Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) and its severe clinical form, metabolic dysfunction-associated steatohepatitis (MASH), are chronic liver diseases that are becoming increasingly prevalent. Accurate identification of the stages of MASH is crucial for patient treatment, as it is associated with an increased risk of cirrhosis, liver failure, liver cancer, and mortality as it progresses.
The understanding of disease pathogenesis has advanced significantly in the past decade, largely due to the use of OMICS and high-throughput technologies. Genomics has enabled the identification of genetic variants that confer either risk or protection against steatotic liver diseases and fibrosis [1]. Epigenetics/epigenomics has helped to clarify the relationship between MASLD and comorbidities [2], disease severity [3], and even the interaction with the microbiome, as recently described [4]. Besides, liver transcriptomics have revealed the key mechanisms regulating gene expression in the context of MASLD and MASH [5].
Despite significant progress in comprehending the molecular structure of MASLD and MASH, a substantial gap remains between the insights gained from OMICs research and their practical application in clinical settings.
In this issue of Clinical and Molecular Hepatology, Oh et al. [6] conducted holistic omics analyses on biopsy tissue and blood samples from 134 patients with MASLD, which includes both steatosis and MASH. Whole-genome sequencing, wholeexome sequencing, whole-genome bisulfite sequencing, and total RNA sequencing were performed, revealing 1,955 MASLD-associated features. Using a Support Vector Machine learning algorithm, the researchers identified the most predictive features. Through linear regression, a signature gene set (CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) capable of differentiating MASLD stages was established and validated in independent MASLD cohorts and a liver cancer cohort. These findings suggest the potential of the identified gene set as a diagnostic panel for MASLD-associated diseases.
The panel genes identified belongs to biological processes such as cellular response to cytokine stimuli, response to cytokine, and positive regulation of secretion and immune response, as already anticipated [7]. CAPG, RNASE6, TREM2, and SPP1 are highly co-expressed in myeloid/macrophage cells and interestingly, some genes, such as TREM2, may be important in the bacterial product’s action on liver health–a relevant finding in light of the liver metataxonomic profile found in MASLD [8].
The gene set enriched in MASH may have implications for disease treatment. For instance, a simple gene list enrichment analysis can yield interesting pharmacome annotations (toppgene.cchmc.org). In this case, the gene products may serve as targets for homochlorcyclizine or terfenadine analogs.
The elevated expression of CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6 in MASH was mechanistically explained by the gene location in open chromatin regions.
Furthermore, the analysis of differentially hypermethylated loci revealed promising candidates, including PACS2 (Phosphofurin Acidic Cluster Sorting Protein 2), a coding gene located in the endoplasmic reticulum and mitochondrion that is involved in endoplasmic reticulum calcium ion homeostasis. The authors also identified ZNF331 (Zinc Finger Protein 331) as one of the differentially hypomethylated loci. This gene encodes a zinc finger protein that contains a KRAB (Kruppel-associated box) domain, which is found in transcriptional repressors that may be methylated and silenced in cancer cells.
Epigenetic modifications might explain the enhancement of gene expression in MASH compared to steatosis, although differentially methylated regions do not contain the panel genes. The main finding is robust, as it was reproduced in an in vivo rodent model and simulated in an in vitro organoid model.
It remains to be explained how the differential expression of the 1,393 genes between steatosis and MASH seems not to be related to the somatic mutations the authors found. In this scenario, the contributions of these mutations and gene variants should be further investigated. Perhaps they are associated with many germline gene variants, a topic solely explored for the pathognomonic gene variants associated with the disease, such as PNPLA3, TM6SF2, and so on [9]. Besides, the chosen approach missed noncoding variants. Genetic variation in non-coding regions through regulatory non-coding RNAs, either small or long, may be associated with MASLD [10].
Finally, it is worth adding two notes of caution. First, the panel composed by CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6 seems to equally discriminate between normal liver histology and liver steatosis, the severity of histological characteristics of MASH, and hepatocellular carcinoma, which may indicate a lack of specificity regarding disease progression. Second, the novel biomarker is based on gene expression solely available after a liver biopsy. It would be important to demonstrate that the biomarker is available, at least as a liquid biopsy, to be clinically useful.
PDF Links
PubReader
ePub Link
XML Download
Full text via DOI
Download Citation
Print
