Gene expression profiling reveals potential prognostic biomarkers associated with the progression of heart failure (original) (raw)
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Transcriptomic Biomarkers for Individual Risk Assessment in New-Onset Heart Failure
Circulation, 2008
Background-Prediction of prognosis remains a major unmet need in new-onset heart failure (HF). Although several clinical tests are in use, none accurately distinguish between patients with poor versus excellent survival. We hypothesized that a transcriptomic signature, generated from a single endomyocardial biopsy, could serve as a novel prognostic biomarker in HF. Methods and Results-Endomyocardial biopsy samples and clinical data were collected from all patients presenting with new-onset HF from 1997 to 2006. Among a total of 350 endomyocardial biopsy samples, 180 were identified as idiopathic dilated cardiomyopathy. Patients with phenotypic extremes in survival were selected: good prognosis (event-free survival for at least 5 years; nϭ25) and poor prognosis (events [death, requirement for left ventricular assist device, or cardiac transplant] within the first 2 years of presentation with HF symptoms; nϭ18). We used human U133 Plus 2.0 microarrays (Affymetrix) and analyzed the data with significance analysis of microarrays and prediction analysis of microarrays. We identified 46 overexpressed genes in patients with good versus poor prognosis, of which 45 genes were selected by prediction analysis of microarrays for prediction of prognosis in a train set (nϭ29) with subsequent validation in test sets (nϭ14 each). The biomarker performed with 74% sensitivity (95% CI 69% to 79%) and 90% specificity (95% CI 87% to 93%) after 50 random partitions. Conclusions-These findings suggest the potential of transcriptomic biomarkers to predict prognosis in patients with new-onset HF from a single endomyocardial biopsy sample. In addition, our findings offer potential novel therapeutic targets for HF and cardiomyopathy. (Circulation. 2008;118:238-246.)
Altered Patterns of Gene Expression in Response to Myocardial Infarction
Circulation Research, 2000
The use of cDNA microarrays has made it possible to simultaneously analyze gene expression for thousands of genes. Microarray technology was used to evaluate the expression of Ͼ4000 genes in a rat model of myocardial infarction. More than 200 genes were identified that showed differential expression in response to myocardial infarction. Gene expression changes were monitored from 2 to 16 weeks after infarction in 2 regions of the heart, the left ventricle free wall and interventricular septum. A novel clustering program was used to identify patterns of expression within this large set of data. Unique patterns were revealed within the transcriptional responses that illuminate changes in biological processes associated with myocardial infarction.
PLoS ONE, 2012
Background: Despite a substantial progress in diagnosis and therapy, acute myocardial infarction (MI) is a major cause of mortality in the general population. A novel insight into the pathophysiology of myocardial infarction obtained by studying gene expression should help to discover novel biomarkers of MI and to suggest novel strategies of therapy. The aim of our study was to establish gene expression patterns in leukocytes from acute myocardial infarction patients.
Circulation, 2004
Background-Gene expression profiling refines diagnostic and prognostic assessment in oncology but has not yet been applied to myocardial diseases. We hypothesized that gene expression differentiates ischemic and nonischemic cardiomyopathy, demonstrating that gene expression profiling by clinical parameters is feasible in cardiology. Methods and Results-Affymetrix U133A microarrays of 48 myocardial samples from Johns Hopkins Hospital (JHH) and
Transcriptomal Insights of Heart Failure from Normality to Recovery
Biomolecules
Current management of heart failure (HF) is centred on modulating the progression of symptoms and severity of left ventricular dysfunction. However, specific understandings of genetic and molecular targets are needed for more precise treatments. To attain a clearer picture of this, we studied transcriptome changes in a chronic progressive HF model. Fifteen sheep (Ovis aries) underwent supracoronary aortic banding using an inflatable cuff. Controlled and progressive induction of pressure overload in the LV was monitored by echocardiography. Endomyocardial biopsies were collected throughout the development of LV failure (LVF) and during the stage of recovery. RNA-seq data were analysed using the PANTHER database, Metascape, and DisGeNET to annotate the gene expression for functional ontologies. Echocardiography revealed distinct clinical differences between the progressive stages of hypertrophy, dilatation, and failure. A unique set of transcript expressions in each stage was identifi...
Identification of key genes related to heart failure by analysis of expression profiles
Archives of Medical Science, 2021
IntroductionTo elucidate the candidate biomarkers involved in the patho�genesis process of heart failure (HF) via analysis of differentially expressed genes (DEGs) of the dataset from the Gene Expression Omnibus (GEO).Material and methodsThe GSE76701 gene expression profiles regarding the HF and control subjects were respectively analysed. Briefly, DEGs were firstly identified and subjected to Cytoscape plug-in ClueGO + CluePedia and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A protein-protein interaction (PPI) network was then built to analyse the in�teraction between DEGs, followed by the construction of an interaction net�work by combining with hub genes with the targeted miRNA genes of DEGs to identify the key molecules of HF. In addition, potential drugs targeting key DEGs were sought using the drug-gene interaction database (DGIdb), and a drug-mRNA-miRNA interaction network was also constructed.ResultsA total of 489 DEGs were verified between HF and co...
Scientific Reports, 2019
Heart failure affects 2–3% of adult Western population. Prevalence of heart failure with preserved left ventricular (LV) ejection fraction (HFpEF) increases. Studies suggest HFpEF patients to have altered myocardial structure and functional changes such as incomplete relaxation and increased cardiac stiffness. We hypothesised that patients undergoing elective coronary bypass surgery (CABG) with HFpEF characteristics would show distinctive gene expression compared to patients with normal LV physiology. Myocardial biopsies for mRNA expression analysis were obtained from sixteen patients with LV ejection fraction ≥45%. Five out of 16 patients (31%) had echocardiographic characteristics and increased NTproBNP levels indicative of HFpEF and this group was used as HFpEF proxy, while 11 patients had Normal LV physiology. Utilising principal component analysis, the gene expression data clustered into two groups, corresponding to HFpEF proxy and Normal physiology, and 743 differentially expr...
Genome Medicine, 2014
Background: Genetic risk scores have been developed for coronary artery disease and atherosclerosis, but are not predictive of adverse cardiovascular events. We asked whether peripheral blood expression profiles may be predictive of acute myocardial infarction (AMI) and/or cardiovascular death. Methods: Peripheral blood samples from 338 subjects aged 62 ± 11 years with coronary artery disease (CAD) were analyzed in two phases (discovery N = 175, and replication N = 163), and followed for a mean 2.4 years for cardiovascular death. Gene expression was measured on Illumina HT-12 microarrays with two different normalization procedures to control technical and biological covariates. Whole genome genotyping was used to support comparative genome-wide association studies of gene expression. Analysis of variance was combined with receiver operating curve and survival analysis to define a transcriptional signature of cardiovascular death.
Clinica Chimica Acta, 2013
Background: The aim of this study was to identify novel candidate biomarker proteins differentially expressed in the plasma of patients with early stage acute myocardial infarction (AMI) using SELDI-TOF-MS as a high throughput screening technology. Methods: Ten individuals with recent acute ischemic-type chest pain (b 12 h duration) and ST-segment elevation AMI (1STEMI) and after a second AMI (2STEMI) were selected. Blood samples were drawn at six times after STEMI diagnosis. The first stage (T 0 ) was in Emergency Unit before receiving any medication, the second was just after primary angioplasty (T 2 ), and the next four stages occurred at 12 h intervals after T 0 . Individuals (n= 7) with similar risk factors for cardiovascular disease and normal ergometric test were selected as a control group (CG). Plasma proteomic profiling analysis was performed using the top-down (i.e. intact proteins) SELDI-TOF-MS, after processing in a Multiple Affinity Removal Spin Cartridge System (Agilent). Results: Compared with the CG, the 1STEMI group exhibited 510 differentially expressed protein peaks in the first 48 h after the AMI (pb 0.05). The 2STEMI group, had~85% fewer differently expressed protein peaks than those without previous history of AMI (76, p b 0.05). Among the 16 differentially-regulated protein peaks common to both STEMI cohorts (compared with the CG at T 0 ), 6 peaks were persistently down-regulated at more than one time-stage, and also were inversed correlated with serum protein markers (cTnI, CK and CKMB) during 48 h-period after IAM. Conclusions: Proteomic analysis by SELDI-TOF-MS technology combined with bioinformatics tools demonstrated differential expression during a 48 h time course suggests a potential role of some of these proteins as biomarkers for the very early stages of AMI, as well as for monitoring early cardiac ischemic recovery.