.png){kind=icon}
.png){kind=icon}
Return
Transcriptomic landscape of human cardiac aging: identification of cardioselective age-associated genes and predictive modeling
.png){kind=icon}
PRE
AI
S
Y
DOI：10.1007/s10522-026-10479-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Age is a major risk factor for cardiovascular disease, yet the molecular mechanisms underlying human cardiac aging are not fully understood. Using bulk RNA sequencing data from the Genotype-Tissue Expression (GTEx) project, we identified cardiac age-associated genes by intersecting transcripts significantly correlated with age in both the atrial appendage and left ventricle, while excluding those similarly altered in skeletal muscle. Among these, 131 genes were positively correlated with age, and 262 were negatively correlated. Functional enrichment and gene set enrichment analyses revealed several recurring biological themes. Pathways positively enriched with age included the P53 pathway, replicative senescence, extracellular matrix organization, transient receptor potential channel activity, and cardiac epithelial-mesenchymal transition. Conversely, negatively enriched pathways involved fatty acid oxidation, oxidative phosphorylation, mitochondrial biogenesis, membrane repolarization, cardiac conduction, and NOS1 signaling. Immune deconvolution indicated an age-dependent increase in neutrophil fractions and a decrease in monocyte abundance. Finally, an ordinal elastic net regression model, trained on these cardiac age-associated genes, demonstrated acceptable internal performance, achieving a quadratic weighted kappa of 0.797 and an ordinal concordance index of 0.901. External validation on two independent microarray cohorts showed modest predictive capability, with R2 values of 0.20 and 0.38. These findings provide a transcriptomic landscape of cardiac aging based on human tissue, with potential implications for biomarker development and therapeutic strategies.
Keywords:
Aging
Gene expression
GTEx
Heart
Regularized ordinal regression
Journal
IF:
4.1
Papers:
1.7K
Citations:
3.5K
Organization
Cited Papers