Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases

J Hepatol. 2024 Aug;81(2):345-359. doi: 10.1016/j.jhep.2024.03.035. Epub 2024 Mar 28.

Abstract

The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.

Keywords: Non-invasive test; genetics; lipidomics; metabolomics; metagenomics; metatranscriptomics; microbiome; proteomics; viromics.

Publication types

  • Review

MeSH terms

  • Biomarkers* / analysis
  • Biomarkers* / metabolism
  • Fatty Liver / diagnosis
  • Fatty Liver / genetics
  • Genomics / methods
  • Humans
  • Metabolomics / methods
  • Proteomics / methods

Substances

  • Biomarkers