Integrating transcriptomic analysis and experimental validation unravels autophagy- and lactylation-related gene signatures in acute myocardial infarction.

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.