Identification of Key Pathways and Genes in Advanced Coronary Atherosclerosis Using Bioinformatics Analysis

Tan, Xin; Zhang, Xiting; Pan, Long; Tian, Xiaoxuan; Dong, Pin

doi:10.1155/2017/4323496

Cited by 45 publications

(44 citation statements)

References 60 publications

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“…Wnt proteins are regulators of VSMCs behavior through beta catenin dependent and independent Wnt signaling pathways. Endothelial injury and dysfunction leads to de-differentiation of VSMCs [ 39 ].…”

Section: Resultsmentioning

confidence: 99%

Switching of vascular cells towards atherogenesis, and other factors contributing to atherosclerosis: a systematic review

Shafi

2020

Thrombosis J

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Background Onset, development and progression of atherosclerosis are complex multistep processes. Many aspects of atherogenesis are not yet properly known. This study investigates the changes in vasculature that contribute to switching of vascular cells towards atherogenesis, focusing mainly on ageing. Methods Databases including PubMed, MEDLINE and Google Scholar were searched for published articles without any date restrictions, involving atherogenesis, vascular homeostasis, aging, gene expression, signaling pathways, angiogenesis, vascular development, vascular cell differentiation and maintenance, vascular stem cells, endothelial and vascular smooth muscle cells. Results Atherogenesis is a complex multistep process that unfolds in a sequence. It is caused by alterations in: epigenetics and genetics, signaling pathways, cell circuitry, genome stability, heterotypic interactions between multiple cell types and pathologic alterations in vascular microenvironment. Such alterations involve pathological changes in: Shh, Wnt, NOTCH signaling pathways, TGF beta, VEGF, FGF, IGF 1, HGF, AKT/PI3K/ mTOR pathways, EGF, FOXO, CREB, PTEN, several apoptotic pathways, ET – 1, NF-κB, TNF alpha, angiopoietin, EGFR, Bcl − 2, NGF, BDNF, neurotrophins, growth factors, several signaling proteins, MAPK, IFN, TFs, NOs, serum cholesterol, LDL, ephrin, its receptor pathway, HoxA5, Klf3, Klf4, BMPs, TGFs and others. This disruption in vascular homeostasis at cellular, genetic and epigenetic level is involved in switching of the vascular cells towards atherogenesis. All these factors working in pathologic manner, contribute to the development and progression of atherosclerosis. Conclusion The development of atherosclerosis involves the switching of gene expression towards pro-atherogenic genes. This happens because of pathologic alterations in vascular homeostasis. When pathologic alterations in epigenetics, genetics, regulatory genes, microenvironment and vascular cell biology accumulate beyond a specific threshold, then the disease begins to express itself phenotypically. The process of biological ageing is one of the most significant factors in this aspect as it is also involved in the decline in homeostasis, maintenance and integrity. The process of atherogenesis unfolds sequentially (step by step) in an interconnected loop of pathologic changes in vascular biology. Such changes are involved in ‘switching’ of vascular cells towards atherosclerosis.

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Section: Resultsmentioning

confidence: 99%

Switching of vascular cells towards atherogenesis, and other factors contributing to atherosclerosis: a systematic review

Shafi

2020

Thrombosis J

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“…In sex-specific DEGs, there were a group of overlapping DEGs but with opposite changes, which lead to a comparatively smaller amount of DEGs in the overall group. The IS blood sample also presented a smaller amount of DEGs compared with early or advanced atherosclerotic plaque samples [33]. The probable reason is that IS is a less stable pathological state and lacks biological Sex specific signaling analysis of ischemic stroke markers compared with atherosclerotic plaque.…”

Section: Discussionmentioning

confidence: 95%

Sex-specific and opposite modulatory aspects revealed by PPI network and pathway analysis of ischemic stroke in humans

et al. 2020

PLoS ONE

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BackgroundIschemic Stroke (IS) is a major disease which greatly threatens human health. Recent studies showed sex-specific outcomes and mechanisms of cerebral ischemic stroke. This study aimed to identify the key changes of gene expression between male and female IS in humans. MethodsGene expression dataset GSE22255, including peripheral blood samples, was downloaded from the Gene Expression Omnibus (GEO) dataset. Differentially Expressed Genes (DEGs) with a LogFC>1, and a P-value <0.05 were screened by BioConductor R package and grouped in female, male and overlap DEGs for further bioinformatic analysis. Gene Ontology (GO) functional annotation, Protein-Protein Interaction (PPI) network, "Molecular Complex Detection" (MCODE) modules, CytoNCA (cytoscape network centrality analysis) essential genes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway interrelation analysis were performed. ResultsIn a total of 54,665 genes, 185 (73 ups and 112 downs) DEGs in the female dataset, 461 DEGs (297 ups and 164 downs) in the male dataset, within which 118 DEGs overlapped (7 similar changes in female and male, 111 opposite changes in female and male) were obtained from the GSE22255 dataset. Female, male and overlapping DEGs enriched for similar cellular components and molecular function. Male DEGs enriched for divergent biological processes from female and overlapping DEGs. Sex-specific and overlapping DEGs were put into the PPI network. Overlapping genes such as IL6, presented opposite changes and were mainly involved in cytokine-cytokine receptor interactions, the TNF-signalling pathway, etc.

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“…Therefore, the matched platform le was used to convert the probe name to the gene standard name (gene symbol). The linear models for microarray data (LIMMA) package [12,13] was used to identify the DEGs between the ICM or DCM compared with normal control specimens of the GSE1869, GSE5406, GSE57338 and GSE79962 datasets. The relative gene expression values with an adjusted P-value < 0.05 and |log 2 FC| > 0.5 (FC, fold change) were considered as DEGs.…”

Section: Identi Cation Of Degsmentioning

confidence: 99%

“…In recent years, the development of the high-throughput technologies has produced extensive molecular state information on HF and has provided new insight into the pathogenesis of HF [11]. Hundreds or even thousands of samples could be rapidly tested in one batch with a very high accuracy [12]. Presently, numerous sources of gene expression information have been uploaded into the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo), which is a free public repository [13].…”

Section: Introductionmentioning

confidence: 99%

ASPN is a Potential Promising Biomarker for Common Heart Failure

Zhang

Zhou

et al. 2020

Preprint

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Background: Heart failure (HF), the leading cause of adult mortality and morbidity worldwide, is the end-stage of various diseases, especially ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM). This study aimed to investigate the common molecular mechanism of ICM and DCM.Methods: Four gene expression datasets, GSE1869, GSE5406, GSE57338, and GSE79962, were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) of ICM or DCM samples compared with those of nonfailing samples were identified. Gene ontology (GO) annotation, Kyoto encyclopedia of gene and genome (KEGG) pathway analysis, and the protein-protein network (PPI) of the coregulated DEGs in at least three datasets were performed using the online tools of DAVID, the KOBAS database, and the STRING database, respectively. Hub genes of HF were analyzed for their correlation with left ventricular ejection fraction (LVEF) in dataset GSE19303. The expression levels of notable DEGs were further validated in our tissue microarray (TMA).Results: Fifty-nine coregulated ICM and sixty-eight coregulated DCM relevant DEGs were identified (in at least three datasets). Moreover, 38 common DEGs between ICM and DCM relevant DEGs were obtained that were mainly involved in inflammatory/stress processes, proliferation, and some lipid metabolism pathways. Among the ten hub genes with top degrees, four genes showed a correlation with LVEF, and ASPN had the most significant correlation. Finally, the expression of ASPN protein was validated in our TMA and was significantly increased in ICM and DCM left ventricular samples.Conclusion: The present study revealed some common molecular mechanisms of HF with different causes. Furthermore, ASPN may be a potential promising biomarker for HF.

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Identification of Key Pathways and Genes in Advanced Coronary Atherosclerosis Using Bioinformatics Analysis

Cited by 45 publications

References 60 publications

Switching of vascular cells towards atherogenesis, and other factors contributing to atherosclerosis: a systematic review

Switching of vascular cells towards atherogenesis, and other factors contributing to atherosclerosis: a systematic review

Sex-specific and opposite modulatory aspects revealed by PPI network and pathway analysis of ischemic stroke in humans

ASPN is a Potential Promising Biomarker for Common Heart Failure

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