Uncovering hepatic transcriptomic and circulating proteomic signatures in MASH: A meta-analysis and machine
Elena Cristina Rusu1, Helena Clavero-Mestres2, Mario Sánchez-Álvarez2
1GEMMAIR research Unit (AGAUR) - Applied Medicine (URV). Department of Medicine and Surgery. University Rovira I Virgili (URV), Health Research Institute Pere Virgili (IISPV), 43007, Tarragona, Spain; Institute for Integrative Systems Biology (I2SysBio), University of Valencia and the Spanish National Research Council (CSIC), 46980, Valencia, Spain.
Background:
Metabolic-associated steatohepatitis (MASH), the progressive form of metabolic-associated steatotic liver disease (MASLD), poses significant risks for liver fibrosis and cardiovascular complications. Despite extensive research, reliable biomarkers for MASH diagnosis and progression remain elusive. This study aimed to identify hepatic transcriptomic and circulating proteomic signatures specific to MASH, and to develop a machine learning-based biomarker discovery model.
Methods:
A systematic review of RNA-Seq and proteomic datasets was conducted, retrieving 7 hepatic transcriptomics and 3 circulating proteomics studies, encompassing 483 liver samples and 169 serum/plasma samples, respectively. Differential gene and protein expression analyses were performed, and pathways were enriched using gene set enrichment analysis. A machine learning (ML) model was developed to identify MASH-specific biomarkers, utilizing biologically significant protein ratios.
Key Findings:
Hepatic transcriptomic analysis identified 5017 differentially expressed genes (DEGs), with significant enrichment of extracellular matrix (ECM) pathways. Serum proteomics revealed six differentially expressed proteins (DEPs), including complement-related proteins. Integration of transcriptomic and proteomic data highlighted the complement cascade as a key pathway in MASH, with discordant regulation between the liver and circulation. The ML-based biomarker discovery model, utilizing protein ratios, achieved an F1 scores of 0.83 and 0.64 in the training sets and 0.67 in an external validation set.
Conclusion:
Our findings indicate ECM deregulation and complement system involvement in MASH progression. The novel ML model incorporating protein ratios offers a potential tool for MASH diagnosis. However, further refinement and validation across larger and more diverse cohorts is needed to generalize these results.
