Investigation into Common Gene Signatures and Molecular Mechanisms Linking Aortic Dissection and Arteriosclerosis

doi:10.21203/rs.3.rs-3292835/v1

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Investigation into Common Gene Signatures and Molecular Mechanisms Linking Aortic Dissection and Arteriosclerosis

https://doi.org/10.21203/rs.3.rs-3292835/v1

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Aortic Dissection is a life-threatening vascular condition that occurs suddenly and is linked to various diseases. The primary objective of this research is to examine the overlapping genes and plausible molecular mechanisms associated with both aortic dissection and arteriosclerosis. From the GEO database, we obtained expression chip data GSE52093 and GSE28829, which are associated with aortic dissection (AD) and atherosclerosis (AS). By employing analysis of differential gene expression, we were able to identify the genes that are commonly shared by both diseases. Afterward, we utilized a PPI network and conducted a Cytoscape analysis, resulting in the identification of 9 central genes. Additionally, we performed a module-trait analysis using the GSE52093 and GSE28829 datasets, employing the WGCNA method, which led to the discovery of crucial genes linked to both conditions. The accuracy of the findings was confirmed through the utilization of independent datasets and real-time quantitative fluorescence PCR. Through PPI and WGCNA analysis of expression chip data GSE52093 and GSE28829 of AD and AS, we finally determined CNN1 and MYO1F as intersection genes. In RT-qPCR validation, the expression of CNN1 was consistent with our analysis results. Surprisingly, the expression of MYO1F did not show any statistically significant difference. However, the single-gene GSEA enrichment analysis revealed a noteworthy finding: CNN1 was significantly enriched in the CYTOSOLIC_DNA_SENSING_PATHWAY pathway for both AD and AS. Through this research, we have identified promising biomarker genes that are linked to both AD and AS diseases. Additionally, we have uncovered intricate molecular mechanisms and pathways associated with these conditions. These findings offer novel perspectives for advancing research and treatment approaches for both diseases.

Aortic Dissection

Atherosclerosis

CNN1

MYO1F

Aortic dissection, a highly prevalent and perilous vascular condition, often leads to fatalities before proper diagnosis (LeMaire et al. 2010; Pacini et al. 2013). The starting death rate is approximately 40%, and it rises by 1% every hour, leading to a final mortality rate of up to 90% (Klompas 2002). However, at present, there is a shortage of efficient medication options capable of managing or reducing the frequency and progression of aortic dissection. Hence, it is imperative to investigate the cellular and molecular foundation of this ailment to discover efficacious pharmaceutical approaches. It is well known that the main causes of aortic dissection involve abnormalities in the arterial wall structure, including connective tissue diseases of the vascular wall, hypertension, and atherosclerosis. The disease often occurs in areas of the aortic wall that are eroded and ruptured by atherosclerotic plaques. Atherosclerosis may result in the constriction, stiffening, and development of plaque in the arterial wall, rendering it delicate and susceptible, consequently elevating the chances of aortic dissection and aortic ulceration (Cho et al. 2004; Evangelista et al. 2019; Stanson et al. 1986). In medical research methods, bioinformatics is an extremely important research method that is now widely used in various fields (Bao et al. 2014). In particular, high-throughput chip platforms provide us with reliable methods for exploring genetic and epigenetic changes in disease and health states, and effectively identifying biomarkers for disease diagnosis and prognosis (Kulasingam and Diamandis 2008). WGCNA, also known as Weighted Gene Co-expression Network Analysis, is a powerful tool for identifying gene modules that exhibit strong correlations. These modules can then be linked to external traits, enabling the identification of potential biomarkers or therapeutic targets. This approach has demonstrated its efficacy in diverse biological contexts, enabling the identification of risk genes and disease phenotypes associated with various conditions (Zhu et al. 2020; Zou et al. 2019). Therefore, we will use bioinformatics methods to identify shared genes between aortic dissection and atherosclerosis and explore their shared molecular mechanisms.

To identify common genes between aortic dissection and atherosclerosis, we utilize expression data obtained from the GEO database, along with differential analysis and WGCNA techniques. Further validation of these results through external datasets and RT-qPCR experiments. Simultaneously, we will explore the fundamental molecular mechanisms and crucial pathways to gain fresh perspectives on the connection between aortic dissection and atherosclerosis, along with potential targets for the origin and progression, thereby offering enhanced scientific evidence for the prevention and treatment of these two ailments. Experimental materials and research procedures.

2.1. Materials

In this study, custom primers were designed and procured from TSINGKE Biotechnology Co., Ltd. The primer sequences are provided in Table 1. Trizol solution (TaKaRa, Japan); reverse transcription kit Prime Script™RT (TaKaRa, Japan); qPCR kit SYBR ® Premix Ex Taq™ (TaKaRa, Japan).

Table 1

The sequences of primers for RT-qPCR
Gene	Forward	Reverse
CNN1	GGTTCCGTGAAGAAGATCAATGA	CCACGTTCACCTTGTTTCCTTTC
MYO1F	TGTGAGAACCAGTGTGTCATCAT	ATATCTTTGACGTGCTGGACCTT
GAPDH	AGATCCCTCCAAAATCAAGTGG	GGCAGAGATGATGACCCTTTT

2.2. Sample Collection

Before starting the research, the Ethics Committee of The First Affiliated Hospital Chongqing Medical University granted ethical approval, and the study strictly followed the guidelines stated in the Helsinki Declaration. All participating patients or their relatives provided written informed consent. Individuals with Marfan syndrome, Ehlers-Danlos syndrome, or other acknowledged connective tissue disorders were not included in the research. Ultimately, eight patients diagnosed with acute aortic dissection were enrolled as participants. No patients who were registered had bicuspid aortic valve, aortic coarctation, Takayasu's arteritis, coronary artery disease, or disclosed cocaine usage. The dissection of the ascending aorta during surgery aided in locating and obtaining aortic wall samples that contained the tear at the dissected area. Adherent thrombus was meticulously removed from the specimens. Additionally, seven normal ascending aorta tissues from organ donors were used as controls. Total RNA was extracted from the obtained specimens for gene expression analysis, following standard protocols.

2.3. Total RNA Isolation and RT-qPCR

We collected 50 mg of aortic tissue and extracted total RNA using Trizol reagent (TaKaRa, Japan).To perform cDNA reverse transcription, 1 µg of RNA was utilized in a reaction volume of 20µl using the Prime Script™RT reagent Kit from TaKaRa, a company based in Japan. Following the initial RT-qPCR, conducted on a BioRad Real-Time PCR System from Bio-Rad in America, SYBR® Premix Ex Taq™ from TaKaRa in Japan was utilized. The levels of gene expression were measured using the 2^-ΔΔCt technique, with GAPD as the reference gene for normalization.

2.4. Data Sets and Analysis of Differentially-Expressed Genes

We acquired the four datasets from the NCBI Gene Expression Omnibus (GEO) repository. The selection of datasets was guided by the following criteria: 1) inclusion of gene expression profiles for both disease and control groups; 2) requirement for aortic dissection sequencing data to originate from aortic tissue, and atherosclerosis sequencing data to originate from atherosclerotic plaques or carotid artery plaques; 3) availability of raw data or raw data suitable for reanalysis. Finally, GSE52093, GSE28829, GSE147026 and GSE43292 were selected. The first two were used as discovery datasets and the last two as validation datasets (Table 2). Gene names and probes were matched according to their respective platform annotation files to obtain an expression profile file with sample names as row names and gene symbols as column names. The gene expression data were normalized using the preprocessCore software (version 1.28.0). For genes with multiple probes, the mean value was considered as the gene's expression level and log2 conversion was applied for further analysis of differential gene expression. The Limma software (version 3.17.0) in R was utilized to detect differentially expressed genes (DEGs) in the two discovery datasets using criteria of |log2 foldChange (FC)| >1.0 and adj P value < 0.05. To create heatmaps and volcano plots of DEGs in the database, the pheatmap and ggplot2 R packages were utilized for visualization of the DEGs. To determine the intersection of upregulated and downregulated genes, we took the intersection of differentially expressed genes (DEGs) obtained from GSE52093 and GSE28829.

Table 2

The information of four datasets from the NCBI Gene Expression Omnibus (GEO)
Group	GSE number	Platform	Source types	Disease
Discovery datasets	GSE52093	GLP10558	Aorta	AD
Discovery datasets	GSE28829	GLP570	Atheroma plaque	AS
Validation datasets	GSE147026	GLP24676	Aorta	AD
Validation datasets	GSE43292	GLP6244	Atheroma plaque	AS
AD: Aortic Dissection; AS: Arteriosclerosis

2.5. GO and Pathway Enrichment Analyses

To understand the potential biological roles and mechanisms of the shared genes associated with aortic dissection and atherosclerosis, we utilized the 'clusterProfiler' R software package to perform enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genes (KEGG) terms. The significance of GO terms, including biological processes (BPs), cellular components (CCs), molecular functions (MFs), and KEGG pathways, was determined by applying a P-value threshold of less than 0.05.

2.6. Construction And Analysis of Protein-Protein Interaction (PPI) Networks

A potential network of gene interactions was generated by intersecting the DEGs from the discovery datasets GSE52093(AD) and GSE28829(AS) and inputting them into the gene interaction search box (https //cn.string-db.org/). Nodes that were not interconnected were eliminated after the interaction score exceeded 0.15. To obtain a network diagram of upregulated and downregulated intersection genes, Cytoscape (version 3.9.1) was utilized for visualization. The MCODE plugin was used to calculate the core subnetwork within the interaction network. Moreover, the CytoHubba algorithm was employed to identify 9 essential genes by utilizing 12 analysis algorithms that calculate hub genes in protein interaction network diagrams. These algorithms encompass degree, edge permeability component (EPC), maximum neighborhood component (MNC), density of maximum neighborhood component (DMNC), maximum clique centrality (MCC), shortest path-based centrality, Bottleneck (BN), EcCentricity, Closeness, Radality, Betweenness, and Stress.

2.7. Constructing a Weighted Gene Co-expression Network and Detecting Meaningful Modules.

The R package 'WGCNA' was used to construct a weighted co-expression network on GSE52093(AD) and GSE28829(AS), aiming to investigate the connection between co-expression modules with high biological significance in these two datasets and aortic dissection and atherosclerosis. Initially, Outliers are eliminated by means of Hierarchical Clustering Analysis utilizing 'Hcluster' in R. Following the scale-free network criterion, the ideal soft power value (ranging from 1 to 20) was computed using the 'pickSoftThreshold' function within the 'WGCNA' package. Next, the soft power value between all genes and the gene correlation matrix was calculated using Pearson analysis, resulting in the construction of an adjacency matrix. Afterward, the adjacency matrix underwent a transformation into a topological overlap matrix (TOM) along with its corresponding dissimilarity (1-TOM). Moreover, a dendrogram for hierarchical clustering was created to cluster genes with similar expression patterns into distinct modules. The disease correlation was computed by representing the expression profile of each module using its corresponding module eigengenes (ME) and correlating them. Modules with high correlation coefficients with diseases were selected for subsequent analysis. The soft threshold was 4 in GSE52093 and 8 in GSE28829. minModuleSize = 60, deepSplit = 2.

2.8. Analyzing the ROC curve and validating the hub gene

In order to verify the precision of disease diagnosis, we performed receiver operating characteristic (ROC) analysis on the ultimate chosen genes obtained by combining protein-protein interaction (PPI) and weighted gene co-expression network analysis (WGCNA) findings from GSE52093 and GSE28829 datasets. The qROC package in R was utilized to conduct ROC curve analysis on two discovery datasets, two validation datasets, and qPCR data. A disease diagnosis was considered beneficial if the AUC was greater than 0.7. Violin plots generated by ggPlot2 were used to visualize the expression levels of the chosen core genes in four datasets and qPCR disease groups and control groups.

2.9. Single-gene GSEA Analysis

The software GSEA_4.3.2 was used for single-gene GSEA analysis. The final chosen gene was subjected to Single-gene GSEA analysis in the two discovery datasets, GSE52093(AD) and GSE28829(AS), and then categorized into groups based on high-expression and low-expression. P < 0.05 was used as the significant enrichment standard.

2.10. Forecasting the Regulatory Interactions between Transcription Factors (TFs) and Target Genes, And Small-Molecule Chemicals.

Transcription factors prediction were performed using ALGGEN – PROMO (upc.es). Initially, the genetic sequence of CNN1 was queried on the National Center for Biotechnology Information (nih.gov), and subsequently, the sequence of the promoter region was obtained. The promoter sequence was then inputted into the search box of ALGGEN - PROMO (upc.es). A dissimilarity margin less than or equal to 5% was considered an effective prediction. Cytoscape (version 3.9.1) was used for visualization. The predicted transcription factors were correlated with GSE52093 and GSE28829 discovery datasets using the R cor function, and a correlation scatter plot was drawn using the ggplot2 package. A strong correlation was determined when R was greater than 0.5 and P was less than 0.05. We utilized the Comparative Toxicogenomics Database | CTD (ctdbase.org) to conduct a search for small molecule chemicals, resulting in the acquisition of a collection of gene-chemical interaction pairs. Small molecule chemicals with experimental reports times greater than 3 were selected and the visualization of the gene-chemical interaction network was accomplished using Cytoscape (version 3.9.1).

Our experimental process is shown in Supplementary 1.

3.1. Identification of DEGs.

806 DEGs were identified in the GSE52093 dataset for aortic dissection, including 45 genes that were up-regulated and 341 genes that were down-regulated, when compared to samples of normal aortic tissue (Fig. 1a, 1b). The GSE28829 dataset on atherosclerosis revealed the presence of 571 differentially expressed genes (DEGs), with 410 genes showing up-regulation and 161 genes showing down-regulation when comparing early-stage plaques with late-stage arterial plaques (Fig. 1c, 1d). The comparison of aortic dissection tissue, late-stage arterial plaques, and early-stage arterial plaques using volcano plot and clustering heatmap of DEGs displayed notable distinctions (Fig. 1).

3.2. Discovering Common Gene Patterns in AD and AS

From the two datasets of aortic dissection and atherosclerosis, we identified 23 genes that were up-regulated and 46 down-regulated genes. These genes were found to intersect between the up-regulated and down-regulated gene sets (Fig. 2).

3.3. Enrichment Function Analysis of DEGs.

According to the results of the GO analysis, it was found that out of the 69 genes that were shared, the biological processes (BP) were primarily enriched in muscle organ development, muscle system process, regulation of smooth muscle cell proliferation associated with blood vessels, and various other processes. Within the cellular component (CC), the primary enrichment was observed in the actin cytoskeleton, sarcomere, Z disc, and various other subcellular sites. In the realm of molecular function (MF), they were primarily enriched in actin binding, the activity of phosphoric ester hydrolase, the activity of phosphoric diester hydrolase, and various other molecular functions (Fig. 3a). The analysis of pathway enrichment indicated that the commonly expressed genes primarily participated in processes such as contraction of vascular smooth muscle, synthesis and secretion of cortisol, interaction with extracellular matrix receptors, and various other pathways (Fig. 3b).

3.4. Enrichment Function Analysis of DEGs.

The 69 genes that were shared underwent analysis for protein-protein interactions (PPIs) utilizing the STRING online database (https //string-db.org/cgi/input.pl). This analysis resulted in the acquisition of a network relationship diagram consisting of 69 nodes and 194 edges. Next, the MCODE plugin of Cytoscape was utilized to acquire the core network diagram (Fig. 4a). According to the algorithm of CytoHubba, a Cytoscape plugin, the top 9 genes include CNN1, SPP1, SMTN, MYOCD, SLMAP, JAK2, CFL2, JAK2, and MYO1F (Fig. 4b).In two validation datasets, GSE147026 and GSE43292, there was a notable variation in gene expression among these 9 genes when comparing the disease group and the control group.

3.5. Weighted Gene Co-Expression Network Analysis.

A total of 27 modules were identified in GSE52093 by WGCNA, and 3 modules were identified in GSE28829. The module-trait relationship heat map was drawn using the Spearman correlation coefficient to evaluate the correlation between the modules and the disease. Among the 27 modules in GSE52093, the lightgreen and midnightblue modules showed a high correlation with AD and were determined to be AD-related modules (lightgreen module: r = 0.81, p = 0.001; midnightblue module: r = 0.86, p = 4e-04) (Fig. 5b). Among the 3 modules identified in GSE28829, the turquoise module showed a high correlation with AS and was determined to be an AS-related module (turquoise module: r = 0.76, p = 2e-06) (Fig. 5d). The lightgreen module contained 384 genes, the midnightblue module contained 1062 genes, and the turquoise module contained 119 genes. All three modules were positively correlated with the disease (Fig. 5).

3.6. Identification of genes finally screened

Upon identification of the three disease-associated modules in the WGCNA analysis, it was observed that the lightgreen and midnightblue modules shared 156 common genes, while the midnightblue and turquoise modules shared 9 common genes. Additionally, the lightgreen and turquoise modules exhibited two common genes, namely RGS1 and BCAT1. Four genes, namely CNN1, CNTN4, CKS2, and MYO1F, were identified as hub genes associated with both AD and AS in the lightgreen, midnightblue, and turquoise modules intersection (Fig. 6a). Subsequently, we intersected the 9 hub genes derived from the PPI network findings with the 4 hub genes obtained through WGCNA analysis, resulting in the identification of CNN1 and MYO1F as potential biomarker genes for AD and AS (Fig. 6b).

3.7. Validate Differential Expression RNAs in RT-qPCR and Validated Set.

To confirm our findings, we conducted verification of CNN1 and MYO1F expression in the Validated datasets GSE147026 and GSE43292. In the Validated datasets, it was found that there was a significant difference in the expression of CNN1 and MYO1F between the disease group and the control group, as indicated by the results shown in Fig. 7a, 7b, 7d, and f. Next, we conducted qPCR validation on 8 instances of aortic dissection samples and 7 healthy aortic tissues. Our analysis revealed that CNNI exhibited low expression in aortic dissection, aligning with the obtained results (Fig. 7c). Nonetheless, the expression of MYO1F showed no notable disparity between aortic dissection tissues and normal arterial tissues (Fig. 7d).

3.9. ROC curve.

We finally confirmed CNN1 as the diagnostic gene for aortic dissection and atherosclerosis. To explore the accuracy of CNN1 as a diagnostic biomarker for AD and AS, we plotted the ROC curve of CNN1. In two discovery datasets GSE52093 and GSE28829, q-PCR, and two validation datasets GSE147026 and GSE43292, the AUC of CNN1 was 1.0, 0.805, 1.0, and 0.877, respectively (Fig. 8). This indicates that CNN1 has good diagnostic value as a biomarker for AD and AS.

3.10. GSEA Analysis.

To explore the function of the CNN1 gene, single-gene GSEA analysis was performed in two discovery datasets. In the low-expression group of CNNI in GSE52093, 19 pathways were screened by analysis. In the low-expression group of CNNI in GSE28829, 20 pathways were screened, among which CYTOSOLIC_DNA_SENSING_PATHWAY was enriched in both datasets. We speculate that the low expression of CNN1 in AD and AS may be related to CYTOSOLIC_DNA_SENSING_PATHWAY (Fig. 9).

3.11. Prediction of Transcription Factors (TFs) And Chemicals-Gene Networks.

According to the ALGGEN-PROMO (upc.es) database, a set of 84 candidate transcription factors has been identified with the potential to bind to the CNN1 promoter region (Supplementary Fig. 2). Next, the correlation between CNN1 and the expression levels of these 84 transcription factors in the discovery dataset was examined. Among the 23 transcription factors in GSE52093, 14 exhibited a positive correlation with CNN1, while 9 showed a negative correlation, all with |R|>0.5 and P༜0.05. Within the dataset, GSE28829, a total of 8 transcription factors exhibited a correlation coefficient |R|>0.5 and P༜0.05. Out of these, 2 transcription factors showed a positive correlation with CNN1, while 6 transcription factors displayed a negative correlation with CNN1.In the discovery datasets for AD and AS, there was a positive correlation observed between the transcription factors RGR and AR, and CNN1.In the year AD, the correlation coefficients and p values for the relationship between RGR and AR to CNN1 were R = 0.786 P = 0.00243 and R = 0.672 P = 0.0167, correspondingly. In AS, the correlation coefficients between RGR and AR to CNN1 were R = 0.777 P = 7.19e-07 and R = 0.505 P = 0.00516, respectively (Fig. 10). We speculate that the transcription factors RGR and AR likely affect the expression of CNNI in AD and AS.

Using the CTD database, it was predicted that 125 small molecule chemicals interact with CNN1. Out of these, 56 chemicals were found to enhance the mRNA or protein expression of CNN1, whereas 58 chemicals were observed to reduce the mRNA or protein expression. We selected small molecule chemicals that had been tested more than three times to draw a gene-chemicals interaction network using Cytoscape. Six small-molecule chemicals can increase mRNA or protein expression of CNN1 and nine small-molecule chemicals that can decrease mRNA or protein expression of CNN1 (Fig. 11).

Utilizing information from the GEO database, this research examined the shared gene CNN1 in both AD and AS through the application of WGCNA and PPI. Additionally, independent GEO validation datasets and RT-qPCR were employed to confirm the expression variances of CNN1, aligning with our anticipated outcomes. CNN1 acts as a suppressive controller of smooth muscle contraction by inhibiting interactions between actomyosin (Liu and Jin 2016). Smooth muscle cells (SMCs) possess the ability to sustain various phenotypes and exhibit the traits of osteoblasts, chondrocytes, adipocytes, and macrophage foam cells. The transition from a contractile to an osteogenic/chondrogenic phenotype involves the formation of calcifying vesicles, decreased expression of mineralization inhibitors, and enhancement of calcification-prone matrix (Durham et al. 2016), which is strongly associated with atherosclerosis. This suggests that CNN1 may also be associated with atherosclerosis. The GO and KEGG enrichment analysis of the shared different genes between AD and AS in this research indicated that the significantly enriched GO terms comprised muscle system process, actin cytoskeleton, actin binding, and others. This finding aligns with the regulation of SMCs contractility by CNN1 (Liu and Jin 2016). Likewise, the primary KEGG pathway exhibited enrichment in Vascular smooth muscle contraction, aligning with the inhibitory effect of CNN1 on smooth muscle contraction through the inhibition of actomyosin interactions (Allen and Walsh 1994; Takahashi et al. 1988; Takahashi et al. 1986).

GSEA enrichment analysis showed that both aortic dissection and atherosclerosis were significantly enriched in the CYTOSOLIC_DNA_SENSING_PATHWAY. The activation of STING signaling occurred when nuclear and mitochondrial DNA damage in SMCs caused the release of DNA to the cytosol, leading to cell death through apoptosis and necroptosis (Luo et al. 2020). Furthermore, reports are indicating that the activation of the cGAS-STING pathway hinders the growth of endothelial cells and the restoration of blood vessels. Additionally, the activation of cGAS/STING inhibits the proliferation of endothelial cells by suppressing the Hippo/YAP signaling pathway. During inflammatory damage, Gasdermin D discharges mitochondrial DNA into the cytosol, triggering the DNA-sensing cGAS/STING pathway and consequently inhibiting endothelial cell (EC) proliferation and vascular restoration. The promotion of macrophage transformation is facilitated by the detection of DNA in the cytosol (Cao et al. 2018; Huang et al. 2020). The findings indicate that the pathway of detecting DNA in the cytosol is connected to the demise and decay of smooth muscle cells, as well as the conversion of macrophages. These processes play a crucial role in the development of aortic dissection and atherosclerosis. However, further experimental verification is needed.

The presence of RGR and AR transcription factors shows a positive association with CNN1 expression in both AD and AS, indicating that these two transcription factors could potentially influence the expression of CNN1. While there is presently no conclusive proof regarding the involvement of PGR and PR in the manifestation of CNN1, research has indicated that PGR provides a safeguarding function in pulmonary artery smooth muscle cells (PASMCs) when influenced by IL-6 (Hu et al. 2022). The occurrence and development of aortic dissection are probably closely associated with the role of vascular smooth muscle. In addition, smooth muscle cells have a significant impact on the development of atherosclerosis. The occurrence of aneurysms was found to be significantly higher in female and male mice, as well as male rats when treated with exogenous androgens during studies on the abdominal aorta in mice. This suggests that AR plays a crucial part in increasing the likelihood and danger of auxiliary aneurysm rupture (Cho et al. 2009; Henriques et al. 2008). Atherosclerosis progression can potentially be limited by targeting AR in specific cells (Huang et al. 2014). Transcription factors PGR and AR both play a certain role in aortic dissection and atherosclerosis, but how they specifically affect these two diseases still needs further experimental verification.

Our study has some limitations. All the data utilized in this research are sourced from the GEO database, and there is a possibility of data overlap that could potentially impact the analysis outcomes. Secondly, the sample size is small and comes from the same center, which may reduce accuracy. The specimens of atherosclerosis could not be collected. This experiment finally collected thoracic aorta tissue as experimental verification to study aortic dissection. In subsequent studies, complete experimental verification of dual disease models will be carried out.

Utilizing bioinformatics tools, our study identified distinctively expressed genes in aortic dissection and atherosclerosis, uncovering potential biomarkers and plausible pathogenic mechanisms for both conditions. These findings offer promising therapeutic targets for treating these diseases; however, additional experiments are required to validate our research outcomes.

AD, Aortic Dissection; AS, Atherosclerosis; PPI, Protein-Protein Interaction; GEO, Gene Expression Omnibus; WGCNA, Weighted Gene Co-expression Network Analysis; RT-qPCR, Real Time Quantitative Reverse Transcription Polymerase Chain Reaction; NCBI, National Center for Biotechnology Information; DEGs, Differentially Expressed Genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genes; ROC, Receiver Operating Characteristic; ME, Module Eigengenes; GSEA, Gene Set Enrichment Analysis; TFs, Transcription Factors; SMCs, Smooth Muscle Cells; ECs, Endothelial Cells, PASMCs, Pulmonary Artery Smooth Muscle Cells; AR, Androgen Receptor; PGR, Progesterone Receptor.

Availability of Data and Materials

Data supporting the findings of this study are available from the corresponding author upon reasonable request.

Author Contributions

Changying Li and Yue Shao designed the research study. Changying Li performed the research. Haoyu Ran did the literature research and wrote a part of the manuscript. Jun Luo wrote a part of the manuscript. Haoming Shi designed the tables and the figures. Cheng Zhang contributed to the idea of the manuscript. Qingchen Wu provided critical feedback. Yue Shao and Haoming Shi guided the writing. All the authors reviewed the manuscript and approved the submitted version. All authors contributed to editorial changes in the manuscript.

Ethics Approval and Consent to Participate

This study was performed in line with the principles of the Declaration of Helsinki, Approval was granted by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (K2023-285).

Acknowledgment

It was the GEO network and the First Affiliated Hospital of Chongqing Medical University that helped make this study possible. We are very grateful to the patients hospitalized at the First Affiliated Hospital of Chongqing Medical University for agreeing to our study. We are very grateful to the GEO database for providing free data. We would like to express our sincere thanks to Professor Changzhu Duan (Department of Cell Biology and Genetics, Center for Molecular Medicine and Oncology Research, Chongqing Medical University, Chongqing 400016, China) for furnishing a precious experimental platform that has facilitated the seamless progression of this research..

Funding

This study was supported by the Natural Science Foundation of China (NO. 82270506).

Conflict of Interest

The authors declare no conflict of interest.

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No competing interests reported.

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Investigation into Common Gene Signatures and Molecular Mechanisms Linking Aortic Dissection and Arteriosclerosis

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Materials

2.2. Sample Collection

2.3. Total RNA Isolation and RT-qPCR

2.4. Data Sets and Analysis of Differentially-Expressed Genes

2.5. GO and Pathway Enrichment Analyses

2.6. Construction And Analysis of Protein-Protein Interaction (PPI) Networks

2.7. Constructing a Weighted Gene Co-expression Network and Detecting Meaningful Modules.

2.8. Analyzing the ROC curve and validating the hub gene

2.9. Single-gene GSEA Analysis

2.10. Forecasting the Regulatory Interactions between Transcription Factors (TFs) and Target Genes, And Small-Molecule Chemicals.

3. Results

3.1. Identification of DEGs.

3.2. Discovering Common Gene Patterns in AD and AS

3.3. Enrichment Function Analysis of DEGs.

3.4. Enrichment Function Analysis of DEGs.

3.5. Weighted Gene Co-Expression Network Analysis.

3.6. Identification of genes finally screened

3.7. Validate Differential Expression RNAs in RT-qPCR and Validated Set.

3.9. ROC curve.

3.10. GSEA Analysis.

3.11. Prediction of Transcription Factors (TFs) And Chemicals-Gene Networks.

4. Discussion

5. Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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