scholarly journals Identification of key genes and pathways in abdominal aortic aneurysm by integrated bioinformatics analysis

2019 ◽  
Vol 48 (4) ◽  
pp. 030006051989443
Author(s):  
Yihai Liu ◽  
Xixi Wang ◽  
Hongye Wang ◽  
Tingting Hu
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Single Cell ◽  
Target Genes ◽  
Rna Seq ◽  
Ppi Network ◽  
Hub Genes ◽  
Microarray Profile ◽  
Abdominal Aortic ◽  
Key Genes

Objectives To identify key genes associated with abdominal aortic aneurysm (AAA) by integrating a microarray profile and a single-cell RNA-seq dataset. Methods The microarray profile of GSE7084 and the single-cell RNA-seq dataset were obtained from the Gene Express Omnibus database. Differentially expressed genes (DEGs) were chosen using the R package and annotated by Gene Ontology and Kyoto Encyclopedia of Genes and Genomics analysis. The hub genes were identified based on their degrees of interaction in the protein-protein interaction (PPI) network. Expression of hub genes was determined using single-cell RNA-seq analysis. Results In total, 507 upregulated and 842 downregulated DEGs were identified and associated with AAA. The upregulated DEGs were enriched into 9 biological processes and 10 biological pathways, which were closely involved in the pathogenesis and progression of AAA. Based on the PPI network, we focused on six hub genes, four of which were novel target genes compared with the known aneurysm gene database. Using single-cell RNA-seq analysis, we explored the four genes expressed in vascular cells of AAA: CANX, CD44, DAXX, and STAT1. Conclusions We identified key genes that may provide insight into the mechanism of AAA pathogenesis and progression and that have potential to be therapeutic targets.

Expression characteristics of exosomal long non-coding RNA in abdominal aortic aneurysm

2020 ◽  
Author(s):  
Chuang Li ◽  
Yubo Zhao ◽  
Shuwei Wan ◽  
Yaming Guo ◽  
Mingli Han ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Calcium Signaling ◽  
Target Genes ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Abdominal Aortic ◽  
Wide Range ◽  
Significant Difference ◽  
Internal Mechanism

Abstract Background and objective:Abdominal aortic aneurysm(AAA) is one of the important causes of morbidity and mortality in middle-aged and elderly people. Although the understanding of the physiology and pathology of AAA has been improved, the potential molecular mechanism of AAA is still unclear. The existing evidence confirms that exosomal lncRNAs have a wide range of biological functions, and its regulatory disorders are related to the occurrence of diseases such as AAA, but the internal mechanism is not clear. The main purpose of this study is to screen the differentially expressed lncRNAs in exosomes between normal people and patients with AAA and to understand its internal mechanism.Materials and methods:The plasma of a healthy control group and patients with abdominal aortic aneurysm was collected, and the lncRNAs of exosomes were extracted and sequenced. Differential expression was assessed by DEseq using read counts as input and chosen according to the criteria of |log2(fold change)| > 1 and adjusted p-value < 0.05. Based on the Kyoto encyclopedia of genes and genomes (KEGG) and biological pathway and gene ontology (GO) functional enrichment analysis, the target genes were analyzed, and the correlation between lncRNA and target genes was analyzed.Result:We screened 45 species differentially expressed lncRNAs and found pathway significantly related to these genes, namely metabolic pathways, calcium signaling pathways and protein processing in endoplasmic reticulum and They play a significant and important role in the metabolic process and the cell signaling.Conclusion:There was significant difference in expression of exosomal lncRNAs between normal subjects and AAA patients. LncRNAs in exosomes regulate in the progress of AAA by activating metabolic pathway and calcium signaling pathway, but the specific mechanism is not clear and needs to be further explored.

scholarly journals AAA 25. Cell-Specific Profiling of Transcriptional Landscape in Human Abdominal Aortic Aneurysm by Single-Cell RNA Sequencing

2019 ◽  
Vol 70 (5) ◽  
pp. e142-e143 ◽  
Author(s):  
Michele Silvestro ◽  
Tarik Hadi ◽  
Neal S. Cayne ◽  
Thomas S. Maldonado ◽  
Bruce E. Gelb ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Single Cell ◽  
Rna Sequencing ◽  
Abdominal Aortic ◽  
Single Cell Rna Sequencing ◽  
Transcriptional Landscape

scholarly journals Single-Cell Transcriptome Profiles Reveal Fibrocytes as Potential Targets of Cell Therapies for Abdominal Aortic Aneurysm

2021 ◽  
Vol 8 ◽  
Author(s):  
Bolun Li ◽  
Xiaomin Song ◽  
Wenjun Guo ◽  
Yangfeng Hou ◽  
Huiyuan Hu ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Single Cell ◽  
Aortic Rupture ◽  
Ang Ii ◽  
Cell Therapies ◽  
Abdominal Aortic ◽  
Life Threatening ◽  
Cell Transcriptome ◽  
Therapy Target

Abdominal aortic aneurysm (AAA) is potentially life-threatening in aging population due to the risk of aortic rupture and a lack of optimal treatment. The roles of different vascular and immune cells in AAA formation and pathogenesis remain to be future characterized. Single-cell RNA sequencing was performed on an angiotensin (Ang) II-induced mouse model of AAA. Macrophages, B cells, T cells, fibroblasts, smooth muscle cells and endothelial cells were identified through bioinformatic analyses. The discovery of multiple subtypes of macrophages, such as the re-polarization of Trem2+Acp5+ osteoclast-like and M2-like macrophages toward the M1 type macrophages, indicates the heterogenous nature of macrophages during AAA development. More interestingly, we defined CD45+COL1+ fibrocytes, which was further validated by flow cytometry and immunostaining in mouse and human AAA tissues. We then reconstituted these fibrocytes into mice with Ang II-induced AAA and found the recruitment of these fibrocytes in mouse AAA. More importantly, the fibrocyte treatment exhibited a protective effect against AAA development, perhaps through modulating extracellular matrix production and thus enhancing aortic stability. Our study reveals the heterogeneity of macrophages and the involvement of a novel cell type, fibrocyte, in AAA. Fibrocyte may represent a potential cell therapy target for AAA.

Identification of potential proteases for abdominal aortic aneurysm by weighted gene coexpression network analysis

Genome ◽  
2020 ◽  
Vol 63 (11) ◽  
pp. 561-575
Author(s):  
Hui Zhang ◽  
Dan Yang ◽  
Siliang Chen ◽  
Fangda Li ◽  
Liqiang Cui ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Network Analysis ◽  
Aortic Aneurysm ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Abdominal Aortic ◽  
T Cell Regulation ◽  
Coexpression Network Analysis

Proteases are involved in the degradation of the extracellular matrix (ECM), which contributes to the formation of abdominal aortic aneurysm (AAA). To identify new disease targets in addition to the results of previous microarray studies, we performed next-generation sequencing (NGS) of the whole transcriptome of Angiotensin II-treated ApoE−/− male mice (n = 4) and control mice (n = 4) to obtain differentially expressed genes (DEGs). Identified DEGs of proteases were analyzed using weighted gene coexpression network analysis (WGCNA). RT-qPCR was conducted to validate the differential expression of selected hub genes. We found that 43 DEGs were correlated with the expression of the protease profile, and most were clustered in immune response module. Among 26 hub genes, we found that Mmp16 and Mmp17 were significantly downregulated in AAA mice, while Ctsa, Ctsc, and Ctsw were upregulated. Our functional annotation analysis of genes coexpressed with the five hub genes indicated that Ctsw and Mmp17 were involved in T cell regulation and Cell adhesion molecule pathway, respectively, and that both were involved in general regulation of the cell cycle and gene expression. Overall, our data suggest that these ectopic genes are potentially crucial to AAA formation and may act as biomarkers for the diagnosis of AAA.

scholarly journals Predicting Abdominal Aortic Aneurysm Target Genes by Level-2 Protein-Protein Interaction

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0140888 ◽  
Author(s):  
Kexin Zhang ◽  
Tuoyi Li ◽  
Yi Fu ◽  
Qinghua Cui ◽  
Wei Kong
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Protein Interaction ◽  
Target Genes ◽  
Protein Protein Interaction ◽  
Abdominal Aortic ◽  
Level 2

scholarly journals Integrated analysis and the identification of a circRNA-miRNA-mRNA network in the progression of abdominal aortic aneurysm

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12682
Author(s):  
Ke Si ◽  
Da Lu ◽  
Jianbo Tian
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Hub Genes ◽  
Tissue Samples ◽  
Protein Protein Interaction ◽  
Abdominal Aortic ◽  
Protein Protein Interaction Network

Background Abdominal aortic aneurysm (AAA) is a disease commonly seen in the elderly. The aneurysm diameter increases yearly, and the larger the AAA the higher the risk of rupture, increasing the risk of death. However, there are no current effective interventions in the early stages of AAA. Methods Four gene expression profiling datasets, including 23 normal artery (NOR) tissue samples and 97 AAA tissue samples, were integrated in order to explore potential molecular biological targets for early intervention. After preprocessing, differentially expressed genes (DEGs) between AAA and NOR were identified using LIMMA package. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were conducted using the DAVID database. The protein-protein interaction network was constructed and hub genes were identified using the STRING database and plugins in Cytoscape. A circular RNA (circRNA) profile of four NOR tissues versus four AAA tissues was then reanalyzed. A circRNA-miRNA-mRNA interaction network was constructed after predictions were made using the Targetscan and Circinteractome databases. Results A total of 440 DEGs (263 up-regulated and 177 down-regulated) were identified in the AAA group, compared with the NOR group. The majority were associated with the extracellular matrix, tumor necrosis factor-α, and transforming growth factor-β. Ten hub gene-encoded proteins (namely IL6, RPS27A, JUN, UBC, UBA52, FOS, IL1B, MMP9, SPP1 and CCL2) coupled with a higher degree of connectivity hub were identified after protein‐protein interaction network analysis. Our results, in combination with the results of previous studies revealed that miR-635, miR-527, miR-520h, miR-938 and miR-518a-5p may be affected by circ_0005073 and impact the expression of hub genes such as CCL2, SPP1 and UBA52. The miR-1206 may also be affected by circ_0090069 and impact RPS27A expression. Conclusions This circRNA-miRNA-mRNA network may perform critical roles in AAA and may be a novel target for early intervention.

scholarly journals Identification of crucial genes in abdominal aortic aneurysm by WGCNA

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7873 ◽  
Author(s):  
Siliang Chen ◽  
Dan Yang ◽  
Chuxiang Lei ◽  
Yuan Li ◽  
Xiaoning Sun ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Normal Condition ◽  
Expression Patterns ◽  
Gene Clusters ◽  
Hub Genes ◽  
Hub Gene ◽  
Abdominal Aortic ◽  
Gene Modules ◽  
Medical Therapies

Background Abdominal aortic aneurysm (AAA) is the full thickness dilation of the abdominal aorta. However, few effective medical therapies are available. Thus, elucidating the molecular mechanism of AAA pathogenesis and exploring the potential molecular target of medical therapies for AAA is of vital importance. Methods Three expression datasets (GSE7084, GSE47472 and GSE57691) were downloaded from the Gene Expression Omnibus (GEO). These datasets were merged and then normalized using the “sva” R package. Differential expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were conducted. We compared the co-expression patterns between AAA and normal conditions, and hub genes of each functional module were identified. DEGs were mapped to co-expression network under AAA condition and a DEG co-expression network was generated. Crucial genes were identified using molecular complex detection (MCODE) (a plugin in Cytoscape). Results In our study, 6 and 10 gene modules were detected for the AAA and normal conditions, respectively, while 143 DEGs were screened. Compared to the normal condition, genes associated with immune response, inflammation and muscle contraction were clustered in three gene modules respectively under the AAA condition; the hub genes of the three modules were MAP4K1, NFIB and HPK1, respectively. A DEG co-expression network with 102 nodes and 303 edges was identified, and a hub gene cluster with 10 genes from the DEG co-expression network was detected. YIPF6, RABGAP1, ANKRD6, GPD1L, PGRMC2, HIGD1A, GMDS, MGP, SLC25A4 and FAM129A were in the cluster. The expression levels of these 10 genes showed potential diagnostic value. Conclusion Based on WGCNA, we detected 6 modules under the AAA condition and 10 modules in the normal condition. Hub genes of each module and hub gene clusters of the DEG co-expression network were identified. These genes may act as potential targets for medical therapy and diagnostic biomarkers. Further studies are needed to elucidate the detailed biological function of these genes in the pathogenesis of AAA.

scholarly journals Identification of novel genetic biomarkers and treatment targets for arteriosclerosis-related abdominal aortic aneurysm using bioinformatic tools

2021 ◽  
Vol 18 (6) ◽  
pp. 9761-9774
Author(s):  
Fang Niu ◽  
◽  
Zongwei Liu ◽  
Peidong Liu ◽  
Hongrui Pan ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Treatment Targets ◽  
Genetic Biomarkers ◽  
Abdominal Aortic

<abstract> <p>A large number of epidemiological studies have confirmed that arteriosclerosis (AS) is a risk factor for abdominal aortic aneurysm (AAA). However, the relationship between AS and AAA remains controversial. The objective of this work is to better understand the association between the two diseases by identifying the co-differentially expressed genes under both pathological conditions, so as to identify potential genetic biomarkers and treatment targets for atherosclerosis-related aneurysms. Differentially-expressed genes (DEGs) shared by both AS and AAA patients were identified by bioinformatics analyses of Gene Expression Omnibus (GEO) datasets GSE100927 and GSE7084. These DEGs were then subjected to bioinformatic analyses of protein-protein interaction (PPI), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the identified hub genes were further validated by qRT-PCR in AS (n = 4), AAA (n = 4), and healthy (n = 4) individuals. Differential expression analysis revealed a total of 169 and 37 genes that had increased and decreased expression levels, respectively, in both AS and AAA patients compared with healthy controls. The construction of a PPI network and key modules resulted in the identification of five hub genes (SPI1, TYROBP, TLR2, FCER1G, and MMP9) as candidate diagnostic biomarkers and treatment targets for patients with AS-related AAA. AS and AAA are indeed correlated; SPI1, TYROBP, TLR2, FCER1G and MMP9 genes are potential new genetic biomarkers for AS-related AAA.</p> </abstract>

scholarly journals MicroRNASeq Target Analysis and Validation by Real-Time PCR in Abdominal Aortic Aneurysm

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Willa Sasso ◽  
Leni Moldovan ◽  
Michael Murphy
Keyword(s):  
Risk Factor ◽  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Cigarette Smoke ◽  
Target Genes ◽  
Inflammatory Factors ◽  
Cigarette Smoke Exposure ◽  
Aortic Tissue ◽  
Epigenetic Changes ◽  
Abdominal Aortic

Background/ Objective: Abdominal aortic aneurysm (AAA) is an epigenetic event characterized by chronic inflammation and degeneration of the aortic wall leading to catastrophic rupture. Cigarette smoke exposure is the greatest environmental risk factor associated with AAA development. MicroRNAs (miRNA) regulate gene expression and may play a role in smoking-induced aortic inflammation. Epigenetic changes could include dysregulation of miRNA, causing post-transcriptional abnormalities pathogenic to AAA.     Methods: miRNA was extracted from plasma of 24 AAA patients and 7 risk factor matched (RFM) patients and analyzed by RNA sequencing. We compared previous (PS) and current smokers (CS) within and between both patient cohorts. Differential expression of miRNAs was analyzed by ANOVA (p≤ 0.05). Potential targets of significant differentially expressed miRNAs were predicted using cross-analysis of TargetScan and miRanda databases.     Results: Analysis revealed 7 significantly different miRNAs between AAA CS and AAA PS and 6 significantly different miRNAs between RFM CS and RFM PS. Of greatest significance, hsa-miR-223-3p was significantly downregulated as an effect of smoking cessation in AAA PS compared to AAA CS (p=0.000263), while also showing clinically relevant expression levels. Target genes of hsa-miR-223-3p include pro-inflammatory factors IL-6, TNFα, TGFβ, and MCP-1. Speculatively, as tissue levels of miR-223 tend to inversely correlate with plasma levels, we could hypothesize that the observed plasma upregulation of hsa-miR-223-3p in AAA CS contributes to the pro-inflammatory microenvironment of aortic tissue.     Conclusion: Cigarette smoke contributes to epigenetic changes impacting factors of immune regulation or inflammation, eventually leading to disease states such as AAA. Inflammatory-related hsa-miR-223-3p is upregulated in AAA CS, suggesting its potential role in the disease course.     Implications: Upregulation of hsa-miR-223-3p in AAA CS offers a link between disease state and the number one environmental factor attributed to AAA. This signature miRNA could serve as a biomarker for AAA or as a potential therapy target.  

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