Molecular Landscape and Diagnostic Model of MASH: Transcriptomic, Proteomic, Metabolomic, and Lipidomic Perspectives

Abstract

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.