Identification and analysis of diagnostic senescence-related gene signatures for acute myocardial infarction based on
Hanmo Zhang1,2, Hongyu Huang1, Zhuo Jiang1
1School of Medicine, Nankai University, Tianjin, China.
Background:
Acute myocardial infarction (AMI) incidence increases with population aging, alongside heightened cellular senescence; however, clinically useful senescence-related genes (SRGs) for AMI remain poorly defined. This study aimed to identify AMI-associated SRGs and develop multi-dimensional models for diagnostic evaluation and patient stratification.
Methods:
Transcriptomic datasets comprising 106 AMI patients and 76 controls were integrated for differential expression and weighted gene co-expression network analyses. An external independent validation cohort including 37 AMI cases and 15 controls was used for further evaluation. Four machine learning algorithms were applied to identify diagnostic SRGs. The selected genes were validated across bulk RNA-seq, single-cell RNA-seq, proteomic data, and mouse myocardial infarction models. Based on these genes, we constructed three SRG-based models: a diagnostic classifier, a patient stratification system, and a senescence scoring system.
Results:
Thirteen AMI-associated SRGs were identified, among which four genes, FOS, SOD2, MXD1, and GRN, showed consistent diagnostic relevance across datasets. The four-gene diagnostic model achieved an AUC of 0.808 and showed favorable clinical net benefit. Patient stratification identified a low-senescence group enriched in anti-inflammatory cells and a high-senescence group characterized by pro-inflammatory neutrophil infiltration. The senescence score showed a moderate positive correlation with neutrophil infiltration.
Conclusion:
This study identifies FOS, SOD2, MXD1, and GRN as candidate AMI-associated senescence-related biomarkers and establishes preliminary SRG-based models for AMI diagnosis and stratification. These findings suggest that neutrophil-enriched inflammatory responses are associated with senescence-related transcriptional features in AMI and provide a framework for future studies on senescence-related pathways in personalized AMI management.
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