ReviewJournal of the American Heart Association2021
Consensus Transcriptional Landscape of Human End-Stage Heart Failure.
Review in Journal of the American Heart Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 70 citations in OpenAlex.
- A comprehensive transcriptional reference for severity and progression in spinal cord injury reveals novel translational biomarker genes.Journal of translational medicine · 2025Pooled it
- The transcriptional landscape of atrial fibrillation: A systematic review and meta-analysis.PloS one · 2025Pooled it
- An integrated cell atlas of 2.4 million cardiac cells across 209 individuals in health and disease.Nature cardiovascular research · 2026Article
- Interleukin-1β Drives Disease Progression in Arrhythmogenic Cardiomyopathy.JACC. Basic to translational science · 2026Article
- A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling.Nature communications · 2025Article
- RBPMS and RBPMS2 Cooperate to Safeguard Cardiac Splicing.Circulation research · 2025Article
- 401-Gene Signature of Myocardial Dysfunction in Human Heart Failure: A Transcriptomic Analysis.Cureus · 2025Article
- Spatial transcriptional landscape of human heart failure.European heart journal · 2025Article
- Transcriptional changes of the extracellular matrix in chronic thromboembolic pulmonary hypertension govern right ventricle remodeling and recovery.Nature cardiovascular research · 2025Article
- Transcriptional Signatures of the Right Ventricle in End-Stage Heart Failure.Pulmonary circulation · 2025Article
- Integrated transcriptomic and regulatory RNA profiling reflects complex pathophysiology and uncovers a conserved gene signature in end stage heart failure.Journal of molecular and cellular cardiology plus · 2025Article
- Analysis and validation of characteristic genes in RNA sequencing datasets from heart failure patients based on multiple algorithms.Frontiers in cardiovascular medicine · 2025Article
- Interleukin-1β Drives Disease Progression in Arrhythmogenic Cardiomyopathy.bioRxiv : the preprint server for biology · 2024Article
- Single-cell transcriptomics reveal distinctive patterns of fibroblast activation in heart failure with preserved ejection fraction.Basic research in cardiology · 2024Article
- Proteome-wide copy-number estimation from transcriptomics.Molecular systems biology · 2024Article
- Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results.Nucleic acids research · 2024Article
- Transverse aortic constriction multi-omics analysis uncovers pathophysiological cardiac molecular mechanisms.Database : the journal of biological databases and curation · 2024Article
- A common gene signature of the right ventricle in failing rat and human hearts.Nature cardiovascular research · 2024Article
- RNA-binding proteins in cardiovascular biology and disease: the beat goes on.Nature reviews. Cardiology · 2024Review
- Using Omics to Identify Novel Therapeutic Targets in Heart Failure.Circulation. Genomic and precision medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Transcriptomic studies have contributed to fundamental knowledge of myocardial remodeling in human heart failure (HF). However, the key HF genes reported are often inconsistent between studies, and systematic efforts to integrate evidence from multiple patient cohorts are lacking. Here, we aimed to provide a framework for comprehensive comparison and analysis of publicly available data sets resulting in an unbiased consensus transcriptional signature of human end-stage HF. Methods and Results We curated and uniformly processed 16 public transcriptomic studies of left ventricular samples from 263 healthy and 653 failing human hearts. First, we evaluated the degree of consistency between studies by using linear classifiers and overrepresentation analysis. Then, we meta-analyzed the deregulation of 14 041 genes to extract a consensus signature of HF. Finally, to functionally characterize this signature, we estimated the activities of 343 transcription factors, 14 signaling pathways, and 182 micro RNAs, as well as the enrichment of 5998 biological processes. Machine learning approaches revealed conserved disease patterns across all studies independent of technical differences. These consistent molecular changes were prioritized with a meta-analysis, functionally characterized and validated on external data. We provide all results in a free public resource (https://saezlab.shinyapps.io/reheat/) and exemplified usage by deciphering fetal gene reprogramming and tracing the potential myocardial origin of the plasma proteome markers in patients with HF. Conclusions Even though technical and sampling variability confound the identification of differentially expressed genes in individual studies, we demonstrated that coordinated molecular responses during end-stage HF are conserved. The presented resource is crucial to complement findings in independent studies and decipher fundamental changes in failing myocardium.
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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.