Dataset Information

In silico identification of common and specific signatures in coronary heart diseases.

ABSTRACT: Coronary heart disease (CHD) is on the increase in developing countries, where lifestyle choices such as smoking, bad diet, and no exercise contribute and increase the incidence of high blood pressure and high cholesterol levels to cause CHD. Through utilization of a biomarker-based approach for developing interventions, the aim of the study was to identify differentially expressed genes (DEGs) and their association and impact on various bio-targets. The microarray datasets of both healthy and CHD patients were analyzed to identify the DEGs and their interactions using Gene Ontology, PANTHER, Reactome, and STRING (for the possible associated genes with multiple targets). Our data mining approach suggests that the DEGs were associated with molecular functions, including protein binding (75%) and catalytic activity (56%); biological processes such as cellular process (83%), biological regulation (57%), and metabolic process (44%); and cellular components such as cell (65%) and organelle (58%); as well as other associations including apoptosis, inflammatory, cell development and metabolic pathways. The molecular functions were further analyzed, and protein binding in particular was analyzed using network analysis to determine whether there was a clear association with CHD and disease. The ingenuity pathway analysis revealed pathways related to cell cholesterol biosynthesis, the immune system including cytokinin signaling, in which, the understanding of DEGs is crucial to predict the advancement of preventive strategies. Results of the present study showed that, there is a need to validate the top DEGs to rule out their molecular mechanism in heart failure caused by CHD.

SUBMITTER: Yang Z

PROVIDER: S-EPMC7464937 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

<i>In silico</i> identification of common and specific signatures in coronary heart diseases.

Yang Zhijia Z Ma Haifang H Liu Wei W

Experimental and therapeutic medicine 20200813 4

Coronary heart disease (CHD) is on the increase in developing countries, where lifestyle choices such as smoking, bad diet, and no exercise contribute and increase the incidence of high blood pressure and high cholesterol levels to cause CHD. Through utilization of a biomarker-based approach for developing interventions, the aim of the study was to identify differentially expressed genes (DEGs) and their association and impact on various bio-targets. The microarray datasets of both healthy and C ...[more]

PMID: 32905032

Similar Datasets

Project description:BackgroundComorbidities of coronavirus disease 2019 (COVID-19)/coronary heart disease (CHD) pose great threats to disease outcomes, yet little is known about their shared pathology. The study aimed to examine whether comorbidities of COVID-19/CHD involved shared genetic pathology, as well as to clarify the shared genetic variants predisposing risks common to COVID-19 severity and CHD risks.MethodsBy leveraging publicly available summary statistics, we assessed the genetically determined causality between COVID-19 and CHD with bidirectional Mendelian randomization. To further quantify the causality contributed by shared genetic variants, we interrogated their genetic correlation with the linkage disequilibrium score regression method. Bayesian colocalization analysis coupled with conditional/conjunctional false discovery rate analysis was applied to decipher the shared causal single nucleotide polymorphisms (SNPs).FindingsBriefly, we observed that the incident CHD risks post COVID-19 infection were partially determined by shared genetic variants. The shared genetic variants contributed to the causality at a proportion of 0.18 (95% CI 0.18-0.19) to 0.23 (95% CI 0.23-0.24). The SNP (rs10490770) located near LZTFL1 suggested direct causality (SNPs → COVID-19 → CHD), and SNPs in ABO (rs579459, rs495828), ILRUN(rs2744961), and CACFD1(rs4962153, rs3094379) may simultaneously influence COVID-19 severity and CHD risks.InterpretationFive SNPs located near LZTFL1 (rs10490770), ABO (rs579459, rs495828), ILRUN (rs2744961), and CACFD1 (rs4962153, rs3094379) may simultaneously influence their risks. The current study suggested that there may be shared mechanisms predisposing to both COVID-19 severity and CHD risks. Genetic predisposition to COVID-19 is a causal risk factor for CHD, supporting that reducing the COVID-19 infection risk or alleviating COVID-19 severity among those with specific genotypes might reduce their subsequent CHD adverse outcomes. Meanwhile, the shared genetic variants identified may be of clinical implications for identifying the target population who are more vulnerable to adverse CHD outcomes post COVID-19 and may also advance treatments of 'Long COVID-19.'

Project description:Aims:To identify the common and specific molecular mechanisms of three well-defined subtypes of endometriosis (EMs): ovarian endometriosis (OE), peritoneal endometriosis (PE), and deep infiltrating endometriosis (DIE). Methods:Four microarray datasets: GSE7305 and GSE7307 for OE, E-MTAB-694 for PE, and GSE25628 for DIE were downloaded from public databases and conducted to compare ectopic lesions (EC) with eutopic endometrium (EU) from EMs patients. Differentially expressed genes (DEGs) identified by limma package were divided into two parts: common DEGs among three subtypes and specific DEGs in each subtype, both of which were subsequently performed with the Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analysis. The protein-protein interaction (PPI) network was constructed by common DEGs and five hub genes were screened out from the PPI network. Besides, these five hub genes together with selected interested pathway-related genes were further validated in an independent OE RNA-sequencing dataset GSE105764. Results:A total of 54 EC samples from three EMs subtypes (OE, PE, DIE) and 58 EU samples were analyzed, from which we obtained 148 common DEGs among three subtypes, and 729 specific DEGs in OE, 777 specific DEGs in PE and 36 specific DEGs in DIE. The most enriched pathway of 148 shared DEGs was arachidonic acid (AA) metabolism, in which most genes were up-regulated in EC, indicating inflammation was the most common pathogenesis of three subtypes. Besides, five hub genes AURKB, RRM2, DTL, CCNB1, CCNB2 identified from the PPI network constructed by 148 shared DEGs were all associated with cell cycle and mitosis, and down-regulated in EC, suggesting a slow and controlled proliferation in ectopic lesions. The KEGG pathway analysis of specific DEGs in each subtype revealed that abnormal ovarian steroidogenesis was a prominent feature in OE; OE and DIE seems to be at more risk of malignant development since both of their specific DEGs were enriched in the pathways in cancer, though enriched genes were different, while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment. Conclusion:By integrated bioinformatic analysis, we explored common and specific molecular signatures among different subtypes of endometriosis: activated arachidonic acid (AA) metabolism-related inflammatory process and a slow and controlled proliferation in ectopic lesions were common features in OE, PE and DIE; OE and DIE seemed to be at more risk of malignant development while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment, all of which could deepen our perception of endometriosis.

Dataset Information

In silico identification of common and specific signatures in coronary heart diseases.

Publications

<i>In silico</i> identification of common and specific signatures in coronary heart diseases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets