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Blood gene expression signatures associate with heart failure outcomes.

ABSTRACT: Gene expression signatures in blood correlate with specific diseases. Such signatures may serve as valuable diagnostic and prognostic tools in disease management. Blood gene expression signatures associated with heart failure may be applied to predict prognosis, monitor disease progression, and optimize treatment. Blood gene expression profiles were generated for 71 subjects with heart failure and 15 controls without heart failure, using the Affymetrix GeneChip U133Plus2.0. Survival analysis identified 197 "mortality genes" that were significantly associated with patient outcome. Functional categorization showed that genes associated with T cell receptor signaling were most significantly overpresented. Cluster analysis of these T cell receptor signaling genes significantly categorized heart failure patients into three risk groups (P = 0.031) that were distinct from the three risk groups categorized by New York Heart Association (NYHA) Classification (P = 0.0002). By combining the analysis of clinical assessment (NYHA class) with T cell receptor signaling gene expression, we proposed a model that demonstrated an even greater differentiation of patients at risk (P = 0.0001). In this discovery study, we identified blood expression signatures associated with heart failure patient outcomes. Characterization of these mortality genes helped identify a set of T cell receptor signaling genes that may be of utility in predicting survival of heart failure patients. These data raise the possibility of prospectively risk stratifying patients with heart failure by integrating blood gene expression signatures with current clinical assessment.

SUBMITTER: Vanburen P

PROVIDER: S-EPMC3092336 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Blood gene expression signatures associate with heart failure outcomes.

Vanburen Peter P Ma Jun J Chao Samuel S Mueller Enkhtuyaa E Schneider David J DJ Liew Choong-Chin CC

Physiological genomics 20110125 8

Gene expression signatures in blood correlate with specific diseases. Such signatures may serve as valuable diagnostic and prognostic tools in disease management. Blood gene expression signatures associated with heart failure may be applied to predict prognosis, monitor disease progression, and optimize treatment. Blood gene expression profiles were generated for 71 subjects with heart failure and 15 controls without heart failure, using the Affymetrix GeneChip U133Plus2.0. Survival analysis ide ...[more]

PMID: 21266504

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Project description:BackgroundHeart failure (HF) with preserved ejection fraction (HFpEF) constitutes half of all HF but lacks effective therapy. Understanding of its myocardial biology remains limited because of a paucity of heart tissue molecular analysis.MethodsWe performed RNA sequencing on right ventricular septal endomyocardial biopsies prospectively obtained from patients meeting consensus criteria for HFpEF (n=41) contrasted with right ventricular septal tissue from patients with HF with reduced ejection fraction (HFrEF, n=30) and donor controls (n=24). Principal component analysis and hierarchical clustering tested for transcriptomic distinctiveness between groups, effect of comorbidities, and differential gene expression with pathway enrichment contrasted HF groups and donor controls. Within HFpEF, non-negative matrix factorization and weighted gene coexpression analysis identified molecular subgroups, and the resulting clusters were correlated with hemodynamic and clinical data.ResultsPatients with HFpEF were more often women (59%), African American (68%), obese (median body mass index 41), and hypertensive (98%), with clinical HF characterized by 65% New York Heart Association Class III or IV, nearly all on a loop diuretic, and 70% with a HF hospitalization in the previous year. Principal component analysis separated HFpEF from HFrEF and donor controls with minimal overlap, and this persisted after adjusting for primary comorbidities: body mass index, sex, age, diabetes, and renal function. Hierarchical clustering confirmed group separation. Nearly half the significantly altered genes in HFpEF versus donor controls (1882 up, 2593 down) changed in the same direction in HFrEF; however, 5745 genes were uniquely altered between HF groups. Compared with controls, uniquely upregulated genes in HFpEF were enriched in mitochondrial adenosine triphosphate synthesis/electron transport, pathways downregulated in HFrEF. HFpEF-specific downregulated genes engaged endoplasmic reticulum stress, autophagy, and angiogenesis. Body mass index differences largely accounted for HFpEF upregulated genes, whereas neither this nor broader comorbidity adjustment altered pathways enriched in downregulated genes. Non-negative matrix factorization identified 3 HFpEF transcriptomic subgroups with distinctive pathways and clinical correlates, including a group closest to HFrEF with higher mortality, and a mostly female group with smaller hearts and proinflammatory signaling. These groupings remained after sex adjustment. Weighted gene coexpression analysis yielded analogous gene clusters and clinical groupings.ConclusionsHFpEF exhibits distinctive broad transcriptomic signatures and molecular subgroupings with particular clinical features and outcomes. The data reveal new signaling targets to consider for precision therapeutics.

Project description:BACKGROUND:Clinical variables may correlate with lack of response to treatment (primary resistance) or clinical benefit in patients with clear cell renal cell carcinoma (ccRCC) treated with anti-programmed death 1/ligand one antibodies. METHODS:In this multi-institutional collaboration, clinical characteristics of patients with primary resistance (defined as progression on initial computed tomography scan) were compared to patients with clinical benefit using Two sample t-test and Chi-square test (or Fisher's Exact test). The Kaplan-Meier method was used to estimate the distribution of progression-free survival (PFS) and overall survival (OS) in all patients and the subsets of patients with clinical benefit or primary resistance. Cox's regression model was used to evaluate the correlation between survival endpoints and variables of interest. To explore clinical factors in a larger, independent patient sample, The Cancer Genome Atlas (TCGA) was analyzed. RNAseq gene expression data as well as demographic and clinical information were downloaded for primary tumors of 517 patients included within TCGA-ccRCC. RESULTS:Of 90 patients, 38 (42.2%) had primary resistance and 52 (57.8%) had clinical benefit. Compared with the cohort of patients with initial benefit, primary resistance was more likely to occur in patients with worse ECOG performance status (p?=?0.03), earlier stage at diagnosis (p?=?0.04), had no prior nephrectomy (p?=?0.04) and no immune-related adverse events (irAE) (p?=?0.02). In patients with primary resistance, improved OS was significantly correlated with lower International Metastatic RCC Database Consortium risk score (p?=?0.02) and lower neutrophil:lymphocyte ratio (p?=?0.04). In patients with clinical benefit, improved PFS was significantly associated with increased BMI (p?=?0.007) and irAE occurrence (p?=?0.02) while improved OS was significantly correlated with overweight BMI (BMI 25-30; p?=?0.03) and no brain metastasis (p?=?0.005). The cohort TCGA-ccRCC was examined for the correlations between gene expression patterns, clinical factors, and survival outcomes observing associations of T-cell inflammation and angiogenesis signatures with histologic grade, pathologic stage and OS. CONCLUSIONS:Clinical characteristics including performance status, BMI and occurrence of an irAE associate with outcomes in patients with ccRCC treated with immunotherapy. The inverse association of angiogenesis gene signature with ccRCC histologic grade highlight opportunities for adjuvant combination VEGFR2 tyrosine kinase inhibitor and immune-checkpoint inhibition.

Project description:Background: a considerable numeer of patients with heart failure (HF) present systemic iron deficiency (ID), which is related with worse prognosis and a significant decline in quality of life. The mechanisms that underlay the crosstatlk between myocardial performance, neurohormonal activation and heart metabolism are still to be uncovered. Nonetheless, previous experimental studies have already revealed the potential links between iron, mitochondrial metabolism and heart function. Objectives: The present research attempts to investigate the mechanisms that link HF and ID and to identify potential biomarkers of disease prognosis by determning the gene expression pattern through mRNA transcriptome and PCR analyses. Results: The transcriptome of a first cohort of HF patients was analysed according to the presence of ID, underscoring 1128 overexpressed transcripts in patients with iron dysregulation, which permitted to identify 22 genes involved in biological pathways that could be related to HF and the cellular iron regulation and metabolism. From these 22 selected genes, 6 were found differentially expressed by PCR in a new wider cohort of patients, and comprehensive statistical studies identifying specific genetic patterns in three genes that correlate with prognostic outcomes. Briefly, a genetic expression pattern of the smal integral membrane protein 20 and the sirtuin 7 towards decreased expression (higher Δct) was significantly associated with an increased risk of death and the combined end-point of death and HF hospitalization and the composite. Similarly, the intermediate Δct gene expression pattern of mitochondrial ferritin was also associated of both of previous determined outcomes. Conclusions: Patients with HF present different standards of gene expression according to systemic iron status that involve paramount myocardial metabolic pathways implicated in myocardial function and correlate with prognosis. The study highlights the importance of iron regulation in the comprehensive approach to the treatment of myocardial disease.

Project description:Iron deficiency is a common disorder in patients with heart failure and is related with adverse outcomes and poor quality of life. Previous experimental studies have shown biological connections between iron homeostasis, mitochondrial metabolism, and myocardial function. However, the mechanisms involved in this crosstalk are yet to be unfolded. The present research attempts to investigate the intrinsic biological mechanisms between heart failure and iron deficiency and to identify potential prognostic biomarkers by determining the gene expression pattern in the blood of heart failure patients, using whole transcriptome and targeted TaqMan® low-density array analyses. We performed a stepwise cross-sectional longitudinal study in a cohort of chronic heart failure patients with and without systemic iron deficiency. First, the full transcriptome was performed in a nested case-control exploratory cohort of 7 paired patients and underscored 1128 differentially expressed transcripts according to iron status (cohort1#). Later, we analyzed the messenger RNA levels of 22 genes selected by their statistical significance and pathophysiological relevance, in a validation cohort of 71 patients (cohort 2#). Patients with systemic iron deficiency presented lower mRNA levels of mitochondrial ferritin, sirtuin-7, small integral membrane protein 20, adrenomedullin and endothelin converting enzyme-1. An intermediate mitochondrial ferritin gene expression and an intermediate or low sirtuin7 and small integral membrane protein 20 mRNA levels were associated with an increased risk of all-cause mortality and heart failure admission ((HR 2.40, 95% CI 1.04-5.50, p-value = 0.039), (HR 5.49, 95% CI 1.78-16.92, p-value = 0.003), (HR 9.51, 95% CI 2.69-33.53, p-value < 0.001), respectively). Patients with chronic heart failure present different patterns of blood gene expression depending on systemic iron status that affect pivotal genes involved in iron regulation, mitochondrial metabolism, endothelial function and cardiovascular physiology, and correlate with adverse clinical outcomes.

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Blood gene expression signatures associate with heart failure outcomes.

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Blood gene expression signatures associate with heart failure outcomes.

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