Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Nie H

PROVIDER: S-EPMC7758434 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Nie Han H Qiu Jiacong J Wen Si S Zhou Weimin W

Frontiers in genetics 20201210

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 corr ...[more]

PMID: 33362847

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Project description:ObjectivesAbdominal aortic aneurysm (AAA) is a cardiovascular disease with high mortality and pathogenesis closely related to various cell death types, e.g., autophagy, apoptosis and pyroptosis. However, the association between AAA and ferroptosis is unknown.MethodsGSE57691 and GSE98278 dataset were obtained from the Gene Expression Omnibus database, and a ferroptosis-related gene (FRG) set was downloaded from the FerrDb database. These data were normalized, and ferroptosis-related differentially expressed genes (FDEGs, AAA vs. normal samples) were identified using the limma package in R. FRGs expression was analyzed by Gene Set Expression Analysis (GSEA), and FDEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analyses using the clusterProfiler package in R and ClueGO in Cytoscape. Protein-protein interaction networks were assembled using Cytoscape, and crucial FDEGs were identified using CytoHubba. Critical FDEG transcription factors (TFs) were predicted with iRegulon. FDEGs were verified in GSE98278 set, and key FDEGs in AAA (compared with normal samples) and ruptured AAA (RAAA; compared with AAA samples) were identified. Ferroptosis-related immune cell infiltration and correlations with key genes were analyzed by CIBERSORT. Key FEDGs were reverified in Ang II-induced AAA models of ApoE-/- and CD57B/6J mice by immunofluorescence assay.ResultsIn AAA and normal samples, 40 FDEGs were identified, and the expression of suppressive FRGs was significantly downregulated with GSEA. For FDEGs, the GO terms were response to oxidative stress and cellular response to external stimulus, and the KEGG pathways were the TNF and NOD-like receptor signaling pathways. IL6, ALB, CAV1, PTGS2, NOX4, PRDX6, GPX4, HSPA5, HSPB1, and NCF2 were the most enriched genes in the crucial gene cluster. CEBPG, NFAT5, SOX10, GTF2IRD1, STAT1, and RELA were potential TFs affecting these crucial genes. Ferroptosis-related immune cells involved in AAA formation were CD8+ T, naive CD4+ T, and regulatory T cells (Tregs); M0 and M2 macrophages; and eosinophils. Tregs were also involved in RAAA. GPX4, SLC2A1, and PEBP1 expression was downregulated in both the RAAA and AAA samples. GPX4 and PEBP1 were more important in AAA because they influenced ferroptosis-related immune cell infiltration, and SLC2A1 was more important in RAAA.ConclusionsThis is the first study to show that ferroptosis is crucial to AAA/RAAA formation. The TNF and NOD-like signaling pathways and ferroptosis-related immune cell infiltration play key roles in AAA/RAAA. GPX4 is a key ferroptosis-related gene in AAA. Ferroptosis and related genes might be promising targets in the treatment of AAA/RAAA.

Project description:PurposeFerroptosis plays a crucial role in the development and progression of abdominal aortic aneurysm (AAA). The aim of this study was to identify differentially expressed genes associated with ferroptosis in AAA through bioinformatics analysis combined with experimental validation.Materials and methodsFirstly, the mRNA expression profile datasets GSE57691 and GSE47472 from Gene Expression Omnibus database were screened, and principal component analysis was carried out. Next, the R software (version 4.0.0) was used to analyze potentially differentially expressed genes associated with AAA and ferroptosis. Subsequently, protein-protein interaction analysis, gene ontology enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed on the selected candidate genes. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of the first five selected abnormal ferroptosis-related genes in clinical samples obtained from patients with AAA and healthy controls.ResultsBased on the information contained in the two datasets, a total of 20 differentially expressed ferroptosis-related genes (three upregulated genes and 17 downregulated genes) were selected. Protein-protein interaction analysis demonstrated interaction between these genes, while gene ontology enrichment analysis of ferroptosis genes with differential expression indicated that some enrichment items were associated with oxidative stress. The qRT-PCR results showed that the expression levels of interleukin-6 (IL-6), peroxiredoxin 1 (PRDX1), and stearoyl-CoA desaturase (SCD) were consistent with the bioinformatics prediction results obtained from the mRNA chip.ConclusionBioinformatics analysis identified 20 potential ferroptosis-related differentially expressed genes in AAA. Further verification by qRT-PCR showed that IL-6, PRXD1, and SCD might affect the process of AAA by regulating ferroptosis. Our results might assist in further understanding the pathogenesis of AAA and guiding treatment.

Project description:ObjectiveAbdominal 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.MethodsIn 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.ResultsMonocytes/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.ConclusionThis 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.

Dataset Information

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

Publications

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

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