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Identification of lactylation-related genes associated with heart failure via bioinformatics and machine learning
Yan Zhang1, Haixia Luo1, Xiaojie Jia1
1Department of Cardiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710000, China.
Insights
Researchers identified four lactylation-related genes (HLTF, HMGN2, ARGLU1, LSP1) as potential heart failure (HF) biomarkers. Machine learning models accurately diagnosed HF, offering new avenues for early detection and treatment.
Area of Science:
- Biomolecular mechanisms
- Cardiovascular disease research
- Computational biology
Background:
- Heart failure (HF) represents the advanced stage of cardiovascular disease, characterized by high global incidence and unfavorable outcomes.
- Identifying novel diagnostic biomarkers is crucial for improving HF patient prognosis and management.
Purpose of the Study:
- To pinpoint lactylation-related genes (LRGs) as potential diagnostic biomarkers for heart failure (HF).
- To develop and validate a machine learning-based diagnostic model for HF using identified LRGs.
Main Methods:
- Utilized gene expression data from the GEO database for HF patients and healthy controls.
- Integrated LRGs from existing literature with differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to identify HF-specific LRGs.
- Constructed and validated diagnostic models using machine learning techniques, including external datasets and animal models.
Main Results:
- Identified four key upregulated genes in HF patients: HLTF, HMGN2, ARGLU1, and LSP1.
- Developed nine machine learning models with Area Under the Curve (AUC) values exceeding 0.9, indicating high diagnostic accuracy.
- Validated gene expression trends in an external dataset (GSE84796) and in a mouse model of cardiac hypertrophy (TAC), confirming upregulation of the four hub genes.
- Gene Set Enrichment Analysis (GSEA) associated these hub genes with critical metabolic and disease-related pathways.
Conclusions:
- The identified LRGs (HLTF, HMGN2, ARGLU1, LSP1) show promise as novel diagnostic biomarkers for heart failure.
- The machine learning diagnostic model demonstrates significant accuracy and potential for clinical application in the early diagnosis and treatment of HF.
Background:
Heart failure (HF) is the terminal stage of cardiovascular disease with high global prevalence and poor prognosis. This study aimed to identify lactylation-related genes as novel diagnostic biomarkers for HF using bioinformatics and machine learning approaches.
Methods:
Gene expression data of HF patients and healthy controls were obtained from the GEO database. Combined with lactylation-related genes (LRGs) from literature, differentially expressed genes and weighted gene co-expression network analysis (WGCNA) were used to identify HF-LRGs. A diagnostic model was constructed using machine learning and validated with external datasets and animal experiments.
Results:
Four hub genes (HLTF, HMGN2, ARGLU1, and LSP1) were identified, all significantly upregulated in HF patients. Nine machine learning models achieved AUC values > 0.9, demonstrating high diagnostic accuracy. The expression trends were validated in the GSE84796 dataset and in TAC mouse hearts, where qRT-PCR confirmed upregulation of all four genes. GSEA linked hub genes to multiple metabolic and disease-related pathways.
Conclusion:
The identified lactylation-related genes, particularly HLTF, HMGN2, ARGLU1, and LSP1, serve as potential diagnostic biomarkers for heart failure. The machine learning-based diagnostic model shows high accuracy and clinical application potential, offering new perspectives for early diagnosis and treatment of HF.