Identification of Potential Hub Genes of Atherosclerosis Through Bioinformatic Analysis

Yuan, Yin; Zou, Yuze; Xiao, Yu; Wang, Tianxi; Wang, Yani; Dong, Zhicheng; Huo, Yu-Hu; Yao, Bo-Chen; Meng, Ling‐Bing; Du, Shuang-Xia

doi:10.1089/cmb.2019.0334

Cited by 6 publications

(7 citation statements)

References 37 publications

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“…The powerful online bioinformatics database, Database for Annotation, Visualization, and Integrated Discovery (DAVID 3 ), furnishes investigators with an all-round range of functional annotation tools to solve the biological implications of large gene lists ( Yin et al, 2020 ). In this research, we used DAVID to aggregate the functions of DEGs including the screening of significant biological processes, molecular functions, cellular components, and as well as, identifying pathways related to these DEGs ( Zou Z.…”

Section: Methodsmentioning

confidence: 99%

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Predicting the Key Genes Involved in Aortic Valve Calcification Through Integrated Bioinformatics Analysis

Wang

Xiong

et al. 2021

Front. Genet.

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Background: Valvular heart disease is obtaining growing attention in the cardiovascular field and it is believed that calcific aortic valve disease (CAVD) is the most common valvular heart disease (VHD) in the world. CAVD does not have a fully effective treatment to delay its progression and the specific molecular mechanism of aortic valve calcification remains unclear.Materials and Methods: We obtained the gene expression datasets GSE12644 and GSE51472 from the public comprehensive free database GEO. Then, a series of bioinformatics methods, such as GO and KEGG analysis, STING online tool, Cytoscape software, were used to identify differentially expressed genes in CAVD and healthy controls, construct a PPI network, and then identify key genes. In addition, immune infiltration analysis was used via CIBERSORT to observe the expression of various immune cells in CAVD.Results: A total of 144 differential expression genes were identified in the CAVD samples in comparison with the control samples, including 49 up-regulated genes and 95 down-regulated genes. GO analysis of DEGs were most observably enriched in the immune response, signal transduction, inflammatory response, proteolysis, innate immune response, and apoptotic process. The KEGG analysis revealed that the enrichment of DEGs in CAVD were remarkably observed in the chemokine signaling pathway, cytokine-cytokine receptor interaction, and PI3K-Akt signaling pathway. Chemokines CXCL13, CCL19, CCL8, CXCL8, CXCL16, MMP9, CCL18, CXCL5, VCAM1, and PPBP were identified as the hub genes of CAVD. It was macrophages that accounted for the maximal proportion among these immune cells. The expression of macrophages M0, B cells memory, and Plasma cells were higher in the CAVD valves than in healthy valves, however, the expression of B cells naïve, NK cells activated, and macrophages M2 were lower.Conclusion: We detected that chemokines CXCL13, CXCL8, CXCL16, and CXCL5, and CCL19, CCL8, and CCL18 are the most important markers of aortic valve disease. The regulatory macrophages M0, plasma cells, B cells memory, B cells naïve, NK cells activated, and macrophages M2 are probably related to the occurrence and the advancement of aortic valve stenosis. These identified chemokines and these immune cells may interact with a subtle adjustment relationship in the development of calcification in CAVD.

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Section: Methodsmentioning

confidence: 99%

“…gene lists (Yin et al, 2020). In this research, we used DAVID to aggregate the functions of DEGs including the screening of significant biological processes, molecular functions, cellular components, and as well as, identifying pathways related to these DEGs .…”

Section: Figure 2 | the Identification Of Edgs (A)mentioning

confidence: 99%

Predicting the Key Genes Involved in Aortic Valve Calcification Through Integrated Bioinformatics Analysis

Wang

Xiong

et al. 2021

Front. Genet.

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“…Emerging studies ( Xu and Yang, 2020 ; Zhao et al, 2020 ) have revealed that immune cells participate in AS progression. Several experimental studies ( Yin et al, 2021 ) have demonstrated that T cells play a key role in the immune response observed during atherogenesis. As cholesterol accumulates in the arteries and inflammation occurs, naïve CD4 T cells differentiate into adaptive cells, and the adaptive T cell response is induced.…”

Section: Discussionmentioning

confidence: 99%

Identification of oxidative stress-related genes and potential mechanisms in atherosclerosis

Tang

Deng²,

Luo³

et al. 2023

Front. Genet.

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Atherosclerosis (AS) is the main cause of death in individuals with cardiovascular and cerebrovascular diseases. A growing body of evidence suggests that oxidative stress plays an essential role in Atherosclerosis pathology. The aim of this study was to determine genetic mechanisms associated with Atherosclerosis and oxidative stress, as well as to construct a diagnostic model and to investigate its immune microenvironment. Seventeen oxidative stress-related genes were identified. A four-gene diagnostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm based on these 17 genes. The area under the Receiver Operating Characteristic (ROC) curve (AUC) was 0.967. Based on the GO analysis, cell-substrate adherens junction and focal adhesion were the most enriched terms. KEGG analysis revealed that these overlapping genes were enriched in pathways associated with Alzheimer’s disease and Parkinson’s disease, as well as with prion disease pathways and ribosomes. Immune cell infiltration correlation analysis showed that the immune cells with significant differences were CD4 memory activated T cells and follicular helper T cells in the GSE43292 dataset and CD4 naïve T cells and CD4 memory resting T cells in the GSE57691 dataset. We identified 17 hub genes that were closely associated with oxidative stress in AS and constructed a four-gene (aldehyde dehydrogenase six family member A1 (ALDH6A1), eukaryotic elongation factor 2 kinase (EEF2K), glutaredoxin (GLRX) and l-lactate dehydrogenase B (LDHB)) diagnostic model with good accuracy. The four-gene diagnostic model was also found to have good discriminatory efficacy for the immune cell infiltration microenvironment of AS. Overall, these findings provide valuable information and directions for future research into Atherosclerosis diagnosis and aid in the discovery of biological mechanisms underlying AS with oxidative stress.

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“…PPI network among all DEGs was established based on an online tool STRING (https://cn.string-db.org/) and then software Cytoscape (version 3.7.2) was employed to adjust and visualize PPI networks (43). Subsequently, we utilized a plug-in Cytohubba of Cytoscape to determine top 10 hub genes according to the MCC algorithms.…”

Section: Protein-protein Interaction (Ppi) Network and Hub Gene Ident...mentioning

confidence: 99%

Integrated Bioinformatics Analysis Reveals Marker Genes and Immune Infiltration for Pulmonary Arterial Hypertension

Tang

Zeng

Liu³

et al. 2022

Preprint

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Background It is well known that Pulmonary arterial hypertension (PAH) is a chronic cardiopulmonary syndrome with high pulmonary vascular load and eventually causing RV heart failure even death. This research is aimed to detect the underlying key genes and potential mechanism of PAH using some kinds of bioinformatic tools. Methods The gene microarrays GSE22356, GSE131793 and GSE168905 were acquired from the GEO. Subsequently, a host of bioinformatics techniques, for instance, DAVID for GO annotation and KEGG enrichment analysis, STING for PPI, Cytoscape software etc. were utilized to investigate DEGs between PAH and healthy controls. Additionally, we also predicted the transcription factors regulating DEGs. And CIBERSORT was used to conduct immune infiltration analysis. Results we identified 403 up-regulated DEGs and 874 down-regulated DEGs in peripheral blood samples between 32 PAH patients and 29 controls. These functional changes of DEGs were mainly enriched in protein binding, extracellular exosome, extracellular space, extracellular region and integral component of plasma membrane. SLC4A1, AHSP, ALAS2, CA1, HBD, SNCA, HBM, SELENBP1, SERPINE1 and ITGA2B were detected as hub genes. These key genes are chiefly enriched at extracellular exosome, hemoglobin complex, blood microparticle, oxygen transporter activity. Among TF-DEGs network, 42 target DEGs and 6 TFs were predicted with an NES > 4 (TEAD4, TGIF2LY, GATA5, GATA1, GATA2, FOS). Immune infiltration analysis showed that monocytes occupied the largest proportion of immune cells. Conclusion We detected SLC4A1, AHSP, ALAS2, CA1, HBD, SNCA, HBM, SELENBP1, SERPINE1 and ITGA2B as the most significant markers of PAH. The PAH patients had higher infiltration of NK cell activation, monocyte, T cell CD4 memory activation, and mast cell than healthy controls and lower infiltration of T cell CD4 naive. These identified genes and these immune cells probably have precise regulatory relationships in the development of PAH.

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Identification of Potential Hub Genes of Atherosclerosis Through Bioinformatic Analysis

Cited by 6 publications

References 37 publications

Predicting the Key Genes Involved in Aortic Valve Calcification Through Integrated Bioinformatics Analysis

Predicting the Key Genes Involved in Aortic Valve Calcification Through Integrated Bioinformatics Analysis

Identification of oxidative stress-related genes and potential mechanisms in atherosclerosis

Integrated Bioinformatics Analysis Reveals Marker Genes and Immune Infiltration for Pulmonary Arterial Hypertension

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