scholarly journals Biodata Mining of Differentially Expressed Genes between Acute Myocardial Infarction and Unstable Angina Based on Integrated Bioinformatics

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Siyu Guo ◽  
Zhihong Huang ◽  
Xinkui Liu ◽  
Jingyuan Zhang ◽  
Peizhi Ye ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Unstable Angina ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Trp Channels ◽  
Cytokine Receptor ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Coronary Syndrome

Acute coronary syndrome (ACS) is a complex syndrome of clinical symptoms. In order to accurately diagnose the type of disease in ACS patients, this study is aimed at exploring the differentially expressed genes (DEGs) and biological pathways between acute myocardial infarction (AMI) and unstable angina (UA). The GSE29111 and GSE60993 datasets containing microarray data from AMI and UA patients were downloaded from the Gene Expression Omnibus (GEO) database. DEG analysis of these 2 datasets is performed using the “limma” package in R software. DEGs were also analyzed using protein-protein interaction (PPI), Molecular Complex Detection (MCODE) algorithm, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Correlation analysis and “cytoHubba” were used to analyze the hub genes. A total of 286 DEGs were obtained from GSE29111 and GSE60993, including 132 upregulated genes and 154 downregulated genes. Subsequent comprehensive analysis identified 20 key genes that may be related to the occurrence and development of AMI and UA and were involved in the inflammatory response, interaction of neuroactive ligand-receptor, calcium signaling pathway, inflammatory mediator regulation of TRP channels, viral protein interaction with cytokine and cytokine receptor, human cytomegalovirus infection, and cytokine-cytokine receptor interaction pathway. The integrated bioinformatical analysis could improve our understanding of DEGs between AMI and UA. The results of this study might provide a new perspective and reference for the early diagnosis and treatment of ACS.

scholarly journals Identification of Key Genes Involved in Acute Myocardial Infarction by Comparative Transcriptome Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaodong Sheng ◽  
Tao Fan ◽  
Xiaoqi Jin
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Protein Protein Interaction ◽  
Key Genes ◽  
Rna Interaction

Background. Acute myocardial infarction (AMI) is regarded as an urgent clinical entity, and identification of differentially expressed genes, lncRNAs, and altered pathways shall provide new insight into the molecular mechanisms behind AMI. Materials and Methods. Microarray data was collected to identify key genes and lncRNAs involved in AMI pathogenesis. The differential expression analysis and gene set enrichment analysis (GSEA) were employed to identify the upregulated and downregulated genes and pathways in AMI. The protein-protein interaction network and protein-RNA interaction analysis were utilized to reveal key long noncoding RNAs. Results. In the present study, we utilized gene expression profiles of circulating endothelial cells (CEC) from 49 patients of AMI and 50 controls and identified a total of 552 differentially expressed genes (DEGs). Based on these DEGs, we also observed that inflammatory response-related genes and pathways were highly upregulated in AMI. Mapping the DEGs to the protein-protein interaction (PPI) network and identifying the subnetworks, we found that OMD and WDFY3 were the hub nodes of two subnetworks with the highest connectivity, which were found to be involved in circadian rhythm and organ- or tissue-specific immune response. Furthermore, 23 lncRNAs were differentially expressed between AMI and control groups. Specifically, we identified some functional lncRNAs, including XIST and its antisense RNA, TSIX, and three lncRNAs (LINC00528, LINC00936, and LINC01001), which were predicted to be interacting with TLR2 and participate in Toll-like receptor signaling pathway. In addition, we also employed the MMPC algorithm to identify six gene signatures for AMI diagnosis. Particularly, the multivariable SVM model based on the six genes has achieved a satisfying performance ( AUC = 0.97 ). Conclusion. In conclusion, we have identified key regulatory lncRNAs implicated in AMI, which not only deepens our understanding of the lncRNA-related molecular mechanism of AMI but also provides computationally predicted regulatory lncRNAs for AMI researchers.

scholarly journals Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Li ◽  
Xiao_nan He ◽  
Chao Li ◽  
Ling Gong ◽  
Min Liu
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Differentially Expressed ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Background. Identification of potential molecular targets of acute myocardial infarction is crucial to our comprehensive understanding of the disease mechanism. However, studies of gene coexpression analysis via jointing multiple microarray data of acute myocardial infarction still remain restricted. Methods. Microarray data of acute myocardial infarction (GSE48060, GSE66360, GSE97320, and GSE19339) were downloaded from Gene Expression Omnibus database. Three data sets without heterogeneity (GSE48060, GSE66360, and GSE97320) were subjected to differential expression analysis using MetaDE package. Differentially expressed genes having upper 25% variation across samples were imported in weighted gene coexpression network analysis. Functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID. The predicted microRNAs to regulate target genes in the most significant module were identified using TargetScan. Moreover, subpathway analyses using iSubpathwayMiner package and GenCLiP 2.0 were performed on hub genes with high connective weight in the most significant module. Results. A total of 1027 differentially expressed genes and 33 specific modules were screened out between acute myocardial infarction patients and control samples. Ficolin (collagen/fibrinogen domain containing) 1 (FCN1), CD14 molecule (CD14), S100 calcium binding protein A9 (S100A9), and mitochondrial aldehyde dehydrogenase 2 (ALDH2) were identified as critical target molecules; hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential regulators of the expression of the key genes in the two biggest modules. Conclusions. FCN1, CD14, S100A9, ALDH2, hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential candidate regulators in acute myocardial infarction. These findings might provide new comprehension into the underlying molecular mechanism of disease.

scholarly journals Comparative transcriptomic analysis of porcine peripheral blood reveals differentially expressed genes from the cytokine–cytokine receptor interaction pathway related to health status

Genome ◽  
2017 ◽  
Vol 60 (12) ◽  
pp. 1021-1028 ◽  
Author(s):  
M.H. Ye ◽  
H. Bao ◽  
Y. Meng ◽  
L.L. Guan ◽  
P. Stothard ◽  
...  
Keyword(s):  
Health Status ◽  
Differentially Expressed Genes ◽  
Peripheral Blood ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Cytokine Receptor ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Marker Genes

While some research has looked into the host genetic response in pigs challenged with specific viruses or bacteria, few studies have explored the expression changes of transcripts in the peripheral blood of sick pigs that may be infected with multiple pathogens on farms. In this study, the architecture of the peripheral blood transcriptome of 64 Duroc sired commercial pigs, including 18 healthy animals at entry to a growing facility (set as a control) and 23 pairs of samples from healthy and sick pen mates, was generated using RNA-Seq technology. In total, 246 differentially expressed genes were identified to be specific to the sick animals. Functional enrichment analysis for those genes revealed that the over-represented gene ontology terms for the biological processes category were exclusively immune activity related. The cytokine–cytokine receptor interaction pathway was significantly enriched. Nine functional genes from this pathway encoding members (as well as their receptors) of the interleukins, chemokines, tumor necrosis factors, colony stimulating factors, activins, and interferons exhibited significant transcriptional alteration in sick animals. Our results suggest a subset of novel marker genes that may be useful candidate genes in the evaluation and prediction of health status in pigs under commercial production conditions.

scholarly journals Elevated Plasma IL-37, IL-18, and IL-18BP Concentrations in Patients with Acute Coronary Syndrome

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Qingwei Ji ◽  
Qiutang Zeng ◽  
Ying Huang ◽  
Ying Shi ◽  
Yingzhong Lin ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Acute Coronary Syndrome ◽  
Angina Pectoris ◽  
Unstable Angina ◽  
Left Ventricular ◽  
Unstable Angina Pectoris ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Coronary Syndrome

Objective.More recently, evidence showed that the novel anti-inflammatory cytokine interleukin- (IL-) 37 was expressed in the foam-like cells of atherosclerotic coronary and carotid artery plaques, suggesting that IL-37 is involved in atherosclerosis-related diseases. However, the plasma levels of IL-37 in patients with acute coronary syndrome (ACS, including unstable angina pectoris and acute myocardial infarction) have yet to be investigated.Methods.Plasma IL-37, IL-18, and IL-18BP levels were measured in 50 patients with stable angina pectoris (SAP), 75 patients with unstable angina pectoris (UAP), 67 patients with acute myocardial infarction (AMI), and 65 control patients.Results.The plasma IL-37, IL-18, and IL-18BP levels were significantly increased in ACS patients compared to SAP and control patients. A correlation analysis showed that the plasma biomarker levels were positively correlated with each other and with the levels of C-reactive protein (CRP),N-terminal probrain natriuretic peptide (NT-proBNP), and left ventricular end-diastolic dimension (LVEDD) but negatively correlated with left ventricular ejection fraction (LVEF). Furthermore, the plasma IL-37, IL-18, and IL-18BP had no correlation with the severity of the coronary artery stenosis.Conclusions.The results indicate that the plasma IL-37 levels are associated with the onset of ACS.

scholarly journals Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Da-Qiu Chen ◽  
Xiang-Sheng Kong ◽  
Xue-Bin Shen ◽  
Mao-Zhi Huang ◽  
Jian-Ping Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed ◽  
High Morbidity ◽  
Hub Genes ◽  
Module Analysis

Background. Acute myocardial infarction (AMI) is a common disease with high morbidity and mortality around the world. The aim of this research was to determine the differentially expressed genes (DEGs), which may serve as potential therapeutic targets or new biomarkers in AMI. Methods. From the Gene Expression Omnibus (GEO) database, three gene expression profiles (GSE775, GSE19322, and GSE97494) were downloaded. To identify the DEGs, integrated bioinformatics analysis and robust rank aggregation (RRA) method were applied. These DEGs were performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using Clusterprofiler package. In order to explore the correlation between these DEGs, the interaction network of protein-protein internet (PPI) was constructed using the STRING database. Utilizing the MCODE plug-in of Cytoscape, the module analysis was performed. Utilizing the cytoHubba plug-in, the hub genes were screened out. Results. 57 DEGs in total were identified, including 2 down- and 55 upregulated genes. These DEGs were mainly enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and so on. The module analysis filtered out 18 key genes, including Cxcl5, Arg1, Cxcl1, Spp1, Selp, Ptx3, Tnfaip6, Mmp8, Serpine1, Ptgs2, Il6, Il1r2, Il1b, Ccl3, Ccr1, Hmox1, Cxcl2, and Ccl2. Ccr1 was the most fundamental gene in PPI network. 4 hub genes in total were identified, including Cxcl1, Cxcl2, Cxcl5, and Mmp8. Conclusion. This study may provide credible molecular biomarkers in terms of screening, diagnosis, and prognosis for AMI. Meanwhile, it also serves as a basis for exploring new therapeutic target for AMI.

scholarly journals Trends in Coded Indications for Percutaneous Coronary Interventions in Medicare and the Veterans Affairs After Implementation of Hospital-Level Reporting of Appropriate Use Criteria

2021 ◽  
Vol 14 (4) ◽  
Author(s):  
Elias J. Dayoub ◽  
Ashwin S. Nathan ◽  
Sameed Ahmed M. Khatana ◽  
Rishi K. Wadhera ◽  
Daniel M. Kolansky ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Unstable Angina ◽  
Veterans Affairs ◽  
Appropriate Use Criteria ◽  
Appropriate Use ◽  
Difference In Differences ◽  
Coronary Syndrome ◽  
Percutaneous Coronary ◽  
Va Hospitals

Background: In 2009, the American College of Cardiology and American Heart Association published Appropriate Use Criteria for Coronary Revascularization (AUC) to aid patient selection for percutaneous coronary intervention (PCI). The subsequent decline in inappropriate PCIs was interpreted as a success of AUC. However, there are concerns clinicians reclassify nonacute PCIs to acute indications to fulfill AUC. Methods: A longitudinal, observational difference-in-differences analysis was performed using administrative claims from US Department of Veterans Affairs (VA) beneficiaries coenrolled in Medicare and from a national random sample of Medicare beneficiaries, undergoing PCI from September 30, 2009, to December 31, 2013. Non-VA hospitals participating in the American College of Cardiology CathPCI registry began receiving AUC reports in 2011, while VA hospitals did not receive reports, serving as quasiexperimental and control cohorts, respectively. We measured the proportion of PCIs coded for acute myocardial infarction, unstable angina, and nonacute coronary syndrome indications by quarter. Results: There were 87 464 and 30 251 PCIs performed in the Medicare and VA cohorts, respectively. In Medicare, proportion of PCIs coded for acute myocardial infarction and unstable angina changed from 31.9% and 12.6% in quarter 4 2009 to 41.0% and 10.5% in quarter 4 2013, an associated 2.00% (95% CI, 1.56%–2.44%; P <0.001) increase per year in PCIs coded for acute coronary syndrome indications. In the VA, proportion of PCIs coded for acute myocardial infarction and unstable angina changed from 26.5% and 15.7% in quarter 4 2009 to 34.3% and 12.3% in quarter 4 2013, an associated 1.20% (95% CI, 0.56%–1.88%; P =0.001) increase per year in PCIs coded for acute coronary syndrome indications. Difference-in-differences modeling found no statistically significant change in PCI coded for acute indications between Medicare and VA, pre- and post-AUC reporting. Conclusions: After introduction of AUC assessments and reporting, we observed comparable increases in coding for acute myocardial infarction and corresponding decreases in coding for unstable angina and nonacute coronary syndrome indications among national cohorts of Medicare and VA enrollees. The provision of appropriate use reporting did not appear to have a substantial impact on the proportion of PCIs coded for acute indications during this study period.

scholarly journals Differential Analysis of Expressed Genes in Diabetic Nephropathy Based on Bioinformatics Technology

2021 ◽  
Author(s):  
Yu Liu ◽  
Jundong Wang ◽  
wencheng Chi ◽  
Jing Xie ◽  
LaiKuan Teh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Biological Effects ◽  
Cytokine Receptor ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Differential Analysis ◽  
Core Genes

Abstract Objective: Bioinformatics technology was used in this study to analyze the expression data of patients with diabetic nephropathy (DN) and normal subjects from the microarray. The purpose of this study was to screen the differentially expressed genes in DN and to explore the pathogenesis and potential therapeutic targets of DN. Methods: The data of gene expression in the gse142153 gene chip was downloaded from the gene expression database (GEO). The up-regulated and down-regulated expressed genes were analyzed by R language. The core genes of differentially expressed genes were analyzed by string database, Cytoscape software and its plug-in. The differentially expressed genes were analyzed by gene ontology and Kyoto Encyclopedia of genes and genomes. Results: A total of 112 differentially expressed genes were screened, including 50 down-regulated genes and 62 up-regulated genes. There are 10 up-regulated core genes including CXCL8, MMP9, IL1B, IL6, IL10, CXCL2, CCL20, ATF3, CXCL3, F3. Their biological effects are mainly concentrated in the IL-17 signaling pathway, rheumatoid arthritis, viral protein interaction with cytokine and cytokine receptor, Amoebiasis, TNF signaling pathway, Legionellosis, Cytokine-cytokine receptor interaction, Lipid, and atherosclerosis, Malaria, NOD-like receptor signaling pathway, etc. Conclusion: Analysis of differentially expressed genes and core genes enhanced the understanding of the pathogenesis of DN and provided a potential train of thought for the treatment of DN.

scholarly journals Influence of type A behavioral activity on the development of cardiovascular disease

2019 ◽  
Vol 10 (4) ◽  
pp. 39-43
Author(s):  
Anatoly I. Martynov ◽  
Evgeniya V. Akatova ◽  
Elena I. Pervichko ◽  
Olesya P. Nikolin ◽  
Inna V. Urlaeva
Keyword(s):  
Myocardial Infarction ◽  
Blood Pressure ◽  
Acute Myocardial Infarction ◽  
Acute Coronary Syndrome ◽  
Unstable Angina ◽  
Type A ◽  
Behavioral Activity ◽  
Daily Monitoring ◽  
Coronary Syndrome ◽  
Type Ab

Aim. To study the influence of type of behavioral activity on the development of cardiovascular diseases, to evaluate the effect of type of behavior on the frequency of repeated hospitalizations and fatal outcomes after acute coronary syndrome. Materials and methods. The study included 100 patients with acute coronary syndrome who were subsequently divided into groups according to the main disease - acute myocardial infarction and unstable angina. The median age was 62.09±5.46 years, the therapy according to the underlying disease. All patients had anamnesis of previous and concomitant diseases, anthropometric measurements, physical examination, and observation during the next 24 months after inclusion in the study. In dynamics he carried out daily monitoring of electrocardiogramm, daily monitoring of blood pressure, echocardiography. Diagnosis of types of behavioral activity was carried out using the test method "type of behavioral activity" developed on the basis of the questionnaire Jenkins Activity Survey, published in 1974 by C. Jenkins, the Russian-language adaptation was performed in NIPNI Bekhtereva (L.I. Wasserman, N.V. Gumenyuk). Results. In patients with behavioral activity type A more frequent occurrence of diseases such as angina, hypertension, acute myocardial infarction, acute violation of cerebral circulation, type 2 diabetes than in persons with behavior type AB and B. Repeated hospitalizations for unstable blood pressure and unstable angina on the background of standard therapy in patients with type a behavior occur more often than in patients with type AB and B.

scholarly journals Uncovering Potential lncRNAs and mRNAs in the Progression From Acute Myocardial Infarction to Myocardial Fibrosis to Heart Failure

2021 ◽  
Vol 8 ◽  
Author(s):  
Shuo Wang ◽  
Enmao Wang ◽  
Qincong Chen ◽  
Yan Yang ◽  
Lei Xu ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Acute Myocardial Infarction ◽  
Myocardial Fibrosis ◽  
Fluid Shear Stress ◽  
Cell Lineage ◽  
Diagnostic Value ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Mannose Metabolism

Background: Morbidity and mortality of heart failure (HF) post-myocardial infarction (MI) remain elevated. The aim of this study was to find potential long non-coding RNAs (lncRNAs) and mRNAs in the progression from acute myocardial infarction (AMI) to myocardial fibrosis (MF) to HF.Methods: Firstly, blood samples from AMI, MF, and HF patients were used for RNA sequencing. Secondly, differentially expressed lncRNAs and mRNAs were obtained in MF vs. AMI and HF vs. MF, followed by functional analysis of shared differentially expressed mRNAs between two groups. Thirdly, interaction networks of lncRNA-nearby targeted mRNA and lncRNA-co-expressed mRNA were constructed in MF vs. AMI and HF vs. MF. Finally, expression validation and diagnostic capability analysis of selected lncRNAs and mRNAs were performed.Results: Several lncRNA-co-expressed/nearby targeted mRNA pairs including AC005392.3/AC007278.2-IL18R1, AL356356.1/AL137145.2-PFKFB3, and MKNK1-AS1/LINC01127-IL1R2 were identified. Several signaling pathways including TNF and cytokine–cytokine receptor interaction, fructose and mannose metabolism and HIF-1, hematopoietic cell lineage and fluid shear stress, and atherosclerosis and estrogen were selected. IL1R2, IRAK3, LRG1, and PLAC4 had a potential diagnostic value for both AMI and HF.Conclusion: Identified AC005392.3/AC007278.2-IL18R1, AL356356.1/AL137145.2-PFKFB3, and MKNK1-AS1/LINC01127-IL1R2 lncRNA-co-expressed/nearby targeted mRNA pairs may play crucial roles in the development of AMI, MF, and HF.

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