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.

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 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 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 Expression gradient of metalloproteinases and their inhibitors from proximal to distal segments of abdominal aortic aneurysm

2021 ◽  
Author(s):  
Aleksandra Augusciak-Duma ◽  
Karolina L. Stepien ◽  
Marta Lesiak ◽  
Ewa Gutmajster ◽  
Agnieszka Fus-Kujawa ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Tissue Inhibitors Of Metalloproteinases ◽  
Abdominal Aortic ◽  
Extracellular Matrix Remodelling ◽  
Affected Tissue ◽  
Membrane Type

AbstractAbdominal aortic aneurysm refers to abnormal, asymmetric distension of the infrarenal aortic wall due to pathological remodelling of the extracellular matrix. The distribution of enzymes remodelling the extracellular matrix and their expression patterns in the affected tissue are largely unknown. The goal of this work was to investigate the expression profiles of 20 selected genes coding for metalloproteinases and their inhibitors in the proximal to the distal direction of the abdominal aortic aneurysm. RNA samples were purified from four lengthwise fragments of aneurysm and border tissue obtained from 29 patients. The quantities of selected mRNAs were determined by real-time PCR to reveal the expression patterns. The genes of interest encode collagenases (MMP1, MMP8, MMP13), gelatinases (MMP2, MMP9), stromelysins (MMP3, MMP7, MMP10, MMP11, MMP12), membrane-type MMPs (MMP14, MMP15, MMP16), tissue inhibitors of metalloproteinases (TIMP1, TIMP2, TIMP3, TIMP4), and ADAMTS proteinases (ADAMTS1, ADAMTS8, and ADAMTS13). It was found that MMP, TIMP, and ADAMTS are expressed in all parts of the aneurysm with different patterns. A developed aneurysm has such a disturbed expression of the main participants in extracellular matrix remodelling that it is difficult to infer the causes of the disorder development. MMP12 secreted by macrophages at the onset of inflammation may initiate extracellular matrix remodelling, which, if not controlled, initiates a feedback loop leading to aneurysm formation.

scholarly journals Construction of the circRNA-miRNA-mRNA Regulatory Network of an Abdominal Aortic Aneurysm to Explore Its Potential Pathogenesis

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hao Zhang ◽  
Ce Bian ◽  
Simei Tu ◽  
Fanxing Yin ◽  
Panpan Guo ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Protein Interaction ◽  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Abdominal Aortic

Background. Abdominal aortic aneurysm (AAA) is a progressive cardiovascular disease, which is a permanent and localized dilatation of the abdominal aorta with potentially fatal consequence of aortic rupture. Dysregulation of circRNAs is correlated with the development of various pathological events in cardiovascular diseases. However, the function of circRNAs in abdominal aortic aneurysm (AAA) is unknown and remains to be explored. This study is aimed at determining the regulatory mechanisms of circRNAs in AAAs. This study was aimed at exploring the underlying molecular mechanisms of abdominal aortic aneurysms based on the competing endogenous RNA (ceRNA) regulatory hypothesis of circRNA, miRNA, and mRNA. Methods. The expression profiles of circRNAs (GSE144431), miRNAs (GSE62179), and mRNAs (GSE7084, GSE57691, and GSE47472) in human tissue sample from the aneurysm group and normal group were obtained from the Gene Expression Omnibus database, respectively. The circRNA-miRNA-mRNA network was constructed by using Cytoscape 3.7.2 software; then, the protein-protein interaction (PPI) network was constructed by using the STRING database, and the hub genes were identified by using the cytoHubba plug-in. The circRNA-miRNA-hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in AAAs. Results. The present study identified 40 differentially expressed circRNAs (DECs) in the GSE144431, 90 differentially expressed miRNAs (DEmiRs) in the GSE62179, and 168 differentially expressed mRNAs (DEGs) with the same direction regulation (130 downregulated and 38 upregulated) in the GSE7084, GSE57691, and GSE47472 datasets identified regarding AAAs. The miRNA response elements (MREs) of three DECs were then predicted. Four overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 17 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of 4 miRNAs with 168 DEGs. Furthermore, the circRNA-miRNA-mRNA network was constructed through 3 circRNAs, 4 miRNAs, and 17 mRNAs, and three hub genes (SOD2, CCR7, and PGRMC1) were identified. Simultaneously, functional enrichment and pathway analysis were performed within genes in the circRNA-miRNA-mRNA network. Three of them (SOD2, CCR7, and PGRMC1) were suggested to be crucial based on functional enrichment, protein-protein interaction, and ceRNA network analysis. Furthermore, the expression of SOD2 and CCR7 may be regulated by hsa_circ_0011449/hsa_circ_0081968/hsa-let-7f-5p; the expression of PGRMC1 may be regulated by hsa_circ_0011449/hsa_circ_0081968-hsa-let-7f-5p/hsa-let-7e-5p. Conclusion. In conclusion, the ceRNA interaction axis we identified may be an important target for the treatment of abdominal aortic aneurysms. This study provided further understanding of the potential pathogenesis from the perspective of the circRNA-related competitive endogenous RNA network in AAAs.

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.

Identification of key genes and pathways involved in abdominal aortic aneurysm initiation and progression

Vascular ◽  
2021 ◽  
pp. 170853812110264
Author(s):  
Zhixiang Su ◽  
Yongquan Gu
Keyword(s):  
Gene Expression ◽  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Hub Genes ◽  
Biological Regulation ◽  
Main Site ◽  
Abdominal Aortic ◽  
Normal Controls

Objective The study aimed to assess the gene expression profile of biopsies obtained from the neck of human abdominal aortic aneurysm (AAA) and the main site of AAA dilatation and to investigate the molecular mechanism underlying the development of AAA. Methods The microarray profile of GSE47472 and GSE57691 were obtained from the Gene Expression Omnibus (GEO) database. The GSE47472 was a microarray dataset of tissues from the aortic neck of AAA patients versus normal controls. The GSE57691 was a microarray dataset including the tissues from main site of AAA dilatation versus normal controls. Differentially expressed genes (DEGs) were chosen using the R package and annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG). The hub genes were identified in the protein–protein interaction (PPI) network. Results 342 upregulated DEGs and 949 downregulated DEGs were obtained from GSE47472. The upregulated DEGs were mainly enriched in biological regulation (ontology: BP), the membrane (ontology: CC), and protein binding (ontology: MF), and the downregulated genes were mainly enriched in biological regulation (ontology: BP), the membrane (ontology: CC), and protein blinding (ontology: MF). In the KEGG enrichment analysis, the DEGs mainly involved glycosaminoglycan degradation, vasopressin-regulated water reabsorption, and pyruvate metabolism. The hub genes in GSE47472 mainly include VAMP8, PTPRC, DYNLL1, RPL38, RPS4X, HNRNPA1, PRMT1, TGOLN2, PA2G4, and CUL2. From GSE57691, 248 upregulated DEGs and 1120 downregulated DEGs were selected. The upregulated DEGs of GSE57691 were mainly enriched in biological regulation (ontology: BP), the membrane (ontology: CC), and protein binding (ontology: MF), and the downregulated genes were mainly enriched in metabolic process (ontology: BP), the membrane (ontology: CC), and protein blinding (ontology: MF). In the KEGG enrichment analysis, the DEGs mainly involved the mitochondrial respiratory, respiratory chain complex, and respiratory chain. RPS15A, RPS5, RPL23, RPL27A, RPS24, RPL35A, RPS4X, RPL7, RPS25, and RPL21 were identified as the hub genes . Conclusion At the early stage of AAA, the current study indicated the importance of glycosaminoglycan degradation and anaerobic metabolism. We also identified several hub genes closely related to AAA ( VAMP8, PTPRC, DYNLL1, etc. ). At the progression of the AAA, the dysfunctional mitochondria played a critical role in AAA formation and the RPS15A, RPS5, RPL23, etc., were identified as the hub genes.

Current evidence and prospects for medical treatment of abdominal aortic aneurysms

VASA ◽  
2005 ◽  
Vol 34 (4) ◽  
pp. 217-223 ◽  
Author(s):  
Diehm ◽  
Schmidli ◽  
Dai-Do ◽  
Baumgartner
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Medical Treatment ◽  
Endovascular Treatment ◽  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Current Evidence ◽  
Significant Morbidity ◽  
Abdominal Aortic ◽  
Risk Of Rupture

Abdominal aortic aneurysm (AAA) is a potentially fatal condition with risk of rupture increasing as maximum AAA diameter increases. It is agreed upon that open surgical or endovascular treatment is indicated if maximum AAA diameter exceeds 5 to 5.5cm. Continuing aneurysmal degeneration of aortoiliac arteries accounts for significant morbidity, especially in patients undergoing endovascular AAA repair. Purpose of this review is to give an overview of the current evidence of medical treatment of AAA and describe prospects of potential pharmacological approaches towards prevention of aneurysmal degeneration of small AAAs and to highlight possible adjunctive medical treatment approaches after open surgical or endovascular AAA therapy.

Influence of preoperative statins and aspirin administration on biological and magnetic resonance imaging properties in patients with abdominal aortic aneurysm

VASA ◽  
2020 ◽  
pp. 1-9
Author(s):  
Milos Sladojevic ◽  
Petar Zlatanovic ◽  
Zeljka Stanojevic ◽  
Igor Koncar ◽  
Sasenka Vidicevic ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Abdominal Aortic Aneurysm ◽  
Magnetic Resonance ◽  
Aortic Aneurysm ◽  
Signal Intensity ◽  
Psoas Muscle ◽  
Resonance Imaging ◽  
Maximal Diameter ◽  
Aneurysm Wall ◽  
Abdominal Aortic

Summary: Background: Main objective of this study was to evaluate the influence of statins and/or acetylsalicylic acid on biochemical characteristics of abdominal aortic aneurysm (AAA) wall and intraluminal thrombus (ILT). Patients and methods: Fifty patients with asymptomatic infrarenal AAA were analyzed using magnetic resonance imaging on T1w sequence. Relative ILT signal intensity (SI) was determined as a ratio between ILT and psoas muscle SI. Samples containing the full ILT thickness and aneurysm wall were harvested from the anterior surface at the level of the maximal diameter. The concentration of enzymes such as matrix metalloproteinase (MMP) 9, MMP2 and neutrophil elastase (NE/ELA) were analyzed in ILT and AAA wall; while collagen type III, elastin and proteoglycan 4 were analyzed in harvested AAA wall. Oxidative stress in the AAA wall was assessed by catalase and malondialdehyde activity in tissue samples. Results: Relative ILT signal intensity (1.09 ± 0.41 vs 0.89 ± 0.21, p = 0.013) were higher in non-statin than in statin group. Patients who were taking aspirin had lower relative ILT area (0.89 ± 0.19 vs 1.13. ± 0.44, p = 0.016), and lower relative ILT signal intensity (0.85 [0.73–1.07] vs 1.01 [0.84–1.19], p = 0.021) compared to non-aspirin group. There were higher concentrations of elastin in AAA wall among patients taking both of aspirin and statins (1.21 [0.77–3.02] vs 0.78 (0.49–1.05) ng/ml, p = 0.044) than in patients who did not take both of these drugs. Conclusions: Relative ILT SI was lower in patients taking statin and aspirin. Combination of antiplatelet therapy and statins was associated with higher elastin concentrations in AAA wall.

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