Analysis of Hub Genes and the Mechanism of Immune Infiltration in Stanford Type a Aortic Dissection

Roles of pyroptosis and immune infiltration in aortic dissection

Introduction: Aortic dissection (AD) is often fatal, and its pathogenesis involves immune infiltration and pyroptosis, though the molecular pathways connecting these processes remain unclear. This study aimed to investigate the role of immune infiltration and pyroptosis in AD pathogenesis using bioinformatics analysis. Methods: Two Gene Expression Omnibus datasets and a Gene Cards dataset of pyroptosis-related genes (PRGs) were utilized. Immunological infiltration was assessed using CIBERSORT, and AD diagnostic markers were identified through univariate logistic regression and least absolute shrinkage and selection operator regression. Interaction networks were constructed using STRING, and weighted gene correlation network analysis (WGCNA) was employed to identify important modules and essential genes. Single-sample gene set enrichment analysis determined immune infiltration, and Pearson correlation analysis assessed the association of key genes with infiltrating immune cells. Results: Thirty-one PRGs associated with inflammatory response, vascular epidermal growth factor receptor, and Rap1 signaling pathways were identified. WGCNA revealed seven important genes within a critical module. CIBERSORT detected immune cell infiltration, indicating significant changes in immune cell infiltration and pyroptosis genes in AD and their connections. Discussion: Our findings suggest that key PRGs may serve as indicators for AD or high-risk individuals. Understanding the role of pyroptosis and immune cell infiltration in AD pathogenesis may lead to the development of novel molecular-targeted therapies for AD. Conclusion: This study provides insights into the molecular mechanisms underlying AD pathogenesis, highlighting the importance of immune infiltration and pyroptosis. Identification of diagnostic markers and potential therapeutic targets may improve the management of AD and reduce associated morbidity and mortality.

Downregulation of LILRB4 Promotes Human Aortic Smooth Muscle Cell Contractile Phenotypic Switch and Apoptosis in Aortic Dissection

Aortic dissection (AD) is a severe vascular disease with high rates of mortality and morbidity. However, the underlying molecular mechanisms of AD remain unclear. Differentially expressed genes (DEGs) were screened by bioinformatics methods. Alterations of histopathology and inflammatory factor levels in β-aminopropionitrile (BAPN)-induced AD mouse model were evaluated through Hematoxylin-Eosin (HE) staining and Enzyme-linked immunosorbent assay (ELISA), respectively. Reverse transcription quantitative real-time polymerase chain reaction was performed to detect DEGs expression. Furthermore, the role of LILRB4 in AD was investigated through Cell Counting Kit-8 (CCK-8), wound healing, and flow cytometry. Western blotting was employed to assess the phenotypic switch and extracellular matrix (ECM)-associated protein expressions in platelet-derived growth factor-BB (PDGF-BB)-stimulated in vitro model of AD. In the AD mouse model, distinct dissection formation was observed. TNF-α, IL-1β, IL-8, and IL-6 levels were higher in the AD mouse model than in the controls. Six hub genes were identified, including LILRB4, TIMP1, CCR5, CCL7, MSR1, and CLEC4D, all of which were highly expressed. Further exploration revealed that LILRB4 knockdown inhibited the cell vitality and migration of PDGF-BB-induced HASMCs while promoting apoptosis and G0/G1 phase ratio. More importantly, LILRB4 knockdown promoted the protein expression of α-SMA and SM22α, while decreasing the expression of Co1, MMP2, and CTGF, which suggested that LILRB4 silencing promoted contractile phenotypic transition and ECM stability. LILRB4 knockdown inhibits the progression of AD. Our study provides a new potential target for the clinical treatment of AD.

A neural network model was constructed by screening the potential biomarkers of aortic dissection based on genes associated with pyroptosis

BACKGROUND

Aortic dissection (AD) is one of the crucial and common cardiovascular diseases, and pyroptosis is a novel cell delivery mechanism that is probably involved in the pathogenesis of various cardiovascular diseases. However, no study has investigated the role of pyroptosis in AD.

METHODS

We obtained two AD datasets, GSE153434 and GSE190635, from the Gene Expression Omnibus database. The differential expression of AD-related genes was determined by differential analysis, and their enrichment analysis was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Additionally, a protein-protein interaction network was established. Next, potential biomarkers were screened by Lasso regression analysis, and a neural network model was constructed. Finally, the potential biomarkers were validated by constructing a mouse model of AD.

RESULTS

A total of 1033 differentially expressed related genes were distinguished and these genes were mainly associated with the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) and mitogen-activated protein kinase signaling pathways. The Lasso regression results showed five potential biomarkers, namely platelet endothelial cell adhesion molecule-1 (PECAM1), caspase 4 (CASP4), mixed lineage kinase domain-like pseudokinase (MLKL), APAF1-interacting protein (APIP), and histone deacetylase 6 (HDAC6) and successfully constructed a neural network model to predict AD occurrence. The results showed that CASP4 and MLKL were highly expressed, whereas PECAM1 and HDAC6 were lowly expressed in AD samples, and no statistically significant difference was observed in APIP expression in AD samples.

CONCLUSION

Pyroptosis plays a crucial role in AD occurrence and development. Moreover, the five potential biomarkers identified in the present study can act as targets for the early diagnosis of AD in patients.

The Effect of N6-Methyladenosine Regulators and m6A Reader YTHDC1-Mediated N6-Methyladenosine Modification Is Involved in Oxidative Stress in Human Aortic Dissection

Aortic dissection (AD) develops pathological changes in the separation of the true and false aortic lumen, with high lethality. m6A methylation and oxidative stress have also been shown to be involved in the onset of AD. Through bioinformatics methods, three differentially expressed m6A regulators (YTHDC1, YTHDC2, and RBM15) were excavated from the GSE52093 dataset in the Gene Expression Omnibus (GEO) database, and functional enrichment analysis of the differentially expressed genes (DEGs) regulated by m6A regulators was performed. Then, the genes with oxidative stress-related functions among these genes were found. The protein interaction network of the oxidative stress-related genes and the competing endogenous RNA- (ceRNA-) miRNA-mRNA network were constructed. Among them, DHCR24, P4HB, and PDGFRA, which have m6A differences in AD samples, were selected as key genes. We also performed immune infiltration analysis, as well as cell-gene correlation analysis, on samples from the dataset. The results showed that YTHDC1 was positively correlated with macrophage M1 and negatively correlated with macrophage M2. Finally, we extracted AD and healthy aorta RNA and protein from human tissues that were taken from AD patients and patients who received heart transplants, performed quantitative real-time PCR (qRT-PCR) on YTHDC2 and RBM15, and performed qRT-PCR and western blot (WB) detection on YTHDC1 to verify their differences in AD. The mRNA and protein levels of YTHDC1 were consistent with the results of bioinformatics analysis and were downregulated in AD. Immunofluorescence (IF) was used to colocalize YTHDC1 and endothelial cell marker CD31. After knocking down YTHDC1 in human umbilical vein endothelial cells (HUVECs), reactive oxygen species (ROS) levels had a tendency to increase and the expression of peroxide dismutase SOD2 was decreased. This study provides assistance in discovering the role of m6A regulator YTHDC1 in AD. In particular, m6A modification participates in oxidative stress and jointly affects AD.

Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis

BACKGROUND

Thoracic acute aortic dissection (TAAD), one of the most fatal cardiovascular diseases, leads to sudden death, however, its mechanism remains unclear.

METHODS

Three Gene Expression Omnibus datasets were employed to detect differentially expressed genes (DEGs). A similar function and co-expression network was identified by weighted gene co-expression network analysis. The least absolute shrinkage and selection operator, random forest, and support vector machines-recursive feature elimination were utilized to filter diagnostic TAAD markers, and then screened markers were validated by quantitative real-time PCR and another independent dataset. CIBERSORT was deployed to analyze and evaluate immune cell infiltration in TAAD tissues.

RESULTS

Twenty-five DEGs were identified and narrowed down to three after screening. Finally, two genes, SLC11A1 and FGL2, were verified by another dataset and qRT-PCR. Function analysis revealed that SLC11A1 and FGL2 play significant roles in immune-inflammatory responses.

CONCLUSION

SLC11A1 and FGL2 are differently expressed in aortic dissection and may be involved in immune-inflammatory responses.

Applying multi-omics techniques to the discovery of biomarkers for acute aortic dissection

Acute aortic dissection (AAD) is a cardiovascular disease that manifests suddenly and fatally. Due to the lack of specific early symptoms, many patients with AAD are often overlooked or misdiagnosed, which is undoubtedly catastrophic for patients. The particular pathogenic mechanism of AAD is yet unknown, which makes clinical pharmacological therapy extremely difficult. Therefore, it is necessary and crucial to find and employ unique biomarkers for Acute aortic dissection (AAD) as soon as possible in clinical practice and research. This will aid in the early detection of AAD and give clear guidelines for the creation of focused treatment agents. This goal has been made attainable over the past 20 years by the quick advancement of omics technologies and the development of high-throughput tissue specimen biomarker screening. The primary histology data support and add to one another to create a more thorough and three-dimensional picture of the disease. Based on the introduction of the main histology technologies, in this review, we summarize the current situation and most recent developments in the application of multi-omics technologies to AAD biomarker discovery and emphasize the significance of concentrating on integration concepts for integrating multi-omics data. In this context, we seek to offer fresh concepts and recommendations for fundamental investigation, perspective innovation, and therapeutic development in AAD.

Identification of Immune-Related Gene Signature in Stanford Type A Aortic Dissection

Background: Stanford type A aortic dissection (ATAAD) is a common life-threatening event in the aorta. Recently, immune disorder has been linked to the risk factors that cause ATAAD at the molecular level. However, the specific immune-related gene signature during the progression is unclear.

Methods: The GSE52093 and GSE98770 datasets related to ATAAD from the Gene Expression Omnibus (GEO) database were acquired. The immune gene expression levels were analyzed by single sample gene set enrichment analysis (ssGSEA). The correlations between gene networks and immune scores were determined by weighted gene correlation network analysis (WGCNA). The different immune subgroups were finally divided by consensus clustering. The differentially expressed genes (DEGs) were identified and subsequent functional enrichment analyses were conducted. The hub genes were identified by protein–protein interaction (PPI) network and functional similarities analyses. The immune cell infiltration proportion was determined by the CIBERSORT algorithm.

Results: According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results. Sixty-eight immune-related DEGs (IRDEGs) between the two subgroups were enriched in inflammatory-immune response biological processes, including leukocyte cell–cell adhesion, mononuclear cell migration, and myeloid leukocyte migration. Among these IRDEGs, 8 genes (CXCR4, LYN, CCL19, CCL3L3, SELL, F11R, DPP4, and VAV3) were identified as hub genes that represented immune-related signatures in ATAAD after the PPI and functional similarities analyses. The proportions of infiltrating CD8 T cells and M1 macrophages were significantly higher in ATAAD patients in the immune-high group than the immune-low group.

Conclusion: Eight immune-related genes were identified as hub genes representing potential biomarkers and therapeutic targets linked to the immune response in ATAAD patients.

Role of Necroptosis and Immune Infiltration in Human Stanford Type A Aortic Dissection: Novel Insights from Bioinformatics Analyses

Background

Stanford type A aortic dissection (TAAD) is one of the most life-threatening cardiovascular emergencies with high mortality and morbidity, and necroptosis is a newly identified type of programmed cell death and contributes to the pathogenesis of various cardiovascular diseases. However, the role of necroptosis in TAAD has not been elucidated. This study was aimed at determining the role of necroptosis in TAAD using bioinformatics analyses.

Methods

The RNA sequencing dataset GSE153434 and the microarray dataset GSE52093 were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes of necroptosis (NRDEGs) were identified based on differentially expressed genes (DEGs) and necroptosis gene set. Gene set enrichment analysis (GSEA) was applied to evaluate the gene enrichment signaling pathway in TAAD. The STRING database and Cytoscape software were used to establish and visualize protein-protein interaction (PPI) networks and identify the key functional modules of NRDEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of NRDEGs were also performed. Additionally, Spearman correlations were used to construct the necroptosis-related transcription factor-target genes regulatory network, immune infiltration patterns were analyzed using the ImmuCellAI algorithm, and the correlation between immune cell-type abundance and NRDEGs expression was investigated. The expression levels of NRDEGs and immune infiltration were additionally verified in the GSE52093 dataset.

Results

We found that the necroptosis pathway was considerably enriched and activated in TAAD samples. Overall, 25 NRDEGs were identified including MLKL, RIPK1, and FADD, and among them, 18 were verified in the validation set. Moreover, GO and KEGG enrichment analyses found that NRDEGs were primarily involved in the tumor necrosis factor signaling pathway, nucleotide-binding oligomerization domain-like receptor signaling pathway, and interleukin-17 signaling pathway. The imbalance of Th17/Treg cells was identified in the TAAD samples. Furthermore, correlation analysis indicated that expression of NRDEGs was positively associated with proinflammatory immune-cell infiltrations and negatively associated with anti-inflammatory or regulatory immune-cell infiltrations.

Conclusions

The present findings suggest that necroptosis phenomenon exists in TAAD and correlates with immune cell infiltration, which indicate necroptosis may promote the development of TAAD through activating immune infiltration and immune response. This study paves a new road to future investigation of the pathogenic mechanisms and therapeutic strategies for TAAD.

Bioinformatics Analysis Reveals Cell Cycle-Related Gene Upregulation in Ascending Aortic Tissues From Murine Models

Thoracic aortic aneurysm and dissection (TAAD) is a high-risk aortic disease. Mouse models are usually used to explore the pathological progression of TAAD. In our studies, we performed bioinformatics analysis on a microarray dataset (GSE36778) and verified experiments to define the integrated hub genes of TAAD in three different mouse models. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network analyses, and histological and quantitative reverse transcription-PCR (qRT-PCR) experiments were used in our study. First, differentially expressed genes (DEGs) were identified, and twelve common differentially expressed genes were found. Second, genes related to the cell cycle and inflammation were enriched by using GO and PPI. We focused on filtering and validating eighteen hub genes that were upregulated. Then, expression data from human ascending aortic tissues in the GSE153434 dataset were also used to verify our findings. These results indicated that cell cycle-related genes participate in the pathological mechanism of TAAD and provide new insight into the molecular mechanisms of TAAD.

Comprehensive Analysis of Key m6A Modification Related Genes and Immune Infiltrates in Human Aortic Dissection

Objective

To identify the feature of N6-methyladenosine (m6A) methylation modification genes in acute aortic dissection (AAD) and explore their relationships with immune infiltration.

Methods

The GSE52093 dataset including gene expression data from patients with AAD and healthy controls was downloaded from Gene Expression Omnibus (GEO) database in order to obtain the differentially expressed genes (DEGs). The differentially methylated m6A genes were obtained from the GSE147027 dataset. The differentially expressed m6A-related genes were obtained based on the intersection results. Meanwhile, the protein-protein interaction (PPI) network of differentially expressed m6A-related genes was constructed, and hub genes with close relationships in the network were selected. Later, hub genes were verified by using the GSE153434 dataset. Thereafter, the relationships between these genes and immune cells infiltration were analyzed.

Results

A total of 279 differentially expressed m6A-related genes were identified in the GSE52093 and GSE147027 datasets. Among them, 94 genes were up-regulated in aortic dissection (AD), while the remaining 185 were down-regulated. As indicated by Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, these genes were mainly associated with extracellular matrix (ECM) and smooth muscle cells (SMCs). The seven hub genes, namely, DDX17, CTGF, FLNA, SPP1, MYH11, ITGA5 and CACNA1C, were all confirmed as the potential biomarkers for AD. According to immune infiltration analysis, it was found that hub genes were related to some immune cells. For instance, DDX17, FLNA and MYH11 were correlated with Macrophages M2.

Conclusion

Our study identifies hub genes of AD that may serve as the potential biomarkers, illustrates of the molecular mechanism of AD, and provides support for subsequent research and treatment development.

Construction and Integrated Analysis of Competitive Endogenous Long Non-Coding RNA Network in Thoracic Aortic Dissection

Background

Long non-coding RNAs (lncRNAs) can act as a competitive endogenous RNA (ceRNA) to regulate gene expression by sequestering the microRNA (miRNA). However, the lncRNA-miRNA-mRNA ceRNA network in thoracic aortic dissection (TAD) has been rarely documented.

Methods

Three Gene Expression Omnibus (GEO) datasets were used to detect differentially expressed mRNAs, miRNAs, and lncRNAs in TAD. Gene ontology and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted for the differentially expressed mRNAs. A protein–protein interaction network for differentially expressed mRNAs was also constructed, and hub genes were identified. We established a ceRNA network of TAD based on the differentially expressed miRNAs, mRNAs and lncRNAs, and verified our results using an independent dataset and quantitative real-time PCR (qRT-PCR).

Results

In TAD, 267 lncRNAs, 81 miRNAs, and 346 mRNAs were identified as differentially expressed. The established ceRNA network consisted of seven lncRNA nodes, three mRNA nodes, and three miRNA nodes, and the expression of miRNAs in TAD was opposite to that of lncRNAs and mRNAs. Subsequently, an independent GEO dataset and qRT-PCR were used to validate the expression of three mRNAs. In addition, the expression differences in SLC7A5, associated miRNA and lncRNA were verified. According to gene set enrichment analysis of SLC7A5, the most significant KEGG pathway was considerably enriched in spliceosome and pentose phosphate pathway.

Conclusion

We established a novel ceRNA regulatory network in TAD, which provides valuable information for further research in the molecular mechanisms of TAD.