通过生物信息学和机器学习方法识别与心力衰竭相关的乳酰化相关基因
Identification of lactylation-related genes associated with heart failure via bioinformatics and machine learning approaches.
摘要(中文译文)
心力衰竭（HF）是心血管疾病的终末阶段，全球患病率高且预后不良。本研究旨在利用生物信息学和机器学习方法，识别乳酸化相关基因作为心力衰竭的新型诊断生物标志物。从GEO数据库获取心力衰竭患者和健康对照者的基因表达数据。结合文献中的乳酸化相关基因（LRGs），采用差异表达基因分析和加权基因共表达网络分析（WGCNA）识别心力衰竭乳酸化相关基因（HF-LRGs）。利用机器学习构建诊断模型，并通过外部数据集和动物实验进行验证。共识别出四个核心基因（HLTF、HMGN2、ARGLU1和LSP1），均在心力衰竭患者中显著上调。九种机器学习模型的AUC值均大于0.9，显示出较高的诊断准确性。在GSE84796数据集和TAC小鼠心脏中验证了表达趋势，qRT-PCR证实四个基因均上调。基因集富集分析（GSEA）将核心基因与多条代谢及疾病相关通路相关联。所识别的乳酸化相关基因，尤其是HLTF、HMGN2、ARGLU1和LSP1，可作为心力衰竭的潜在诊断生物标志物。基于机器学习的诊断模型具有较高的准确性和临床应用潜力，为心力衰竭的早期诊断和治疗提供了新视角。
Abstract
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. 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. 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. 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.
如何引用
AMA
Yan Zhang, Haixia Luo, Xiaojie Jia, Renqian Feng, Kexin Wang, Ran Zhao, et al. Identification of lactylation-related genes associated with heart failure via bioinformatics and machine learning approaches.. Biochemical and biophysical research communications. 2026; doi:10.1016/j.bbrc.2026.154301.
APA
Yan Zhang, Haixia Luo, Xiaojie Jia, Renqian Feng, Kexin Wang, Ran Zhao, et al (2026). Identification of lactylation-related genes associated with heart failure via bioinformatics and machine learning approaches.. Biochemical and biophysical research communications. https://doi.org/10.1016/j.bbrc.2026.154301
GB/T 7714
Yan Zhang, Haixia Luo, Xiaojie Jia, Renqian Feng, Kexin Wang, Ran Zhao, et al. Identification of lactylation-related genes associated with heart failure via bioinformatics and machine learning approaches.[J]. Biochemical and biophysical research communications, 2026 doi:10.1016/j.bbrc.2026.154301.
在 PubMed增强版 阅读全文
翻译全文 · AI 文献问答 · PDF 原文下载 · 引用导出