From genome sequencing to the discovery of potential biomarkers in liver disease

Identification of signature gene set as highly accurate determination of metabolic dysfunction-associated steatotic liver disease progression

BACKGROUND/AIMS

Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression.

METHODS

Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD.

RESULTS

After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort.

CONCLUSION

We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.

Methylation-sensitive high-resolution melting analysis of the USP44 promoter can detect early-stage hepatocellular carcinoma in blood samples

Hepatocellular carcinoma (HCC) is dangerous cancer that often evades early detection because it is asymptomatic and an effective detection method is lacking. For people with chronic liver inflammation who are at high risk of developing HCC, a sensitive detection method for HCC is needed. In a meta-analysis of The Cancer Genome Atlas pan-cancer methylation database, we identified a CpG island in the USP44 promoter that is methylated specifically in HCC. We developed methylation-sensitive high-resolution melting (MS-HRM) analysis to measure the methylation levels of the USP promoter in cell-free DNA isolated from patients. Our MS-HRM assay correctly identified 40% of patients with early-stage HCC, whereas the α-fetoprotein test, which is currently used to detect HCC, correctly identified only 25% of early-stage HCC patients. These results demonstrate that USP44 MS-HRM analysis is suitable for HCC surveillance. [BMB Reports 2022; 55(11): 553-558].

Methylation-sensitive high-resolution melting analysis of the USP44 promoter can detect early-stage hepatocellular carcinoma in blood samples

Hepatocellular carcinoma (HCC) is dangerous cancer that often evades early detection because it is asymptomatic and an effective detection method is lacking. For people with chronic liver inflammation who are at high risk of developing HCC, a sensitive detection method for HCC is needed. In a meta-analysis of The Cancer Genome Atlas pan-cancer methylation database, we identified a CpG island in the USP44 promoter that is methylated specifically in HCC. We developed methylation-sensitive high-resolution melting (MS-HRM) analysis to measure the methylation levels of the USP promoter in cell-free DNA isolated from patients. Our MS-HRM assay correctly identified 40% of patients with early-stage HCC, whereas the α-fetoprotein test, which is currently used to detect HCC, correctly identified only 25% of early-stage HCC patients. These results demonstrate that USP44 MS-HRM analysis is suitable for HCC surveillance. [BMB Reports 2022; 55(11): 553-558].

Comprehensive bioinformatics and machine learning analysis identify VCAN as a novel biomarker of hepatitis B virus-related liver fibrosis

Hepatitis B virus (HBV) infection remains the leading cause of liver fibrosis (LF) worldwide, especially in China. Identification of decisive diagnostic biomarkers for HBV-associated liver fibrosis (HBV-LF) is required to prevent chronic hepatitis B (CHB) from progressing to liver cancer and to more effectively select the best treatment strategy. We obtained 43 samples from CHB patients without LF and 81 samples from CHB patients with LF (GSE84044 dataset). Among these, 173 differentially expressed genes (DEGs) were identified. Functional analysis revealed that these DEGs predominantly participated in immune-, extracellular matrix-, and metabolism-related processes. Subsequently, we integrated four algorithms (LASSO regression, SVM-RFE, RF, and WGCNA) to determine diagnostic biomarkers for HBV-LF. These analyses and receive operating characteristic curves identified the genes for phosphatidic acid phosphatase type 2C (PPAP2C) and versican (VCAN) as potentially valuable diagnostic biomarkers for HBV-LF. Single-sample gene set enrichment analysis (ssGSEA) further confirmed the immune landscape of HBV-LF. The two diagnostic biomarkers also significantly correlated with infiltrating immune cells. The potential regulatory mechanisms of VCAN underlying the occurrence and development of HBV-LF were also analyzed. These collective findings implicate VCAN as a novel diagnostic biomarker for HBV-LF, and infiltration of immune cells may critically contribute to the occurrence and development of HBV-LF.

The Chromatin Accessibility Landscape of Nonalcoholic Fatty Liver Disease Progression

The advent of the assay for transposase-accessible chromatin using sequencing (ATAC-seq) has shown great potential as a leading method for analyzing the genome-wide profiling of chromatin accessibility. A comprehensive reference to the ATAC-seq dataset for disease progression is important for understanding the regulatory specificity caused by genetic or epigenetic changes. In this study, we present a genome-wide chromatin accessibility profile of 44 liver samples spanning the full histological spectrum of nonalcoholic fatty liver disease (NAFLD). We analyzed the ATAC-seq signal enrichment, fragment size distribution, and correlation coefficients according to the histological severity of NAFLD (healthy control vs steatosis vs fibrotic nonalcoholic steatohepatitis), demonstrating the high quality of the dataset. Consequently, 112,303 merged regions (genomic regions containing one or multiple overlapping peak regions) were identified. Additionally, we found differentially accessible regions (DARs) and performed transcription factor binding motif enrichment analysis and de novo motif analysis to determine new biomarker candidates. These data revealed the generegulatory interactions and noncoding factors that can affect NAFLD progression. In summary, our study provides a valuable resource for the human epigenome by applying an advanced approach to facilitate diagnosis and treatment by understanding the non-coding genome of NAFLD.

Polydatin: A Critical Promising Natural Agent for Liver Protection via Antioxidative Stress

Polydatin, one of the natural active small molecules, was commonly applied in protecting and treating liver disorders in preclinical studies. Oxidative stress plays vital roles in liver injury caused by various factors, such as alcohol, viral infections, dietary components, drugs, and other chemical reagents. It is reported that oxidative stress might be one of the main reasons in the progressive development of alcohol liver diseases (ALDs), nonalcoholic liver diseases (NAFLDs), liver injury, fibrosis, hepatic failure (HF), and hepatocellular carcinoma (HCC). In this paper, we comprehensively summarized the pharmacological effects and potential molecular mechanisms of polydatin for protecting and treating liver disorders via regulation of oxidative stress. According to the previous studies, polydatin is a versatile natural compound and exerts significantly protective and curative effects on oxidative stress-associated liver diseases via various molecular mechanisms, including amelioration of liver function and insulin resistance, inhibition of proinflammatory cytokines, lipid accumulation, endoplasmic reticulum stress and autophagy, regulation of PI3K/Akt/mTOR, and activation of hepatic stellate cells (HSCs), as well as increase of antioxidant enzymes (such as catalase (CAT), glutathione peroxidase (GPx), glutathione (GSH), superoxide dismutase (SOD), glutathione reductase (GR), and heme oxygenase-1 (HO-1)). In addition, polydatin acts as a free radical scavenger against reactive oxygen species (ROS) by its phenolic and ethylenic bond structure. However, further clinical investigations are still needed to explore the comprehensive molecular mechanisms and confirm the clinical treatment effect of polydatin in liver diseases related to regulation of oxidative stress.

Identification of Molecular Subgroups in Liver Cirrhosis by Gene Expression Profiles

Background: Liver cirrhosis is characterized by high mortality, bringing a serious health and economic burden to the world. The clinical manifestations of liver cirrhosis are complex and heterogeneous. According to subgroup characteristics, identifying cirrhosis has become a challenge. Objectives: The purpose of this study was to evaluate the difference between different subgroups of cirrhosis. The ultimate goal of research on these different phenotypes was to discover groups of patients with unique treatment characteristics, and formulate targeted treatment plans that improve the prognosis of the disease and improve the patients’ quality of life. Methods: We obtained the relevant gene chip by searching the gene expression omnibus (GEO) database. According to the gene expression profile, 79 patients with liver cirrhosis were divided into four subgroups, which showed different expression patterns. Therefore, we used weighted gene coexpression network analysis (WGCNA) to find differences between subgroups. Results: The characteristics of the WGCNA module indicated that subjects in subgroup I might exhibit inflammatory characteristics; subjects in subgroup II might exhibit metabolically active characteristics; arrhythmogenic right ventricular cardiomyopathy and neuroactive ligand-receptive somatic interaction pathways were significantly enriched in subgroup IV. We did not find a significantly upregulated pathway in the third subgroup. Conclusions: In this study, a new type of clinical phenotype classification of liver cirrhosis was derived by consensus clustering. This study found that patients in different subgroups may have unique gene expression patterns. This new classification method helps researchers explore new treatment strategies for cirrhosis based on clinical phenotypic characteristics.

Epigenetic Biomarkers for the Diagnosis and Treatment of Liver Disease

Research in the last decades has demonstrated the relevance of epigenetics in controlling gene expression to maintain cell homeostasis, and the important role played by epigenome alterations in disease development. Moreover, the reversibility of epigenetic marks can be harnessed as a therapeutic strategy, and epigenetic marks can be used as diagnosis biomarkers. Epigenetic alterations in DNA methylation, histone post-translational modifications (PTMs), and non-coding RNA (ncRNA) expression have been associated with the process of hepatocarcinogenesis. Here, we summarize epigenetic alterations involved in the pathogenesis of chronic liver disease (CLD), particularly focusing on DNA methylation. We also discuss their utility as epigenetic biomarkers in liquid biopsy for the diagnosis and prognosis of hepatocellular carcinoma (HCC). Finally, we discuss the potential of epigenetic therapeutic strategies for HCC treatment.