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>

Microarray analysis to identify the similarities and differences of pathogenesis between aortic occlusive disease and abdominal aortic aneurysm

Vascular ◽  
2017 ◽  
Vol 26 (3) ◽  
pp. 301-314 ◽  
Author(s):  
Guofu Wang ◽  
Lechang Bi ◽  
Gaofeng Wang ◽  
Feilai Huang ◽  
Mingjing Lu ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Differentially Expressed Genes ◽  
Occlusive Disease ◽  
Differentially Expressed ◽  
Online Tool ◽  
Abdominal Aortic ◽  
Transcriptional Regulatory ◽  
Similarities And Differences ◽  
High Degree

Objectives Expression profile of GSE57691 was analyzed to identify the similarities and differences between aortic occlusive disease and abdominal aortic aneurysm. Methods The expression profile of GSE57691 was downloaded from Gene Expression Omnibus database, including 20 small abdominal aortic aneurysm samples, 29 large abdominal aortic aneurysm samples, 9 aortic occlusive disease samples, and 10 control samples. Using the limma package in R, the differentially expressed genes were screened. Followed by enrichment analysis was performed for the differentially expressed genes using database for annotation, visualization, and integrated discovery online tool. Based on string online tool and Cytoscape software, protein–protein interaction network and module analyses were carried out. Moreover, integrated TF platform database and Cytoscape software were used for constructing transcriptional regulatory networks. Results As a result, 1757, 354, and 396 differentially expressed genes separately were identified in aortic occlusive disease, large abdominal aortic aneurysm, and small abdominal aortic aneurysm samples. UBB was significantly enriched in proteolysis related pathways with a high degree in three groups. SPARCL1 was another gene shared by these groups and regulated by NFIA, which had a high degree in transcriptional regulatory network. ACTB, a significant upregulated gene in abdominal aortic aneurysm samples, could be regulated by CLIC4, which was significantly enriched in cell motions. ACLY and NFIB were separately identified in aortic occlusive disease and small abdominal aortic aneurysm samples, and separately enriched in lipid metabolism and negative regulation of cell proliferation. Conclusions The downregulated UBB, NFIA, and SPARCL1 might play key roles in both aortic occlusive disease and abdominal aortic aneurysm, while the upregulated ACTB might only involve in abdominal aortic aneurysm. ACLY and NFIB were specifically involved in aortic occlusive disease and small abdominal aortic aneurysm separately.

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.

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 Discovery of crucial cytokines associated with abdominal aortic aneurysm formation by protein array analysis

2019 ◽  
Vol 244 (18) ◽  
pp. 1648-1657
Author(s):  
Yuan Li ◽  
Dan Yang ◽  
Bo Sun ◽  
Xu Zhang ◽  
Fangda Li ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Cathepsin L ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Antibody Array ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Abdominal Aortic ◽  
Related Proteins

As a common disease, abdominal aortic aneurysm (AAA) features permanently progressively dilated abdominal aorta. Various cytokines are implicated in AAA pathogenesis. Clarification of involved cytokines combined with functional analysis may provide new insights into AAA pathogenesis. Using a mouse model, this study analyzed the cytokine profiles in AAA. Cytokines were measured in AAA tissues of saline control or angiotensin II-treated ApoE−/− mice using an antibody array of 200 cytokines, cytokine receptors, and related proteins. Statistical analysis revealed that 21 of 200 proteins were differentially expressed in AAA. These differentially expressed proteins were subjected to function and pathway enrichment analysis, which revealed that leukocyte migration and positive regulation of cell adhesion were the most significant biological processes. Specific signaling pathways, including Janus kinase/signal transducers and activators of transcription and cytokine–cytokine receptor interaction, were prominent in Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Importantly, our data identified cytokines which had not previously been illustrated in AAA pathogenic pathways. Bivariate correlation analysis between these cytokines and protease activity showed that granulocyte colony-stimulating factor (G-CSF), macrophage inflammatory protein 1 g, cardiotrophin 1, milk fat globule-EGF factor 8 protein, interleukin 33, and periostin were positively correlated with matrix metalloprotease 1 (MMP-1), MMP-9, cathepsin B, and cathepsin L. G-CSF was positively correlated with cathepsin L. In conclusion, these results demonstrate that cytokine profile is significantly altered in AAA, and that the newly identified crucial cytokines may function potentially in AAA pathogenesis. Impact statement Various cytokines are known contributors to abdominal aortic aneurysm (AAA) pathologic processes, but the mechanisms underlying the pathogenesis remains unclear. We illustrated the altered cytokine profiles in AAA by high throughput antibody array of 200 cytokines, cytokine receptors and related proteins, as well as bioinformatics analysis of differentially expressed proteins in lesion tissues from AAA mice infused with angiotensin II. Functional analyses of differentially expressed cytokines showed clustering on cell migration and adhesion processes. More importantly, crucial cytokines whose association with AAA formation had not been established were identified. Significant correlations were found between these cytokines and protease activity. This study identifies several crucial markers for further researches on the molecular basis 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 Combining Bioinformatics Techniques to Study the Key Immune-Related Genes in Abdominal Aortic Aneurysm

2020 ◽  
Vol 11 ◽  
Author(s):  
Han Nie ◽  
Jiacong Qiu ◽  
Si Wen ◽  
Weimin Zhou
Keyword(s):  
Immune Response ◽  
T Cells ◽  
Abdominal Aortic Aneurysm ◽  
Mast Cells ◽  
Aortic Aneurysm ◽  
Immune Cells ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Abdominal Aortic ◽  
Significant Difference

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.

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.

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