Introduction

Aortic dissection (AD) is a fatal disease caused by the rupture of the aortic intima, which allows high-speed blood from the aortic lumen to enter the aortic wall through the rupture of the intima, resulting in tearing of the middle layer of the aortic wall.¹ The annual incidence rate of AD is 3.5-6 cases/100 000 people, but the mortality rate of AD is very high, often leading to sudden death.² According to the Stanford classification method, AD can be divided into type A aortic dissection (TAAD) and type B aortic dissection (TBAD).³ Currently, the main treatment for TAAD is emergency surgery with artificial vessel replacement, whereas the main treatment for TBAD is interventional thoracic endovascular aortic repair (TEVAR).⁴ Mortality is higher in the cases of TAAD, especially in areas with poorer health care.^5,6 Therefore, there is a need for new drug development, which requires a deeper understanding of the molecular mechanisms underlying the pathogenesis of TAAD.

The endoplasmic reticulum (ER) controls the quality of the proteins it produces and oversees maintaining protein homeostasis, which is critical for cell survival. Protein imbalances can lead to a variety of diseases, including metabolic, oncological, neurodegenerative, and cardiovascular diseases.^7,8 Under special circumstances, the normal oxidative environment within the intracellular ER is altered by specific strikes such as hypoxia and drug toxicity, calcium metabolism is disturbed, ER function is dysfunctional, and misfolded or unfolded proteins accumulate within the ER, known as endoplasmic reticulum stress (ERS).^9,10 Previous studies have shown that ERS plays a certain role in tumors, vascular calcification disease, liver steatosis, diabetes, and ischemic heart failure. However, the role of endoplasmic reticulum stress-related genes (ERSRGs) in TAAD disease has not yet been fully clarified.^11-16

In this study, we analyzed differentially expressed ERSRGs in TAAD based on the GEO database. Four key ERSRGs were identified and validated by quantitative reverse transcription PCR (qRT-PCR). The results of this study expand our understanding of TAAD and provide new potential diagnostic markers and potential therapeutic targets.

Methods

Artificial Intelligence Statement

We confirmed that we did not use artificial intelligence or any assisted technologies such as large language models in this paper.

Data Source

The Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) is a public functional genomics database. We utilized the keywords “aortic dissection” to search in the GEO database. The inclusion criteria for datasets were as follows: (1) The datasets must include TAAD and normal control aortic specimens and (2) the number of aortic dissection samples and normal control samples in the dataset must be greater than 5 cases. In the end, 2 aortic coarctation mRNA expression profiles, GSE52093 and GSE153434, were included in this study. The GSE52093 dataset (GPL10558 platform) contained 7 aortic specimens of TAAD and 5 normal control aortic specimens,¹⁷ and GSE153434 (GPL20795 platform) contained 10 aortic specimens of TAAD and 10 normal control aortic specimens.¹⁸

Analysis of Differentially Expressed Genes

We downloaded the dataset from the GEO database through the R software GEO query package using the limma package to standardize the data. The results of the analysis of differentially expressed genes (DEGs) were visualized by volcano plots using the ggplot2 R package,¹⁹ with the setting of |log fold change (FC)| >1 and an adjusted P value <.05. The significantly expressed genes were then visualized by heatmap using the ComplexHeatmap package.²⁰

Identification of Differentially Expressed Endoplasmic Reticulum Stress-Related Genes

Following the methods reported in the previous literature,²¹ we downloaded the ERSRGs from the GeneCards website, obtaining 1350 genes. These genes were intersected with differential genes obtained by GSE52093 to obtain differentially expressed ERSRGs in TAAD. Then, we plotted Venn plots.

Functional Enrichment Analysis and Protein–Protein Interaction

To explore the functions and metabolic pathways of differentially expressed ERSRGs, we conducted functional enrichment analysis using Metascape (version3.5) (https://metascape.org/gp/index.html#/main/step1). We input differentially expressed ERSRGs into the GeneMANIA database (http://genemania.org/), which automatically performs protein–protein interaction (PPI) analysis.²²

Immune Infiltration Analysis

Based on the GSE153434 dataset, immune infiltration in aortic wall tissue of TAAD patients was calculated by applying the R software CIBERSORT [v1.03] R package. Twenty-two markers of immune cells were provided by the CIBERSORTx website(https://cibersortx.stanford.edu/).²³

Construction of the Regulatory Network

Transcription factors (TFs) and microRNAs (miRNAs) have inhibitory and regulatory effects on target genes. Based on the NetworkAnalyst online database (https://www.networkanalyst.ca/NetworkAnalyst/), we constructed gene–miRNA and gene–TF regulatory networks for differentially expressed ERSRGs.²⁴ Simultaneously, we searched this database and set the search source as the Comparative Toxicogenomics Database (CTD) to construct the gene–chemical network.

Clinical Specimen Collection and Quantitative Reverse Transcription Polymerase Chain Reaction Validation

The inclusion criteria for TAAD cases were as follows: clearly diagnosed TAAD by aortic CTA angiography and underwent aortic vascular replacement surgery, aged over 18 years old. The exclusion criteria were as follows: Combined connective tissue disease, severe infection, or malignant tumor. Inclusion criteria for coronary artery bypass grafting cases: those who underwent coronary artery bypass grafting surgery due to coronary heart disease. Exclusion criteria: combined connective tissue disease, severe infection, or malignant tumor. This study included a total of 24 clinical specimens of the ascending aorta to further validate the differentially expressed ERSRGs: 12 TAAD patients who underwent aortic vascular replacement (AD group) and 12 coronary artery bypass grafting patients (aortic wall tissue at the site of ascending aortic perforation for graft vessel) as a non-AD control group (NAD group). The study was performed in accordance with the Declaration of Helsinki. All study protocols and experiments were approved by the Ethics Committee of The First Affiliated Hospital of Wannan Medical College, and prior informed consent was signed by the participants.

Total RNA was extracted from aortic wall tissue using TRIzol reagent (Invitrogen Company), and mRNA reverse transcription was performed using Vazyme’s HiScript II Q RTsuperpMix for qPCR (+gDNA wiper) kit (R223-01). qRT-PCR was performed using the AceQ qPCR SYBR Green Master Mix kit (Q111-02103), with the primer sequences shown in Table 1. Relative quantification was calculated using the 2^{– ΔΔCT} method.

Statistical Analysis

Continuous variables are presented as the mean and standard deviation. The statistical analysis of bioinformatics data in this study was conducted using R software (https://www.r-project.org/, version 4.2.1).²⁵ Statistical analysis was conducted on differences in gene expression levels using Prism 9 software and Student’s t-test. The diagnostic significance of differentially expressed ERSRGs in TAAD patients was evaluated using receiver operating characteristic curves (ROC) and the area under the ROC curve (AUC). All tests were considered statistically significant with P < .05.

Results

Analysis of Differentially Expressed Genes

The analysis was based on the GSE52093 dataset. The normalized sample date is shown in Figure 1A, while the PCA plot is shown in Figure 1B. After the analysis of DEGs (|log fold change (FC)|>1, adjusted P value <.05), we plotted volcano maps (Figure 1C) and heatmaps (Figure 1D), and obtained a total of 160 DEGs, including 94 upregulated genes and 66 downregulated genes (Supplementary File S1).

Identification of Differentially Expressed Endoplasmic Reticulum Stress-Related Genes

Twelve differentially expressed ERSRGs were obtained by taking the intersection of GSE52093 with the ERSRGs downloaded from the GeneCards website: ACTC1, CASQ2, REEP1, AKAP6, RYR2, JAK2, CAMK2G, WFS1, SERPINH1, CHEK1, CDKN3, SPP1, and CDKN3 (Figure 2, Table 2).

Functional Enrichment Analysis

Based on the Metascape database, we conducted a functional enrichment analysis of the 12 ERSRGs obtained. The 5 most significantly enriched GO terms and KEGG pathways were identified: (1) cellular response to caffeine; (2) negative regulation of molecular function; (3) Signaling by receptor tyrosine kinases; (4) supramolecular fiber organization; and (5) regulation of growth (Figure 3).

PPI Analysis

GeneMANIA was used to analyze 12 differentially expressed ERSRGs and their interacting proteins, and the results are shown in Figure 4, whose main function is calcium ion transport.

Validation in GSE153434

To evaluate the veracity of the differentially expressed ERSRGs in TAAD, we used GSE153434 to validate the expression levels of the top 5 (|log FC|) differentially expressed ERSRGs: ACTC1, CASQ2, CDKN3, SPP1, and REEP1. The expression levels of ACTC1, CASQ2, and REEP1 were significantly decreased in the AD group compared with the NAD group. In addition, the expression level of SPP1 was significantly increased in the AD group. However, the expression level of CDKN3 showed no significant difference between the 2 groups (Figure 5A). Area under the curve values for ACTC1, CASQ2, SPP1, and REEP1 were 0.92, 0.96, 0.89, and 1.00, respectively (Figure 5B).

Validation in Clinical Samples

We examined the expression of ACTC1, CASQ2, SPP1, and REEP1 using qRT-PCR in the AD (n = 12) and NAD (n = 12) groups of clinical samples, and the results again demonstrated that ACTC1, CASQ2, and REEP1 were expressed at low levels in TAAD, while SPP1 was highly expressed in TAAD, and the difference was statistically significant (Figure 6).

Immune Infiltrate Analysis

To assess the correlation between the 4 validated ERSRGs and immune infiltrating cells, we analyzed the distribution ratio of 22 types of immune cells within the TAAD aortic wall tissue based on the GSE153434 dataset (Figure 7A). Correlation analysis among various immune cells was also performed (Figure 7B), which showed that T cells CD8 were positively correlated with Treg cells and dendritic cells resting. Treg cells were positively correlated with dendritic cells resting. While NK cells activated were negatively correlated with macrophages M2 (P < .05). The expression of ACTC1 was negatively correlated with T cells CD8 and NK cells activated, and negatively correlated with NK cells resting (Figure 7C). The expression of REEP1 was negatively correlated with NK cells resting (Figure 7F). However, the expression of CASQ2 and SPP1 had no significant correlation with immune cell infiltration (Figure 7D,E).

Construction of Regulatory Networks

Then, we built the gene–miRNA (Figure 8A) and gene–TF regulatory networks (Figure 8B) of ACTC1, CASQ2, SPP1, and REEP1. The results revealed that a number of miRNAs and TFs were involved in the regulation of these 4 genes. We also built gene–chemical regulatory networks (Figure 8C), and the results suggested that multiple chemicals could regulate these 4 genes. These chemicals have the potential for the treatment of TAAD.

Discussion

Type A aortic dissection is a serious life-threatening disease that requires prompt diagnosis and emergency surgical treatment. However, the molecular mechanisms underlying its pathogenesis are still not fully elucidated. In this study, we included and analyzed 2 GSE datasets and identified 12 differentially expressed ERSRGs, and ACTC1, CASQ2, SPP1, and REEP1 were validated in clinical specimens. To further understand the possible role of these 4 differentially expressed ERSRGs, we analyzed their correlation with immune cell infiltration. Our study suggests that the 4 differentially expressed ERSRGs can serve as biomarkers for TAAD diagnosis and potential therapeutic targets.

Endoplasmic reticulum stress is closely associated with tumors, myocardial injury, and other diseases. Previous studies have demonstrated that ATF3 attenuates myocardial injury by regulating SPHK1 through the ERS pathway.²⁶ In colon cancer, oridonin increases cancer cell death via TP53-promoted ERS.²⁷ In this study, 4 differentially expressed ERSRGs, ACTC1, CASQ2, SPP1, and REEP1, were identified and validated. In cardiomyocytes and skeletal muscle, calsequestrin 2 (CASQ2) was found in the intracellular endoplasmic reticulum or sarcoplasmic reticulum and binds to calcium.²⁸ Catecholaminergic polymorphic ventricular tachycardia can be caused by homozygous point mutations in CASQ2.²⁹ CASQ2 controls the intracellular calcium concentration, which in turn modulates muscular contraction and activates cardiomyocytes. Additionally, ryanodine receptor 2 (RYR2) allows CASQ2, the main calcium reservoir protein in the heart, to release calcium into the cytosol in response to changes in calcium concentration.³⁰ RYR2 activity in myocytes is linked to electrical and contractile failure in the arrhythmogenic heart of an elderly person via mitochondrial function, calcium homeostasis, and excitation–contraction coupling.³¹ In the present study, we found lower expression of CASQ2 in TAAD patients and further validated this finding in clinical specimens. Our results suggest that alterations in calcium channels may influence the development of TAAD.

SPP1, also known as osteopontin, has previously been linked to degenerative aneurysmal illness, including abdominal aortic aneurysms,³² and has been demonstrated to play a role in a variety of blood vessel diseases.³³ SPP1, for example, is produced during inflammation and binds to transmembrane ligands to influence tissue remodeling pathways.³⁴ Furthermore, SPP1 participates in cellular migration, proliferation, apoptosis, and macrophage chemotaxis.³⁵ Previous research has linked SPP1 to abdominal and thoracic aneurysmal illness by upregulating matrix proteinases via NF-B, increasing tissue degradation.^36,37 A previous study showed that individuals with abdominal aortic aneurysm and AD had higher levels of SPP1 expression in the plasma and aortic wall than healthy controls. In AD patients, serum SPP1 levels correlate positively with MMP-2 levels. Thus, SPP1 is thought to be important in the development of aortic aneurysm and dissection.³⁸ Our findings suggest that REEP is highly expressed in TAAD, which is consistent with previous findings.

Expression-enhancing protein 1 (REEP1) is one of the proteins that make up mitochondria-associated endoplasmic reticulum membranes (MAM). Abnormalities in MAM proteins often lead to the incidence and progression of associated disorders such as diabetic kidney disease, neurodegenerative diseases, and type 2 diabetes mellitus.³⁹ The correlation between REEP1 and TAAD has not been previously reported, and we believe that the downregulation of REEP1 may be related to the development of TAAD, which needs to be further investigated in future experiments.

Vascular inflammation is a major contributor to the development of AD. Many studies have shown that a cascade of cytokines and inflammatory cells plays a role in disease progression. For example, the cytokine IL33 is an efficient AD diagnostic marker.⁴⁰ In addition to cytokines, inflammatory cells pass across the compromised endothelial tight connections, causing vascular inflammation and, eventually, AD.⁴¹ Endoplasmic reticulum stress is associated with a variety of pathological conditions associated with chronic inflammation, which can trigger inflammatory pathways and proinflammatory stimuli such as Toll-like receptor ligands, reactive oxygen species, and cytokines. These proinflammatory signals can initiate ERS and lead to activation of the unfolded protein response, which further amplifies the inflammatory response.⁴² Immunoinflammation-related mechanisms play an important role in AD disease, for example, in animal models, neutrophil-secreted MMP9 can induce AD.⁴³ Previous studies have shown that aortic arterial wall remodeling depends on complex interactions between cells, proinflammatory mediators, and MMPs, which are regulated by immune responses.^44,45 Treg cells will be significantly inhibited in immune cell activation response after blockade by CD25 monoclonal antibody, and IL-10 immunoreactivity will be abnormal, which in turn promotes aortic aneurysm development AD.⁴⁶ Previous studies have suggested that SPP1 is associated with macrophage recruitment in AD disease.⁴⁷ In our study, we found a correlation between ACTC1 and REEP1 and immune cell infiltration, however, CASQ2 and SPP1 did not show a significant correlation with immune cells, which may be due to the small sample size of the enrolled GEO dataset.

However, this study still has some limitations. First, clinical validation with large samples was lacking, and second, animal models were not constructed to study the specific molecular mechanisms of these genes. These limitations will be solved in our future research work.

Conclusion

In this study, we identified 12 differentially expressed ERSRGs by analyzing the GEO database. Four key differentially expressed ERSRGs may influence the development of TAAD by regulating ERS. These results expanded our understanding of TAAD, and the 4 genes are expected to be diagnostic markers and potential therapeutic targets.

Footnotes

Ethics Committee Approval: This study was performed according to the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. All study protocols and experiments were approved by the Ethics Committee of The First Affiliated Hospital of Wannan Medical College (Ethics No.: 2021-02).

Informed Consent: Written informed consent was obtained from the patients who agreed to take part in the study.

Peer-review: Externally peer-reviewed.

Author Contributions: Concept – W.Z.; Design – W.Z., D.Z.; Supervision – W.Z., J.N.; Resources– W.Z., J.N.; Materials – W.Z., D.Z., J.N.; Collection and/or Processing – J.N.; Analysis and/or Interpretation – W.Z.; Literature Search – D.Z., J.N.; Writing – W.Z.; Critical Review – D.Z.

Acknowledgements: The authors thank the researchers of the enrolled GEO dataset.

Declaration of Interests: The authors have no conflicts of interest to declare.

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