Similar Datasets
Project description:A mechanistic framework for cardiometabolic and coronary artery disease
| PRJNA781115 | ENA
Project description:Coronary atherosclerosis results from the delicate interplay of genetic and exogenous risk factors, principally taking place in metabolic organs and the arterial wall. Here we show that 224 gene-regulatory coexpression networks (GRNs) identified by integrating genetic and clinical data from patients with (n = 600) and without (n = 250) coronary artery disease (CAD) with RNA-seq data from seven disease-relevant tissues in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study largely capture this delicate interplay, explaining >54% of CAD heritability. Within 89 cross-tissue GRNs associated with clinical severity of CAD, 374 endocrine factors facilitated inter-organ interactions, primarily along an axis from adipose tissue to the liver (n = 152). This axis was independently replicated in genetically diverse mouse strains and by injection of recombinant forms of adipose endocrine factors (EPDR1, FCN2, FSTL3 and LBP) that markedly altered blood lipid and glucose levels in mice. Altogether, the STARNET database and the associated GRN browser (http://starnet.mssm.edu) provide a multiorgan framework for exploration of the molecular interplay between cardiometabolic disorders and CAD.
| S-EPMC9583458 | biostudies-literature
Project description:Coronary artery disease (CAD) and the frequently coexisting aortic valve stenosis (AS) are the heart diseases accounting for the highest proportion of cardiac surgeries. Routine biomarkers for an earl detection of either of these atherosclerotic-rooted conditions would be important to anticipate the diagnosis and to evaluate their severity with imaging techniques before they become advanced to the point where intervention or surgery have limited therapeutic benefit. Urine is an attractive biofluid for biomarker assessment, provided the noninvasive nature of its collection. Therefore, we conducted a shotgun proteomics analysis of urine collected from 12 CAD and/or AS patients and 11 cardiovascular disease-free controls, aiming at identification of putative molecular candidates that could differentiate these diseases from healthy subjects
2022-12-18 | PXD036800 | Pride
Project description:We aimed to clarify the possible functional role of hsa_circ_0000563 in coronary artery disease. Therefore, the ChIRP-MS was conducted to explore the interaction between BTBD7_hsa_circ_0000563 and proteins on a genomic scale in human peripheral blood mononuclear cell (PBMC). This project is the raw files of the proteins bound to hsa_circ_0000563 found by ChIRP-MS in PBMC.
2023-03-11 | PXD033196 | Pride
Project description:ObjectiveThis study focuses on the innovative application of Automated Machine Learning (AutoML) technology in cardiovascular medicine to construct an explainable Coronary Artery Disease (CAD) prediction model to support the clinical diagnosis of CAD.MethodsThis study utilizes a combined data set of five public data sets related to CAD. An ensemble model is constructed using the AutoML open-source framework AutoGluon to evaluate the feasibility of AutoML in constructing a disease prediction model in cardiovascular medicine. The performance of the ensemble model is compared against individual baseline models. Finally, the disease prediction ensemble model is explained using SHapley Additive exPlanations (SHAP).ResultsThe experimental results show that the AutoGluon-based ensemble model performs better than the individual baseline models in predicting CAD. It achieved an accuracy of 0.9167 and an AUC of 0.9562 in 4-fold cross-bagging. SHAP measures the importance of each feature to the prediction of the model and explains the prediction results of the model.ConclusionThis study demonstrates the feasibility and efficacy of AutoML technology in cardiovascular medicine and highlights its potential in disease prediction. AutoML reduces the barriers to model building and significantly improves prediction accuracy. Additionally, the integration of SHAP enhances model transparency and explainability, which is critical to ensuring model credibility and widespread adoption in cardiovascular medicine.
| S-EPMC11246996 | biostudies-literature
Project description:The human LncRNA microarray analysis of the 6 monocytes samples from Coronary Artery Disease patients and non Coronary Artery Disease 3 Coronary Artery Disease patients and 3 non-Coronary Artery Disease donors
2015-06-05 | E-GEOD-69587 | biostudies-arrayexpress
Project description:The human LncRNA microarray analysis of the 6 plasma samples from Coronary Artery Disease patients and non Coronary Artery Disease Agilent Feature Extraction software (version 11.0.1.1) was used to analyze acquired array images. Quantile normalization was performed using Expander6 and subsequent data processing was performed using the GeneSpring GX v11.5.1 software package (Agilent Technologies). After low intensity filtering, LncRNAs and mRNAs that at least 2 out of 12 samples have flags in Present or Marginal (“All Targets Value”) were chosen for quantile normalization and further data analysis. Differentially expressed LncRNAs and mRNAs with statistical significance were identified through Volcano Plot filtering. Pathway analysis and GO analysis were applied to determine the roles of these differentially expressed mRNAs played in these biological pathways or GO terms. Finally, Hierarchical Clustering was performed to show the distinguishable LncRNAs expression pattern among samples.
2015-05-05 | E-GEOD-68506 | biostudies-arrayexpress
Project description:Coronary artery disease (CAD) is the leading cause of mortality and morbidity driven by both genetic and environmental risk factors. Meta-analyses of genome-wide association studies (GWAS) have identified multiple single nucleotide polymorphisms (SNPs) associated with CAD and myocardial infarction (MI) susceptibility in multi-ethnic populations. The majority of these variants reside in non-coding regulatory regions and are co-inherited with hundreds of candidate regulatory SNPs. Herein, we use integrative genomic, epigenomic, and transcriptomic fine-mapping in human coronary artery smooth muscle cells (HCASMC) and tissues to identify causal regulatory variation and mechanisms responsible for CAD associations. Using these genome-wide maps we prioritize 65 candidate variants and perform allele-specific binding and expression analyses on 7 top candidates. We validate our findings in two independent cohorts of diseased human arterial expression quantitative trait loci (eQTL), which together demonstrate fundamental links between CAD associations and regulatory function in the appropriate disease context.
2016-07-06 | GSE72696 | GEO
Project description:We developed a coronary plaque sampling approach that could be applied broadly in live patients with coronary artery disease (CAD) to obtain molecular and cellular insights into human coronary atherosclerosis. Our approach combined RNA retrieval directly from balloons used in percutaneous coronary interventions (PCI) and inexpensive, low-input RNA-Seq using SMART-seq. We generated SMART-Seq libraries from coronary samples from 27 patients. Of the 27 patients, 13 were confirmed to have stable CAD (sCAD) and 14 confirmed to have been performed on lesions causing acute coronary syndrome (ACS). We applied CIBERSORTx to analyze the SMART-seq data from each of the 27 samples. We found fibroblasts and fibromyoctes were enriched, while smooth muscle cells were reduced, in samples from ACS compared with sCAD patients. We identified 371 genes as significantly differential expressed (q<0.05) between sCAD and ACS patients.
2023-07-10 | GSE236610 | GEO
Project description:Purpose: Utilized Next-generation sequencing (NGS) to profile transcriptional differences between two populations, carriers (CC) of rs10846744 SNP associated with cardiovascular disease (CVD) and non-carriers (GG) who are disease free. Methods: Total RNA was isolated from three subjects homozygous for the rs10846744 reference (GG) allele and three subjects homozygous for the rs10846744 risk (CC) allele and then subjected to full transcriptome sequencing using the Perkin Elmer next gen sequencing platform (Perkin Elmer, Branford, CT). Bioinformatics was performed using Perkin Elmer GeneSifter software program. The data was adjusted by selecting total map reads, quality reads >20, log transformation, and using Benjamini Hochberg to correct for multiple testing. RNA targets of interest were validated by qRT–PCR using TaqMan assays and western blotting using standard methodologies. Results: Using Perkin Elmer's Genesifter Analysis Edition Software, we mapped about 100 million sequence reads per sample to the human genome (build 37.2), normalized the raw read count by total mapped million reads and identified 937 upregulated and 587 downregulated transcripts in the EBV (Epstein Barr Virus)-transformed B lymphocyte cells isolated from 3 carriers of the risk (CC) allele and 3 non-carriers of the (GG) reference allele with BWA workflow. RNA-seq data confirmed differential expression of LAG3 and this was validated with qRT–PCR. Conclusions: Our study represents the first detailed analysis of differential LAG3 expression, contributing to CVD, with biologic replicates, generated by RNA-seq technology.
2016-10-20 | GSE87891 | GEO