Patterns of immune infiltration in stable and raptured abdominal aortic aneurysms: A gene-expression-based retrospective study

Construction of ferroptosis-related prediction model for pathogenesis, diagnosis and treatment of ruptured abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a dangerous cardiovascular disease, which often brings great psychological burden and economic pressure to patients. If AAA rupture occurs, it is a serious threat to patients' lives. Therefore, it is of clinical value to actively explore the pathogenesis of ruptured AAA and prevent its occurrence. Ferroptosis is a new type of cell death dependent on lipid peroxidation, which plays an important role in many cardiovascular diseases. In this study, we used online data and analysis of ferroptosis-related genes to uncover the formation of ruptured AAA and potential therapeutic targets. We obtained ferroptosis-related differentially expressed genes (Fe-DEGs) from GSE98278 dataset and 259 known ferroptosis-related genes from FerrDb website. Enrichment analysis of differentially expressed genes (DEGs) was performed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG). Receiver Operating characteristic (ROC) curve was employed to evaluate the diagnostic abilities of Fe-DEGs. Transcription factors and miRNAs of Fe-DEGs were identified through PASTAA and miRDB, miRWalk, TargetScan respectively. Single-sample gene set enrichment analysis (ssGSEA) was used to observe immune infiltration between the stable group and the rupture group. DGIdb database was performed to find potential targeted drugs of DEGs. GO and KEGG enrichment analysis found that DEGs mainly enriched in "cellular divalent inorganic cation homeostasis," "cellular zinc ion homeostasis," "divalent inorganic cation homeostasis," "Mineral absorption," "Cytokine - cytokine receptor interaction," "Coronavirus disease - COVID-19." Two up-regulated Fe-DEGs MT1G and DDIT4 were found to further analysis. Both single and combined applications of MT1G and DDIT4 showed good diagnostic efficacy (AUC = 0.8254, 0.8548, 0.8577, respectively). Transcription factors STAT1 and PU1 of MT1G and ARNT and MAX of DDIT4 were identified. Meanwhile, has_miR-548p-MT1G pairs, has_miR-53-3p/has_miR-181b-5p/ has_miR-664a-3p-DDIT4 pairs were found. B cells, NK cells, Th2 cells were high expression in the rupture group compared with the stable group, while DCs, Th1 cells were low expression in the rupture group. Targeted drugs against immunity, GEMCITABINE and INDOMETHACIN were discovered. We preliminarily explored the clinical significance of Fe-DEGs MT1G and DDIT4 in the diagnosis of ruptured AAA, and proposed possible upstream regulatory transcription factors and miRNAs. In addition, we also analyzed the immune infiltration of stable and rupture groups, and found possible targeted drugs for immunotherapy.

Identification of biomarkers for abdominal aortic aneurysm in Behçet's disease via mendelian randomization and integrated bioinformatics analyses

Behçet's disease (BD) is a complex autoimmune disorder impacting several organ systems. Although the involvement of abdominal aortic aneurysm (AAA) in BD is rare, it can be associated with severe consequences. In the present study, we identified diagnostic biomarkers in patients with BD having AAA. Mendelian randomization (MR) analysis was initially used to explore the potential causal association between BD and AAA. The Limma package, WGCNA, PPI and machine learning algorithms were employed to identify potential diagnostic genes. A receiver operating characteristic curve (ROC) for the nomogram was constructed to ascertain the diagnostic value of AAA in patients with BD. Finally, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were conducted. The MR analysis indicated a suggestive association between BD and the risk of AAA (odds ratio [OR]: 1.0384, 95% confidence interval [CI]: 1.0081-1.0696, p = 0.0126). Three hub genes (CD247, CD2 and CCR7) were identified using the integrated bioinformatics analyses, which were subsequently utilised to construct a nomogram (area under the curve [AUC]: 0.982, 95% CI: 0.944-1.000). Finally, the immune cell infiltration assay revealed that dysregulation immune cells were positively correlated with the three hub genes. Our MR analyses revealed a higher susceptibility of patients with BD to AAA. We used a systematic approach to identify three potential hub genes (CD247, CD2 and CCR7) and developed a nomogram to assist in the diagnosis of AAA among patients with BD. In addition, immune cell infiltration analysis indicated the dysregulation in immune cell proportions.

Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms

Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.

Blood immunological profile of abdominal aortic aneurysm based on autoimmune injury

Abdominal aortic aneurysm (AAA) occupies a large part of aorta aneurysm, and if there's no timely intervention or treatment, the risks of rupture and death would rise sharply. With the depth of research in AAA, more and more evidence showed correlations between AAA and autoimmune injury. Currently, a variety of bioactive peptides and cells have been confirmed to be related with AAA progression. Despite the tremendous progress, more than half researches were sampling from lesion tissues, which would be difficult to obtain. Given that the intrusiveness and convenience, serological test take advantages in initial diagnosis. Here we review blood biomarkers associated with autoimmune injury work in AAA evolution, aiming to make a profile on blood immune substances of AAA and provide a thought for potential clinical practice.

Markers of extracellular matrix remodeling and systemic inflammation in patients with heritable thoracic aortic diseases

Background

In approximately 20% of patients with thoracic aortic aneurysms or dissections a heritable thoracic aortic disease (HTAD) is suspected. Several monogenic connective tissue diseases imply high risk of aortic disease, including both non-syndromic and syndromic forms. There are some studies assessing inflammation and extracellular matrix remodeling in patients with non-hereditary aortic disease, but such studies in patients with hereditary diseases are scarce.

Aims

To quantify markers of extracellular matrix (ECM) and inflammation in patients with vascular connective tissue diseases versus healthy controls.

Methods

Patients with Loeys-Dietz syndrome (LDS, n = 12), Marfan syndrome (MFS, n = 11), and familial thoracic aortic aneurysm 6 (FTAA6, n = 9), i.e., actin alpha 2 (ACTA2) pathogenic variants, were recruited. Exome or genome sequencing was performed for genetic diagnosis. Several markers of inflammation and ECM remodeling were measured in plasma by enzyme immunoassays. Flow cytometry of T-cell subpopulations was performed on a subgroup of patients. For comparison, blood samples were drawn from 14 healthy controls.

Results

(i) All groups of HTAD patients had increased levels matrix metalloproteinase-9 (MMP-9) as compared with healthy controls, also in adjusted analyses, reflecting altered ECM remodeling. (ii) LDS patients had increased levels of pentraxin 3 (PTX3), reflecting systemic inflammation. (iii) LDS patients have increased levels of soluble CD25, a marker of T-cell activation.

Conclusion

Our data suggest that upregulated MMP-9, a matrix degrading enzyme, is a common feature of several subgroups of HTAD. In addition, LDS patients have increased levels of PTX3 reflecting systemic and in particular vascular inflammation.

Identification of key monocytes/macrophages related gene set of the early-stage abdominal aortic aneurysm by integrated bioinformatics analysis and experimental validation

Objective

Abdominal aortic aneurysm (AAA) is a lethal peripheral vascular disease. Inflammatory immune cell infiltration is a central part of the pathogenesis of AAA. It’s critical to investigate the molecular mechanisms underlying immune infiltration in early-stage AAA and look for a viable AAA marker.

Methods

In this study, we download several mRNA expression datasets and scRNA-seq datasets of the early-stage AAA models from the NCBI-GEO database. mMCP-counter and CIBERSORT were used to assess immune infiltration in early-stage experimental AAA. The scRNA-seq datasets were then utilized to analyze AAA-related gene modules of monocytes/macrophages infiltrated into the early-stage AAA by Weighted Correlation Network analysis (WGCNA). After that, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis for the module genes was performed by ClusterProfiler. The STRING database was used to create the protein-protein interaction (PPI) network. The Differentially Expressed Genes (DEGs) of the monocytes/macrophages were explored by Limma-Voom and the key gene set were identified. Then We further examined the expression of key genes in the human AAA dataset and built a logistic diagnostic model for distinguishing AAA patients and healthy people. Finally, real-time quantitative polymerase chain reaction (RT-qPCR) and Enzyme Linked Immunosorbent Assay (ELISA) were performed to validate the gene expression and serum protein level between the AAA and healthy donor samples in our cohort.

Results

Monocytes/macrophages were identified as the major immune cells infiltrating the early-stage experimental AAA. After pseudocell construction of monocytes/macrophages from scRNA-seq datasets and WGCNA analysis, four gene modules from two datasets were identified positively related to AAA, mainly enriched in Myeloid Leukocyte Migration, Collagen-Containing Extracellular matrix, and PI3K-Akt signaling pathway by functional enrichment analysis. Thbs1, Clec4e, and Il1b were identified as key genes among the hub genes in the modules, and the high expression of Clec4e, Il1b, and Thbs1 was confirmed in the other datasets. Then, in human AAA transcriptome datasets, the high expression of CLEC4E, IL1B was confirmed and a logistic regression model based on the two gene expressions was built, with an AUC of 0.9 in the train set and 0.79 in the validated set. Additionally, in our cohort, we confirmed the increased serum protein levels of IL-1β and CLEC4E in AAA patients as well as the increased expression of these two genes in AAA aorta samples.

Conclusion

This study identified monocytes/macrophages as the main immune cells infiltrated into the early-stage AAA and constructed a logistic regression model based on monocytes/macrophages related gene set. This study could aid in the early diagnostic of AAA.

Identification of biomarkers and analysis of infiltrated immune cells in stable and ruptured abdominal aortic aneurysms

Objectives

The mortality rate of abdominal aortic aneurysm (AAA) is extremely high in the older population. This study aimed to identify potential biomarkers of AAA and aortic rupture and analyze infiltration of immune cells in stable and ruptured AAA samples.

Methods

Raw data of GSE47472, GSE57691, and GSE98278 were downloaded. After data processing, the co-expression gene networks were constructed. Gene Ontology and pathway enrichment analysis of AAA- and aortic rupture-related gene modules were conducted using the Database for Annotation, Visualization, and Integrated Discovery. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used for further enrichment analysis. The CIBERSORT tool was used to analyze the relative abundance of immune cells in samples. Differentially expressed immune-related genes were analyzed between different samples. Predictive models were constructed via extreme gradient boosting, and hub genes were identified according to feature importance.

Results

Blue and yellow modules were significantly related to AAA, and genes in these modules were associated with the aortic wall and immune response, respectively. In terms of aortic rupture, the most relevant module was significantly enriched in the inflammatory response. The results of GSEA and GSVA suggested that immune cells and the inflammatory response were involved in the development of AAA and aortic rupture. There were significant differences in the infiltration of immune cells and expression levels of immune-related genes among different samples. NFKB1 might be an important transcription factor mediating the inflammatory response of AAA and aortic rupture. After the construction of a predictive model, CD19, SELL, and CCR7 were selected as hub genes for AAA whereas OAS3, IFIT1, and IFI44L were identified as hub genes for aortic rupture.

Conclusion

Weakening of the aortic wall and the immune response both contributed to the development of AAA, and the inflammatory response was closely associated with aortic rupture. The infiltration of immune cells was significantly different between different samples. NFKB1 might be an important transcription factor in AAA and aortic rupture. CD19, SELL, and CCR7 had potential diagnostic value for AAA. OAS3, IFIT1, and IFI44L might be predictive factors for aortic rupture.

Genome-Wide Identification of Immune-Related Alternative Splicing and Splicing Regulators Involved in Abdominal Aortic Aneurysm

The molecular mechanism of AAA formation is still poorly understood and has not been fully elucidated. The study was designed to identify the immune-related genes, immune-RAS in AAA using bioinformatics methods. The GSE175683 datasets were downloaded from the GEO database. The DEseq2 software was used to identify differentially expressed genes (DEGs). SUVA pipeline was used to quantify AS events and RAS events. KOBAS 2.0 server was used to identify GO terms and KEGG pathways to sort out functional categories of DEGs. The CIBERSORT algorithm was used with the default parameter for estimating immune cell fractions. Nine samples from GSE175683 were used to construct the co-disturbed network between expression of SFs and splicing ratio of RAS events. PCA analysis was performed by R package factoextra to show the clustering of samples, and the pheatmap package in R was used to perform the clustering based on Euclidean distance. The results showed that there were 3,541 genes significantly differentially expressed, of which 177 immune-related genes were upregulated and 48 immune-related genes were downregulated between the WT and WTA group. Immune-RAS events were mainly alt5P and IR events, and about 60% of it was complex splicing events in AAA. The WT group and the WTA group can be clearly distinguished in the first principal component by using the splicing ratio of immune-RAS events. Two downregulated genes, Nr4a1 and Nr4a2, and eight upregulated genes, Adipor2, Akt2, Bcl3, Dhx58, Pparg, Ptgds, Sytl1, and Vegfa were identified among the immune-related genes with RAS and DEGs. Eighteen differentially expressed SFs were identified and displayed by heatmap. The proportion of different types of cells and ratio of the average ratio of different cells were quite different. Both M1 and M2 types of macrophages and plasma cells were upregulated, while M0 type was downregulated in AAA. The proportion of plasma cells in the WTA group had sharply increased. There is a correlation between SF expression and immune cells/immune-RAS. Sf3b1, a splicing factor with significantly different expression, was selected to bind on a mass of immune-related genes. In conclusion, our results showed that immune-related genes, immune-RAS, and SFs by genome-wide identification were involved in AAA.

Identification of crucial genes involved in pathogenesis of regional weakening of the aortic wall

Background

The diameter of the abdominal aortic aneurysm (AAA) is the most commonly used parameter for the prediction of occurrence of AAA rupture. However, the most vulnerable region of the aortic wall may be different from the most dilated region of AAA under pressure. The present study is the first to use weighted gene coexpression network analysis (WGCNA) to detect the coexpressed genes that result in regional weakening of the aortic wall.

Methods

The GSE165470 raw microarray dataset was used in the present study. Differentially expressed genes (DEGs) were filtered using the “limma” R package. DEGs were assessed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. WGCNA was used to construct the coexpression networks in the samples with regional weakening of the AAA wall and in the control group to detect the gene modules. The hub genes were defined in the significant functional modules, and a hub differentially expressed gene (hDEG) coexpression network was constructed with the highest confidence based on protein–protein interactions (PPIs). Molecular compound detection (MCODE) was used to identify crucial genes in the hDEG coexpression network. Crucial genes in the hDEG coexpression network were validated using the GSE7084 and GSE57691 microarray gene expression datasets.

Result

A total of 350 DEGs were identified, including 62 upregulated and 288 downregulated DEGs. The pathways were involved in immune responses, vascular smooth muscle contraction and cell–matrix adhesion of DEGs in the samples with regional weakening in AAA. Antiquewhite3 was the most significant module and was used to identify downregulated hDEGs based on the result of the most significant modules negatively related to the trait of weakened aneurysm walls. Seven crucial genes were identified and validated: ACTG2, CALD1, LMOD1, MYH11, MYL9, MYLK, and TPM2. These crucial genes were associated with the mechanisms of AAA progression.

Conclusion

We identified crucial genes that may play a significant role in weakening of the AAA wall and may be potential targets for medical therapies and diagnostic biomarkers. Further studies are required to more comprehensively elucidate the functions of crucial genes in the pathogenesis of regional weakening in AAA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00200-1.

Screening of key biomarkers and immune infiltration in Pulmonary Arterial Hypertension via integrated bioinformatics analysis

This study aimed to screen key biomarkers and investigate immune infiltration in pulmonary arterial hypertension (PAH) based on integrated bioinformatics analysis. The Gene Expression Omnibus (GEO) database was used to download three mRNA expression profiles comprising 91 PAH lung specimens and 49 normal lung specimens. Three mRNA expression datasets were combined, and differentially expressed genes (DEGs) were obtained. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and the protein-protein interaction (PPI) network of DEGs were performed using the STRING and DAVID databases, respectively. The diagnostic value of hub gene expression in PAH was also analyzed. Finally, the infiltration of immune cells in PAH was analyzed using the CIBERSORT algorithm. Total 182 DEGs (117 upregulated and 65 downregulated) were identified, and 15 hub genes were screened. These 15 hub genes were significantly associated with immune system functions such as myeloid leukocyte migration, neutrophil migration, cell chemotaxis, Toll-like receptor signaling pathway, and NF-κB signaling pathway. A 7-gene-based model was constructed and had a better diagnostic value in identifying PAH tissues compared with normal controls. The immune infiltration profiles of the PAH and normal control samples were significantly different. High proportions of resting NK cells, activated mast cells, monocytes, and neutrophils were found in PAH samples, while high proportions of resting T cells CD4 memory and Macrophages M1 cell were found in normal control samples. Functional enrichment of DEGs and immune infiltration analysis between PAH and normal control samples might help to understand the pathogenesis of PAH.

Patterns of Immune Infiltration and the Key Immune-Related Genes in Acute Type A Aortic Dissection in Bioinformatics Analyses

Background

Immune-inflammatory mechanisms contribute greatly to the complex process leading to type A aortic dissection (TAAD). This study aims to explore immune infiltration and key immune-related genes in acute TAAD.

Methods

ImmuCellAI algorithm was applied to analyze patterns of immune infiltration in TAAD samples and normal aortic vessel samples in the GSE153434 dataset. Differentially expressed genes (DEGs) were screened. Immune-related genes were obtained from overlapping DEGs of GSE153434 and immune genes of the ImmPort database. The hub genes were obtained based on the protein–protein interaction (PPI) network. The hub genes in TAAD were validated in the GSE52093 dataset. The correlation between the key immune-related genes and infiltrating immune cells was further analyzed.

Results

In the study, the abundance of macrophages, neutrophils, natural killer T cells (NKT cells), natural regulatory T cells (nTreg), T-helper 17 cells (Th17 cells) and monocytes was increased in TAAD samples, whereas that of dendritic cells (DCs), CD4 T cells, central memory T cells (Tcm), mucosa associated invariant T cells (MAIT cells) and B cells was decreased. Interleukin 6 (IL-6), C-C motif chemokine ligand 2 (CCL2) and hepatocyte growth factor (HGF) were identified and validated in the GSE52093 dataset as the key immune-related genes. Furthermore, IL-6, CCL2 and HGF were correlated with different types of immune cells.

Conclusion

In conclusion, several immune cells such as macrophages, neutrophils, NKT cells, and nTreg may be involved in the development of TAAD. IL-6, CCL2 and HGF were identified and validated as the key immune-related genes of TAAD via bioinformatics analyses. The key immune cells and immune-related genes have the potential to be developed as targets of prevention and immunotherapy for patients with TAAD.

Combining Bioinformatics Techniques to Study the Key Immune-Related Genes in Abdominal Aortic Aneurysm

Approximately 13,000 people die of an abdominal aortic aneurysm (AAA) every year. This study aimed to identify the immune response-related genes that play important roles in AAA using bioinformatics approaches. We downloaded the GSE57691 and GSE98278 datasets related to AAA from the Gene Expression Omnibus database, which included 80 AAA and 10 normal vascular samples. CIBERSORT was used to analyze the samples and detect the infiltration of 22 types of immune cells and their differences and correlations. The principal component analysis showed significant differences in the infiltration of immune cells between normal vascular and AAA samples. High proportions of CD4⁺ T cells, activated mast cells, resting natural killer cells, and 12 other types of immune cells were found in normal vascular tissues, whereas high proportions of macrophages, CD8⁺ T cells, resting mast cells, and six other types of immune cells were found in AAA tissues. In the selected samples, we identified 39 upregulated (involved in growth factor activity, hormone receptor binding, and cytokine receptor activity) and 133 downregulated genes (involved in T cell activation, cell chemotaxis, and regulation of immune response mediators). The key differentially expressed immune response-related genes were screened using the STRING database and Cytoscape software. Two downregulated genes, PI3 and MAP2K1, and three upregulated genes, SSTR1, GPER1, and CCR10, were identified by constructing a protein-protein interaction network. Functional enrichment of the differentially expressed genes was analyzed, and the expression of the five key genes in AAA samples was verified using quantitative polymerase chain reaction, which revealed that MAP2K1 was downregulated in AAA, whereas SSTR1, GEPR1, and CCR10 were upregulated; there was no significant difference in PI3 expression. Our study shows that normal vascular and AAA samples can be distinguished via the infiltration of immune cells. Five genes, PI3, MAP2K1, SSTR1, GPER1, and CCR10, may play important roles in the development, diagnosis, and treatment of AAA.