A Lactylation‐Based Diagnostic Model Reveals Molecular Subtypes and Therapeutic Targets in Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a progressive myocardial disorder lacking reliable molecular biomarkers for early diagnosis. Given the emerging role of protein lactylation in cardiovascular disease, we investigated lactylation-related genes (LRGs) in DCM using transcriptomic data from the GEO database. Differential gene expression and lactylation databases were integrated to identify 18 dysregulated LRGs. Using ensemble machine learning algorithms, six core LRGs (G6PD, PPP1CC, MBP, LSP1, HMGN1, HMGN2) were selected to construct a diagnostic model, which showed robust performance across five external cohorts. Unsupervised clustering revealed two molecular DCM subtypes. Immune infiltration and ssGSEA analyses suggested that core LRGs modulate immunometabolic remodeling. Single-cell RNA-seq analysis confirmed their cell-type-specific distribution, with fibroblasts identified as dominant signaling sources via CellChat analysis. Ferrostatin-1 and Resveratrol were predicted as potential therapeutic compounds targeting core genes. Validation in human and mouse myocardial tissues confirmed differential gene and protein expression. This study uncovers lactylation-driven molecular signatures in DCM and establishes a robust diagnostic model with promising translational potential. By linking lactate metabolism to immune regulation and cardiac remodeling, our findings highlight novel diagnostic markers and potential therapeutic targets for precision intervention in DCM.

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Publication Details

Journal
The FASEB Journal
Published
2025-10-25
DOI
https://doi.org/10.1096/fj.202502739r
Citations
1
Primary Topic
RNA modifications and cancer
Type
article
Field-Weighted Citation Impact
0.81
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article

A Lactylation‐Based Diagnostic Model Reveals Molecular Subtypes and Therapeutic Targets in Dilated Cardiomyopathy

Yafeng Zhou, Rui Ge, Laichun Song, Shuai Xu et al.
1 citations
The FASEB Journal
RNA modifications and cancer
0.81
article

A Lactylation‐Based Diagnostic Model Reveals Molecular Subtypes and Therapeutic Targets in Dilated Cardiomyopathy

Yafeng Zhou, Rui Ge, Laichun Song, Shuai Xu, Yufeng Jiang, Yantong Li, Anchen Xu, Yuxin Nong, Xin Tan, Huimin Fan, Xiangyu Wang, Qiaoyi Sun
article en
1 citations

Abstract

Dilated cardiomyopathy (DCM) is a progressive myocardial disorder lacking reliable molecular biomarkers for early diagnosis. Given the emerging role of protein lactylation in cardiovascular disease, we investigated lactylation-related genes (LRGs) in DCM using transcriptomic data from the GEO database. Differential gene expression and lactylation databases were integrated to identify 18 dysregulated LRGs. Using ensemble machine learning algorithms, six core LRGs (G6PD, PPP1CC, MBP, LSP1, HMGN1, HMGN2) were selected to construct a diagnostic model, which showed robust performance across five external cohorts. Unsupervised clustering revealed two molecular DCM subtypes. Immune infiltration and ssGSEA analyses suggested that core LRGs modulate immunometabolic remodeling. Single-cell RNA-seq analysis confirmed their cell-type-specific distribution, with fibroblasts identified as dominant signaling sources via CellChat analysis. Ferrostatin-1 and Resveratrol were predicted as potential therapeutic compounds targeting core genes. Validation in human and mouse myocardial tissues confirmed differential gene and protein expression. This study uncovers lactylation-driven molecular signatures in DCM and establishes a robust diagnostic model with promising translational potential. By linking lactate metabolism to immune regulation and cardiac remodeling, our findings highlight novel diagnostic markers and potential therapeutic targets for precision intervention in DCM.

The FASEB JournalVol. 39(20)
Wuhan University (CN), Soochow University (CN), Wuhan Asia Heart Hospital (CN), Wuhan University of Science and Technology (CN), The Fourth Affiliated Hospital of Soochow University (Suzhou Dushu Lake Hospital)
Openalex Percentile: Top 22%
RNA modifications and cancer
0.81
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