A haplotype of the phosphodiesterase 4D (PDE4D) gene is associated with myocardial infarction and with cardiometabolic parameters: the GEA study

Rodrı́guez-Pérez, José Manuel; Posadas-Sánchez, Rosalinda; Blachman‐Braun, Ruben; Vargas‐Alarcón, Gilberto; Posadas‐Romero, Carlos; García-Flores, Esbeidy; López-Bautista, Fabiola; Tovilla-Zárate, Carlos Alfonso; González‐Castro, Thelma Beatriz; Borgonio-Cuadra, Verónica Marusa; Pérez-Hernández, Nonanzit

doi:10.17179/excli2018-1608

Cited by 3 publications

References 28 publications

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Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

Vastrad¹,

Vastrad²

2021

Preprint

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Myocardial infarction (MI) is the leading cardiovascular diseases in worldwide, yet relatively little is known about the genes and signaling pathways involved in MI progression. The present investigation aimed to elucidate potential crucial candidate genes and pathways in MI. expression profiling by high throughput sequencing dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 20 MI samples and 12 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the DESeq2 R package. These DEGs were subsequently investigated by Gene Ontology (GO) and pathway enrichment analysis, a protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed. Hub genes were validated by receiver operating characteristic curve (ROC) analysis. In total, 958 DEGs were identified, of which 480 were up regulated and 478 were down regulated. GO and pathway enrichment analysis results revealed that the DEGs were mainly enriched in, immune system, neuronal system, response to stimulus, and multicellular organismal process. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed by using Cytoscape software, and CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 were identified as the hub genes. Our results highlight the important roles of the genes including CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 in MI pathogenesis or therapeutic management.

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Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

Vastrad¹,

Vastrad²

2021

Preprint

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The Identification of Key Genes and Biological Pathways in Cardiac Arrest by Integrated Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad,

Vastrad

2024

Preprint

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Cardiac arrest (CA) is a common cause of death world wide. The disease has lacks effective treatment. Efforts have been made to elucidate the molecular pathogenesis of CA, but the molecular mechanisms remain elusive. To identify key genes and pathways in CA, the next generation sequencing (NGS) GSE200117 dataset was downloaded from the Gene Expression Omnibus (GEO) database. DESeq2 tool was used to recognize differentially expressed genes (DEGs). Gene ontology (GO) and REACTOME pathway enrichment analyses were performed to analyze the DEGs and associated signal pathways in the g:Profiler database. The IID database was used to construct the protein-protein interaction (PPI) network, and modules analysis was performed using Cytoscape. A miRNA-hub gene regulatory network and TF-hub gene regulatory network were then constructed to screen miRNAs, TFs and hub genes by miRNet and NetworkAnalyst database and Cityscape software. Receiver operating characteristic curve (ROC) analysis used to verified the hub genes. In total, 844 DEGs were identified, comprising 414 up regulated genes and 430 down regulated genes. GO and REACTOME pathway enrichment analyses indicated that the DEGs for CA were mainly enriched in organonitrogen compound metabolic process, response to stimulus, translation and immune system. Ten hub genes (up-regulated: HSPA8, HOXA1, INCA1 and TP53; down-regulated: HSPB1, LMNA, SNCA, ADAMTSL4 and PDLIM7) were screened. We also predicted miRNAs (hsa-mir-1914-5p and hsa-mir-598-3p) and TFs (JUN and PRRX2) targeting hub genes. This study uses a series of bioinformatics technologies to obtain hug genes, miRNAs and TFs, and key pathways related to CA. These analysis results provide us with new ideas for finding biomarkers and treatment methods for CA.

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Identification and interaction analysis of molecular markers in myocardial infarction by bioinformatics and next-generation sequencing data analysis

Vastrad,

Vastrad

2024

Egypt J Med Hum Genet

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Background Cardiovascular diseases are prevalent worldwide with any age, and it is characterized by sudden blockage of blood flow to heart and permanent damage to the heart muscle, whose cause and underlying molecular mechanisms are not fully understood. This investigation aimed to explore and identify essential genes and signaling pathways that contribute to the progression of MI. Methods The aim of this investigation was to use bioinformatics and next-generation sequencing (NGS) data analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in MI. NGS dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database. DEGs between MI and normal control samples were identified using the DESeq2 R bioconductor tool. The gene ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using g:Profiler. Next, four kinds of algorithms in the protein–protein interaction (PPI) were performed to identify potential novel biomarkers. Next, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network were constructed by miRNet and NetworkAnalyst database, and Cytoscape software. Finally, the diagnostic effectiveness of hub genes was predicted by receiver operator characteristic curve (ROC) analysis and AUC more than 0.800 was considered as having the capability to diagnose MI with excellent specificity and sensitivity. Results A total of 958 DEGs were identified, consisting of 480 up-regulated genes and 478 down-regulated genes. The enriched GO terms and pathways of the DEGs include immune system, neuronal system, response to stimulus and multicellular organismal process. Ten hub genes (namely cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1) were obtained via protein–protein interaction analysis results. MiRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-409-3p, hsa-mir-3200-3p, creb1 and tp63 might play an important role in the MI. Conclusions Analysis of next-generation sequencing dataset combined with global network information and validation presents a successful approach to uncover the risk hub genes and prognostic markers of MI. Our investigation identified four risk- and prognostic-related gene signatures, including cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of MI.

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A haplotype of the phosphodiesterase 4D (PDE4D) gene is associated with myocardial infarction and with cardiometabolic parameters: the GEA study

Cited by 3 publications

References 28 publications

Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

The Identification of Key Genes and Biological Pathways in Cardiac Arrest by Integrated Bioinformatics and Next Generation Sequencing Data Analysis

Identification and interaction analysis of molecular markers in myocardial infarction by bioinformatics and next-generation sequencing data analysis

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