Chronic liver disease (CLD) is a spectrum of hepatic morbid manifestations which last for more than six months causing progressive deteriorated liver function. CLD includes hepatitis, liver cirrhosis, fibrosis, and malignancies of different etiology (e.g., due to viral, alcoholic, or metabolic reasons). As a major cause of morbidity and mortality, CLDs have become an increasingly severe global burden. Understanding the mechanism of the occurrence and development of CLDs will help to develop better prevention strategies and therapies in accordance with the viewpoint of precision medicine.

As a major organ of ATP production, liver cells are rich in mitochondria. Mitochondrial dysfunction has been found to be involved in many liver diseases such as non-alcoholic fatty liver disease, viral hepatitis, and hepatocellular carcinoma. Interventions targeting mitochondrial dysfunction might have benefits for patients with CLD. However, the mechanism of mitochondrial dysfunction in CLD is still unclear and worthy of further investigation. The omics strategy is one approach to unveil the potential mechanism of mitochondrial dysfunction in CLD in the profiles of the genome, transcriptome, proteome, and metabolome. With the development of technology, omics testing is now widely affordable and has therefore become a preferred option for the investigation of potential targets and pathways involved in mitochondria function related to CLD. Improved algorithms also improve the integration of multi-omic data. Such integrated omics analysis provides more reliable data to inspire further validation studies in vitro and in vivo. Multi-omics data also aids in better prediction of therapeutic effects of drugs or other interventions in CLD with aims to improve mitochondrial function.

This Special Issue aims to collect studies with a focus on the investigation of the molecular mechanism and the construction of diagnostic/prognostic models concerning the impact of mitochondrial dysfunction in chronic liver diseases based on multi-omics approaches, with a combination of omics data from DNA, RNA, proteins, or end-productive metabolites. Data can be generated from humans, animals, or cell lines. Data from public databases with subsequent validation are also welcomed. We welcome studies that investigate the mechanisms underlying the link between mitochondrial dysfunction and chronic liver diseases including chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma (HCC) via combinative approaches of transcriptomics, proteomics, metabolomics, radiomics, etc. Omics studies on key issues of mitochondria in liver transplantation (such as marginal grafts and HCC recurrence) are also welcome.

Potential topics include but are not limited to the following:

• Construction of biological models by multi-omics approaches to evaluate the impact of oxidative stress on CLDs
• System biology approaches to investigate key genes (such as PKLR) to elucidate the molecular mechanism of chronic liver disease
• Novel findings of key factors (on genetic, metabolic aspects, etc.) that are involved in mitochondrial dysfunction in the progression of chronic liver disease based on re-analysis of opening data from public omic website (such as TCGA, GEO, and GTEx databases)
• Description of the development of chronic liver disease based on sequential multi-omic analysis of molecular profiles in mitochondrial function
• Potential mechanisms and the role of metabolic drugs in mitochondrial dysfunction in patients with chronic liver disease
• The combination of metabolic factors and radiomics involved in mitochondrial dysfunction to identify the factors that affect the occurrence and progression of HCC
• Liver injury in chronic liver diseases related to mitochondrial dysfunction mediated by genetic-environmental interactions
• Mitochondrial reactive oxygen species signaling, antioxidant defense system, and intervention in multi-omics
• Multi-omics analysis on the impact of unhealthy lifestyles on CLD
• Multi-omics-based R&D on molecules that target specific pathways for CLD treatment
• Transcriptome, proteome, and genome-wide association studies to identify new mechanisms, targets, and high-risk genetic variants in CLD