Dataset Information

Coordinated modular functionality and prognostic potential of a heart failure biomarker-driven interaction network.

ABSTRACT:

Background

The identification of potentially relevant biomarkers and a deeper understanding of molecular mechanisms related to heart failure (HF) development can be enhanced by the implementation of biological network-based analyses. To support these efforts, here we report a global network of protein-protein interactions (PPIs) relevant to HF, which was characterized through integrative bioinformatic analyses of multiple sources of "omic" information.

Results

We found that the structural and functional architecture of this PPI network is highly modular. These network modules can be assigned to specialized processes, specific cellular regions and their functional roles tend to partially overlap. Our results suggest that HF biomarkers may be defined as key coordinators of intra- and inter-module communication. Putative biomarkers can, in general, be distinguished as "information traffic" mediators within this network. The top high traffic proteins are encoded by genes that are not highly differentially expressed across HF and non-HF patients. Nevertheless, we present evidence that the integration of expression patterns from high traffic genes may support accurate prediction of HF. We quantitatively demonstrate that intra- and inter-module functional activity may be controlled by a family of transcription factors known to be associated with the prevention of hypertrophy.

Conclusion

The systems-driven analysis reported here provides the basis for the identification of potentially novel biomarkers and understanding HF-related mechanisms in a more comprehensive and integrated way.

SUBMITTER: Azuaje F

PROVIDER: S-EPMC2890499 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Publications

Coordinated modular functionality and prognostic potential of a heart failure biomarker-driven interaction network.

Azuaje Francisco F Devaux Yvan Y Wagner Daniel R DR

BMC systems biology 20100512

<h4>Background</h4>The identification of potentially relevant biomarkers and a deeper understanding of molecular mechanisms related to heart failure (HF) development can be enhanced by the implementation of biological network-based analyses. To support these efforts, here we report a global network of protein-protein interactions (PPIs) relevant to HF, which was characterized through integrative bioinformatic analyses of multiple sources of "omic" information.<h4>Results</h4>We found that the st ...[more]

PMID: 20462429

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Project description:AimsThe aim of this study was to generate a biomarker-driven prognostic tool for patients with chronic HFrEF. Circulating levels of N-terminal pro B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT) each have a marked positive relationship with adverse outcomes in heart failure with reduced ejection fraction (HFrEF). A risk model incorporating biomarkers and clinical variables has not been validated in contemporary heart failure (HF) trials.Methods and resultsIn EMPEROR-Reduced, 33 candidate variables were pre-selected. Multivariable Cox regression models were developed using stepwise selection for: (i) the primary composite outcome of HF hospitalization or cardiovascular death, (ii) all-cause death, and (iii) cardiovascular mortality. A total of 3730 patients were followed up for a median of 16 months, 823 (22%) patients had a primary outcome and 515 (14%) patients died, of whom 389 (10%) died from a cardiovascular cause. NT-proBNP and hs-cTnT were the dominant predictors of the primary outcome, and in addition, a shorter time since last HF hospitalization, longer time since HF diagnosis, lower systolic blood pressure, New York Heart Association (NYHA) Class III or IV, higher heart rate and peripheral oedema were key predictors (eight variables in total, all P < 0.001). The primary outcome risk score discriminated well (c-statistic = 0.73), with patients in the top 10th of risk having an event rate >9 times higher than those in the bottom 10th. Empagliflozin benefitted patients across risk levels for the primary outcome. NT-proBNP and hs-cTnT were also the dominant predictors of all-cause and cardiovascular mortality, followed by NYHA Class III or IV and ischaemic aetiology (four variables in total, all P < 0.001). The mortality risk model presented good event discrimination for all-cause and cardiovascular mortality (c-statistic = 0.69 for both). These simple models were externally validated in the BIOSTAT-CHF study, achieving similar c-statistics.ConclusionsThe combination of NT-proBNP and hs-cTnT with a small number of readily available clinical variables provides prognostic assessment for patients with HFrEF. This predictive tool kit can be easily implemented for routine clinical use.

Project description:AimsInsulin-like growth factor binding protein-4 (IGFBP-4) fragments have been shown to predict the risk of major adverse cardiovascular events, including segment-elevation myocardial infarction, in patients with acute coronary syndrome. We evaluated the prognostic value of the carboxy-terminal fragment of IGFBP-4 (CT-IGFBP-4) for all-cause mortality in emergency room patients with acute heart failure (AHF).Methods and resultsCT-IGFBP-4, N-terminal pro brain natriuretic peptide (NT-proBNP), and C-reactive protein (CRP) were measured at admission from the lithium-heparin plasma of 156 patients with AHF. All-cause mortality was recorded for 1 year. Receiver operator characteristic (ROC) curves, Kaplan-Meier, and Cox proportional hazard ratio analyses were performed to evaluate the prognostic value of the various clinical variables, CT-IGFBP-4, NT-proBNP, CRP, and their combinations. During 1 year of follow-up, 52 (33.3%) patients died. CT-IGFBP-4 only weakly correlated with NT-proBNP (Pearson correlation coefficient r = 0.16, P = 0.044) and did not correlate with CRP (r = 0.08, P = 0.35), emphasizing the different nature of these biomarkers. The receiver operator characteristic area under the curve (ROC AUC) of CT-IGFBP-4 for the prediction of all-cause mortality (0.727) was significantly higher than that of NT-proBNP (0.680, P = 0.045) and CRP (0.669, P = 0.016). The combination of CT-IGFBP-4, NT-proBNP, and CRP predicted mortality significantly better (ROC AUC = 0.788) than any of the biomarkers alone (P < 0.01 for all). The addition of CT-IGFBP-4 to a clinical prediction model that included age, gender, systolic blood pressure, creatinine, and sodium levels, as well as the history of previous heart failure, coronary artery disease, and hypertension significantly improved the mortality risk prediction (ROC AUC 0.774 vs. 0.699, P = 0.025). Cox hazard analysis indicated that elevated CT-IGFBP-4 was independently associated with 1 year mortality (hazard ratio 3.26, P = 0.0008) after adjustment for age, gender, history of previous heart failure, coronary artery disease, hypertension, chronic kidney failure, history of diabetes, heart rate, haemoglobin, plasma sodium, NT-proBNP, CRP, cystatin C, and elevated cardiac troponin I or T. Patients with increased levels of either two or three of the biomarkers CT-IGFBP-4, NT-proBNP, and CRP had significantly higher mortality risk (adjusted hazard ratio 10.04, P < 0.0001) than patients with increased levels of one or none of the biomarkers.ConclusionsCT-IGFBP-4 was independently associated with all-cause mortality in patients with AHF. Compared with single biomarkers, the combination of CT-IGFBP-4, NT-proBNP, and CRP improved the prediction of all-cause mortality in patients with AHF.

Project description:Background Circulating microRNAs are emerging biomarkers for heart failure (HF). Our study aimed to assess the prognostic value of microRNA signature that is differentially expressed in patients with acute HF. Methods and Results Our study comprised a screening cohort of 15 patients with AHF and 5 controls, a PCR-discovery cohort of 50 patients with AHF and 26 controls and a validation cohort of 564 patients with AHF from registered study DRAGON-HF (Diagnostic, Risk Stratification and Prognostic Value of Novel Biomarkers in Patients With Heart Failure). Through screening by RNA-sequencing and verification by reverse-transcription quantitative polymerase chain reaction, 9 differentially expressed microRNAs were verified (miR-939-5p, miR-1908-5p, miR-7706, miR-101-3p, miR-144-3p, miR-4732-3p, miR-3615, miR-484 and miR-19b-3p). Among them, miR-19b-3p was identified as the microRNA signature with the highest fold-change of 8.4 and the strongest prognostic potential (area under curve with 95% CI, 0.791, 0.654-0.927). To further validate its prognostic value, in the validation cohort, the baseline level of miR-19b-3p was measured. During a follow-up period of 19.1 (17.7, 20.7) months, primary end point comprising of all-cause mortality or readmission due to HF occurred in 48.9% patients, while patients in the highest quartile of miR-19b-3p level presented the worst survival (Log-rank P<0.001). Multivariate Cox model showed that the level of miR-19b-3p could independently predict the occurrence of primary end point (adjusted hazard ratio,1.39; 95% CI, 1.18-1.64). In addition, miR-19b-3p positively correlated with soluble suppression of tumorigenicity 2 and echocardiographic indexes of left ventricular hypertrophy. Conclusions Circulating miR-19b-3p could be a valuable prognostic biomarker for AHF. In addition, a high level of circulating miR-19b-3p might indicate ventricular hypertrophy in AHF subjects. Registration URL: https://www.clinicaltrials.gov. Unique Identifier: NCT03727828.

Dataset Information

Coordinated modular functionality and prognostic potential of a heart failure biomarker-driven interaction network.

Background

Results

Conclusion

Publications

Coordinated modular functionality and prognostic potential of a heart failure biomarker-driven interaction network.

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