Academy of Diagnostics & Laboratory Medicine - Scientific Short
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Liver fibrosis is a shared process in many chronic liver diseases—including viral hepatitis, alcohol-related liver disease, and metabolic dysfunction–associated fatty liver disease—and is a key stage in the progression to cirrhosis and hepatocellular carcinoma. Worldwide, hundreds of millions of individuals are affected by fibrosis, yet many remain undiagnosed or untreated due to the limited sensitivity of the current diagnostic tools. Liver biopsy remains the gold standard, but it is invasive and has clear limitations. Noninvasive tools such as serologic indices (FIB-4, APRI) and imaging (for example, transient elastography) remain insufficient for identifying early-stage fibrosis. There is an urgent need for more comprehensive and accurate approaches to assess fibrosis.
In recent years, multi-omics has opened a new path. By integrating genomics, transcriptomics, proteomics, and metabolomics, this approach elucidates disease-related molecular networks and their temporal dynamics. Fibrosis arises through immune inflammation, metabolic changes, and remodeling of the extracellular matrix; these processes can be measured across different omics layers. Advances in high-throughput sequencing and mass spectrometry now allow researchers to study these mechanisms at the cellular and molecular levels and to search for useful diagnostic biomarkers.
Multi-omic approaches have yielded important insights into disease mechanisms and biomarker discovery. Single-cell transcriptomics and spatial proteomics have mapped how hepatic stellate cells move from a resting state to activation and matrix deposition, and have revealed stage-specific heterogeneity among immune-cell subsets. In chronic hepatitis B, longitudinal proteomics during antiviral therapy has documented temporal shifts in serum proteins, and specific protein panels can signal fibrosis regression. Metabolomics analyses in the UK Biobank have produced high-accuracy risk models that outperform traditional indices in predicting cirrhosis and related events. In fatty liver disease, proteomics has identified signatures closely associated with fibrosis progression and cirrhosis risk.
In our ADLM 2025 abstract, we combined proteomics and transcriptomics to identify early diagnostic biomarkers of liver fibrosis and identified AEBP1, a secreted matrix protein, which accurately detects significant fibrosis and performs better than existing models. Using spatial transcriptomics, we further found that AEBP1 is mainly expressed in areas with collagen deposition in the liver, suggesting it may be involved in fibrosis progression. In addition, using targeted metabolomics, we observed marked changes in free fatty acid and bile acid profiles as fibrosis advances, indicating their potential utility for the early diagnosis of fibrosis.
Despite this promise, clinical use of multi-omics still faces hurdles. Data integration and standardization remain major challenges; differences in platforms, assays, and analysis pipelines reduce reproducibility. Clinical validation studies often have small sample sizes, which limits broader adoption. Multi-omics datasets also contain sensitive genetic and metabolic information, so strict compliance with international data protection rules is required.
Overall, multi-omics is reshaping the diagnosis of liver fibrosis. It deepens our understanding of disease mechanisms and enables more precise subtyping, earlier detection, and monitoring of treatment response. As technologies mature, costs decline, and multicenter validation expands, multi-omics derived biomarkers are poised to transition from research into routine clinical assays, becoming integral to laboratory medicine.
