Chen Jiawei, Yang Siqi, Shou Diwen, Liu Bo, Li Shaohan, Luo Tongtong, Chen Huiting, Huang Chen, Zhou Yongjian
Department of Gastroenterology and Hepatology, Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510006, China.
Department of Gastroenterology and Hepatology, Guangzhou Digestive Disease Center, Guangzhou First People's Hospital, Guangzhou 510013, China.
Biomedicines. 2025 Jul 3;13(7):1636. doi: 10.3390/biomedicines13071636.
: Metabolic-associated fatty liver disease (MAFLD) is characterized by metabolic syndrome and immune infiltration, with glycolysis pathway activation emerging as a pivotal contributor. This study aims to identify glycolysis-associated key genes driving MAFLD progression and elucidate their crosstalk with immune infiltration through bioinformatics analysis and experimental validation. : Integrative multi-omics analysis was performed on bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets from MAFLD patients and controls. Differential expression analysis and WGCNA were employed to pinpoint glycolysis-correlated key genes. The relationship with immune infiltration was analyzed using single-cell and spatial transcriptomics technologies. Machine learning was applied to identify feature genes for matching shared TFs and miRNAs. External cohort validation and in vivo experiments (methionine choline-deficient diet murine models) were conducted for biological confirmation. : Five glycolysis-associated key genes (, , , , ) were identified and validated as MAFLD discriminators. Single-cell analysis revealed that the hepatocyte-fibroblast-macrophage axis constitutes the predominant glycolysis-active niche. Spatial transcriptomics showed that , , and were colocalized with the monocyte-derived macrophage marker . Using four machine learning models, four feature genes were identified, along with their common transcription factors and , and the miRNA "". External datasets and experimental validation confirmed that the key genes were upregulated in MAFLD samples. : In this study, we identified five glycolysis-related key genes in MAFLD and explored their relationship with immune infiltration, providing new insights for diagnosis and metabolism-directed immunomodulation strategies in MAFLD.
代谢相关脂肪性肝病（MAFLD）的特征是代谢综合征和免疫浸润，糖酵解途径激活是一个关键因素。本研究旨在通过生物信息学分析和实验验证，确定驱动MAFLD进展的糖酵解相关关键基因，并阐明它们与免疫浸润的相互作用。：对来自MAFLD患者和对照的批量RNA测序、单细胞RNA测序和空间转录组数据集进行综合多组学分析。采用差异表达分析和加权基因共表达网络分析（WGCNA）来确定与糖酵解相关的关键基因。使用单细胞和空间转录组学技术分析与免疫浸润的关系。应用机器学习来识别匹配共享转录因子和微小RNA（miRNA）的特征基因。进行外部队列验证和体内实验（蛋氨酸胆碱缺乏饮食小鼠模型）以进行生物学确认。：确定了五个与糖酵解相关的关键基因（、、、、）并验证其为MAFLD鉴别标志物。单细胞分析表明，肝细胞-成纤维细胞-巨噬细胞轴构成了主要的糖酵解活跃微环境。空间转录组学显示，、和与单核细胞衍生的巨噬细胞标志物共定位。使用四种机器学习模型，确定了四个特征基因，以及它们的共同转录因子和，以及miRNA“”。外部数据集和实验验证证实，关键基因在MAFLD样本中上调。：在本研究中，我们在MAFLD中确定了五个与糖酵解相关的关键基因，并探索了它们与免疫浸润的关系，为MAFLD的诊断和代谢导向的免疫调节策略提供了新的见解。
