Chen Yilong, Bian Shuixiu, Le Jiamei
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China.
Genes (Basel). 2025 Mar 29;16(4):399. doi: 10.3390/genes16040399.
Metabolic dysfunction-associated steatohepatitis (MASH), a progressive form of fatty liver disease, presents a significant global health challenge. Despite extensive research, fully elucidating its complex pathogenesis and developing accurate non-invasive diagnostic tools remain key goals. Multi-omics approaches, integrating data from transcriptomics, proteomics, metabolomics, and lipidomics, offer a powerful strategy to achieve these aims. This review summarizes key findings from multi-omics studies in MASH, highlighting their contributions to our understanding of disease mechanisms and the development of improved diagnostic models. Transcriptomic studies have revealed widespread gene dysregulation affecting lipid metabolism, inflammation, and fibrosis, while proteomics has identified altered protein expression patterns and potential biomarkers. Metabolomic and lipidomic analyses have further uncovered significant changes in various metabolites and lipid species, including ceramides, sphingomyelins, phospholipids, and bile acids, underscoring the central role of lipid dysregulation in MASH. These multi-omics findings have been leveraged to develop novel diagnostic models, some incorporating machine learning algorithms, with improved accuracy compared to traditional methods. Further research is needed to validate these findings, explore the complex interplay between different omics layers, and translate these discoveries into clinically useful tools for improved MASH diagnosis and prognosis.
代谢功能障碍相关脂肪性肝炎（MASH）是一种进展性的肝脏脂肪疾病，对全球健康构成了重大挑战。尽管进行了广泛的研究，但全面阐明其复杂的发病机制并开发准确的非侵入性诊断工具仍然是关键目标。整合转录组学、蛋白质组学、代谢组学和脂质组学数据的多组学方法，为实现这些目标提供了一个强有力的策略。本综述总结了MASH多组学研究的关键发现，强调了它们对我们理解疾病机制以及改进诊断模型开发的贡献。转录组学研究揭示了影响脂质代谢、炎症和纤维化的广泛基因失调，而蛋白质组学则确定了改变的蛋白质表达模式和潜在的生物标志物。代谢组学和脂质组学分析进一步发现了各种代谢物和脂质种类的显著变化，包括神经酰胺、鞘磷脂、磷脂和胆汁酸，强调了脂质失调在MASH中的核心作用。这些多组学发现已被用于开发新的诊断模型，其中一些结合了机器学习算法，与传统方法相比具有更高的准确性。需要进一步的研究来验证这些发现，探索不同组学层面之间的复杂相互作用，并将这些发现转化为临床上有用的工具，以改善MASH的诊断和预后。
