ArticlePhysiological genomics2011
Blood gene expression signatures associate with heart failure outcomes.
Article in Physiological genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 22 citations in OpenAlex.
- Connecting intermediate phenotypes to disease using multi-omics in heart failure.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Connecting intermediate phenotypes to disease using multi-omics in heart failure.medRxiv : the preprint server for health sciences · 2024Article
- Integrated gene expression profiling analysis revealsJournal of thoracic disease · 2022Article
- Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review.Frontiers in cardiovascular medicine · 2022Article
- Expression Profile Analysis of Selenium-Related Genes in Peripheral Blood Mononuclear Cells of Patients with Keshan Disease.BioMed research international · 2019Article
- Gene expression signature for detection of gastric cancer in peripheral blood.Oncology letters · 2018Article
- Article
- Systematic permutation testing in GWAS pathway analyses: identification of genetic networks in dilated cardiomyopathy and ulcerative colitis.BMC genomics · 2014Article
- Preliminary biomarkers for identification of human ascending thoracic aortic aneurysm.Journal of the American Heart Association · 2013Article
- Will global transcriptome analysis allow the detection of novel prognostic markers in coronary artery disease and heart failure?Current genomics · 2013Article
- Blood RNA biomarker panel detects both left- and right-sided colorectal neoplasms: a case-control study.Journal of experimental & clinical cancer research : CR · 2013Article
- Article
- Whole blood transcriptome correlates with treatment response in nasopharyngeal carcinoma.Journal of experimental & clinical cancer research : CR · 2012Article
- The new biology: a bridge to clinical cardiology.Hippokratia · 2012Article
- Transcriptome profiling of peripheral blood cells identifies potential biomarkers for doxorubicin cardiotoxicity in a rat model.PloS one · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.