R platform in volcano plots and heatmaps. To visualize co-DEGs between CAVD and ASCVD, we use the VennDiagram package[19] to make a Venn diagram by which the differential genes common to both diseases were visualized. After the exclusion of common differential genes with opposite expression trends, the remaining co-DEGs were used for subsequent analysis.

Enrichment analysis of co-DEGs

To understand the biological functions and signaling pathways involved in these co-DEGs, we use the clusterProfiler package[20] to conduct Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Adjusted p-values < 0.05 were considered significant. We use bubble plots to visualize the GO and KEGG enrichment analysis results.

Establishment of PPI network and preliminary screening of hub genes

To explore the interactions between the co-DEGs, we establish a PPI network by using the Search Tool for Retrieval of Interacting Genes (STRING) 11.5 (http://string-db.org/)[21]. Interactions with a composite score above 0.4 were considered statistically significant. Cytoscape (version 3.9.1) (https://cytocape.org) [22] was utilized to present the results. We have used the Molecular Complex Detection technology (MCODE) plug-in to get the important genetic modules and count the information of genes in the module. We use the cytoHubba plug-in to identify key genes, by using six algorithms (MCC, MNC, EPC, Closeness, Betweenness, Stress) of cytoHubba to calculate the top 20 key genes and take the intersection of the obtained key genes. Finally, the module genes obtained by the MCODE plug-in, and the key genes obtained by the cytoHubba plug-in were intersected, and the obtained genes were used as the hub genes in this study. We later performed functional enrichment of the screened hub genes through the metascape (https://metascape.org)[23] which is a function annotated website.

Immune Infiltration Analysis

We performed an immune infiltration analysis for the two diseases. CIBERSORT is a calculation method that uses tissue gene expression profiling to determine the proportion of different immune cells, it is commonly used to determine the proportion of immune cells in diseased versus non-diseased tissues[24]. We use the "Cibersort. R" script to perform immune cell infiltration analysis. Immune infiltration analysis was performed separately in the GSE12644 dataset and the GSE100927 dataset. Use a barplot to visualize the proportion of each type of immune cell in different samples. Boxplots were used to visualize the proportions of different types of immune cells in diseased tissues and non-diseased tissues. The "Corrplot" R package was used to create heatmaps depicting the associations of the 22 invasive immune cells.

Identify key genes

To finally screen out hub genes, we validate the expression of the initially screened hub genes in the GSE83453 dataset and GSE43292 dataset respectively and screened the genes that were validated in both datasets as our final hub genes for further analysis. In GSE83453, 9 calcified aortic valves and 8 normal aortic tissues were selected. GSE43292 included 32 atherosclerotic lesions and 32 non-atherosclerotic lesions. In the validation process, the t-test was applied to data that conformed to a normal distribution in the validation dataset, and the Wilcoxon test was applied to data that did not conform to a normal distribution in the validation dataset. p-value < 0.05 was considered statistically significant.

Validation of the diagnostic value of hub genes

To evaluate the diagnostic value of hub genes for CAVD and ASCVD, we use the pROC package[25] to establish Receiver operator characteristic (ROC) curves. The area under the curve (AUC) and 95% confidence interval (CI) were calculated to assess their value. AUC༞0.7 is considered a relatively good diagnostic value.

Statistical Analysis

We used R Software (Version 4.2.1) to complete the statistical analysis in the article.

Ethics and Consent to Participate

Our study do not need ethics approval and consent to participate.

Author Contributions

WT-C and CL-W collected and analyzed the data and wrote the manuscript. XH-Q participated in manuscript writing and language proofreading. WQ-C contributed to reviewing and revising the manuscript. All the authors have agreed to the publication of the manuscript

Declaration of Interest Statement

The authors declare no potential conflict of interest.

Funding details

No funding was received for this study.

Acknowledgments

We are grateful for the data provided by the GEO database

Data availability statement

The datasets mentioned in this study are available on online repositories. The main text can find details of the repositories and how the dataset is obtained.

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No competing interests reported.

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Exploring the common pathogenesis of calcific aortic valve disease and atherosclerotic cardiovascular disease through bioinformatics analysis

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