Integrating transcriptomic analysis and experimental validation unravels autophagy- and lactylation-related gene signatures in acute myocardial infarction.
- 1Department of Cardiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, China.
- 2Department of Cardiology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China.
Abstract
Background
Acute myocardial infarction (AMI) is a leading global health burden. While autophagy and lactylation are crucial in cardiovascular pathophysiology, their coordinated roles in AMI remain unclear. This study investigates the transcriptional signatures associated with both processes to identify diagnostic biomarkers.Methods
Transcriptomic datasets (GSE66360, GSE48060), autophagy-related genes (ARGs), and lactylation-related genes (LRGs) were analyzed. Differentially expressed ARGs and LRGs (DE-ARGs, DE-LRGs). Weighted gene co-expression network analysis (WGCNA) pinpointed key module genes correlated with their activity scores. Biomarker selection employed protein-protein interaction networks, least absolute shrinkage and selection operator (LASSO) regression, artificial neural network (ANN) model, and validation in clinical samples.Results
WGCNA identified 1,442 key module genes. ATM and STAT1 were validated as robust diagnostic biomarkers. Models built with these genes showed high diagnostic accuracy in independent cohorts. Functional enrichment implicated the neuroactive ligand-receptor interaction pathway. Immune infiltration analysis revealed a statistically significant negative correlation between ATM/STAT1 expression and eosinophil infiltration levels (cor=-0.42, p < 0.05), suggesting a potential association. Rat models showed a trend toward downregulation of both biomarkers in AMI, although these findings require validation in larger cohorts due to the limited sample size (n = 4 per group).Conclusions
This integrative study identifies ATM and STAT1 as promising diagnostic biomarkers for AMI, with their expression signatures robustly correlated with both autophagy- and lactylation-associated gene activity scores. These findings provide a foundation for further investigation into the potential roles of these genes in linking metabolic reprogramming and inflammatory responses in AMI.
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BibTeX
@article{peng2026integrating,
title = {{Integrating transcriptomic analysis and experimental validation unravels autophagy- and lactylation-related gene signatures in acute myocardial infarction.}},
author = {Jun Peng and Li Chen and Lizhen Liu and Xiangdong Xu and Huiwen Song},
journal = {Frontiers in cardiovascular medicine},
year = {2026},
month = oct,
doi = {10.3389/fcvm.2026.1814910},
url = {https://europepmc.org/article/MED/42909027},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrating transcriptomic analysis and experimental validation unravels autophagy- and lactylation-related gene signatures in acute myocardial infarction.
AU - Jun Peng
AU - Li Chen
AU - Lizhen Liu
AU - Xiangdong Xu
AU - Huiwen Song
PY - 2026
DA - 2026/10/07
JO - Frontiers in cardiovascular medicine
DO - 10.3389/fcvm.2026.1814910
UR - https://europepmc.org/article/MED/42909027
AB - Background Acute myocardial infarction (AMI) is a leading global health burden. While autophagy and lactylation are crucial in cardiovascular pathophysiology, their coordinated roles in AMI remain unclear. This study investigates the transcriptional signatures associated with both processes to identify diagnostic biomarkers.Methods Transcriptomic datasets (GSE66360, GSE48060), autophagy-related genes (ARGs), and lactylation-related genes (LRGs) were analyzed. Differentially expressed ARGs and LRGs (DE-ARGs, DE-LRGs). Weighted gene co-expression network analysis (WGCNA) pinpointed key module genes correlated with their activity scores. Biomarker selection employed protein-protein interaction networks, least absolute shrinkage and selection operator (LASSO) regression, artificial neural network (ANN) model, and validation in clinical samples.Results WGCNA identified 1,442 key module genes. ATM and STAT1 were validated as robust diagnostic biomarkers. Models built with these genes showed high diagnostic accuracy in independent cohorts. Functional enrichment implicated the neuroactive ligand-receptor interaction pathway. Immune infiltration analysis revealed a statistically significant negative correlation between ATM/STAT1 expression and eosinophil infiltration levels (cor=-0.42, p < 0.05), suggesting a potential association. Rat models showed a trend toward downregulation of both biomarkers in AMI, although these findings require validation in larger cohorts due to the limited sample size (n = 4 per group).Conclusions This integrative study identifies ATM and STAT1 as promising diagnostic biomarkers for AMI, with their expression signatures robustly correlated with both autophagy- and lactylation-associated gene activity scores. These findings provide a foundation for further investigation into the potential roles of these genes in linking metabolic reprogramming and inflammatory responses in AMI.
SN - 2297-055X
ER -