Evidence map›Paper›PMID 42909027›Full record

ArticleFrontiers in cardiovascular medicine2026

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

Jun Peng, Li Chen, Lizhen Liu, Xiangdong Xu, Huiwen Song

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Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Jun PengDepartment of Cardiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, China.
Li ChenDepartment of Cardiology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China.
Lizhen LiuDepartment of Cardiology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China.
Xiangdong XuDepartment of Cardiology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China.
Huiwen SongDepartment of Cardiology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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, 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.

Indexed as

acute myocardial infarctionautophagybiomarkerslactylationmachine learning

Identifiers

PMID42909027
PMCPMC13683661

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