Systems biology for hepatologists

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

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.

Potential of lncRNAs to regulate cuproptosis in hepatocellular carcinoma: Establishment and validation of a novel risk model

Cuproptosis, a distinct form of programmed cell death, is an emerging field in oncology with promising implications. This novel mode of cell death has the potential to become a regulatory target for tumor therapy, thus expanding the currently limited treatment options available for patients with cancer. Our research team focused on investigating the role of functional long non-coding RNA (lncRNAs) in hepatocellular carcinoma (HCC). We were particularly intrigued by the potential implications of HCC-lncRNAs on cuproptosis. Through a comprehensive analysis, we identified three cuproptosis-related lncRNAs (CRLs): AC018690.1, AL050341.2, and LINC02038. These lncRNAs were found to influence the sensitivity of HCC to cuproptosis. Based on our results, we constructed a risk model represented by the equation: risk score = 0.82 * AC018690.1 + 0.65 * AL050341.2 + 0.61 * LINC02038. Notably, significant disparities were observed in clinical features, such as the response rate to immunotherapy and targeted therapy, as well as in cellular characteristics, including the composition of the tumor immune microenvironment (TIME), when comparing the high- and low-risk groups. Most importantly, knockdown of these CRLs was confirmed to significantly weaken the resistance to cuproptosis in HCC. This effect resulted from the accelerated accumulation of lipoacylated-DLAT and lipoacylated-DLST. In summary, we identified three CRLs in HCC and established a novel risk model with potential clinical applications. Additionally, we proposed a potential therapeutic method consisting of sorafenib-copper ionophores-immunotherapy.

Cardiotoxicity: Importance of biomarkers

The clinical efficacy of chemotherapy, as a recognized therapeutic approach for malignant diseases, usually has certain limitations due to its cardiotoxicity (CT) and consequent cardiomyopathy, or even heart failure. CT is defined as any cardiac injury connected with oncology treatment, whether it is chemo-, radio-, targeted or immunotherapy, or cancer by itself, and it represents a great challenge for clinicians in everyday practice. A wide spectrum of factors related to chemotherapy (type of drug, dose during each cycle, cumulative dose, schedule, method of application, combination with other cardiotoxic drugs or association with radiotherapy) and patient characteristics (age, presence of cardiovascular risk factors, previous cardiovascular disease) are the determining factors that influence the frequency of CT. Imaging methods for morphological and functional monitoring of the heart muscle are used for monitoring CT. The quest for diagnostic tools for early CT detection is of great significance. In line with this, the measurement of some cardiac biomarkers has found its place in clinical settings as an early determinant of myocardial injury. Therefore, in this review article, special attention will be paid to certain well-established, as well as certain novel cardiac biomarkers, and their role in recognizing asymptomatic CT, in order to gain deeper insight into their diagnostic utility.

Radiomics: a primer on high-throughput image phenotyping

Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.

The Role of Diagnostic Biomarkers, Omics Strategies, and Single-Cell Sequencing for Nonalcoholic Fatty Liver Disease in Severely Obese Patients

Liver disease due to metabolic dysfunction constitute a worldwide growing health issue. Severe obesity is a particularly strong risk factor for non-alcoholic fatty liver disease, which affects up to 93% of these patients. Current diagnostic markers focus on the detection of advanced fibrosis as the major predictor of liver-related morbidity and mortality. The most accurate diagnostic tools use elastography to measure liver stiffness, with diagnostic accuracies similar in normal-weight and severely obese patients. The effectiveness of elastography tools are however hampered by limitations to equipment and measurement quality in patients with very large abdominal circumference and subcutaneous fat. Blood-based biomarkers are therefore attractive, but those available to date have only moderate diagnostic accuracy. Ongoing technological advances in omics technologies such as genomics, transcriptomics, and proteomics hold great promise for discovery of biomarkers and increased pathophysiological understanding of non-alcoholic liver disease and steatohepatitis. Very recent developments have allowed for single-cell sequencing and cell-type resolution of gene expression and function. In the near future, we will therefore likely see a multitude of breakthrough biomarkers, developed from a deepened understanding of the biological function of individual cell types in the healthy and injured liver.

Exploration the Mechanism of Doxorubicin-Induced Heart Failure in Rats by Integration of Proteomics and Metabolomics Data

Heart failure is a common systemic disease with high morbidity and mortality worldwide. Doxorubicin (DOX) is a commonly used anthracycline broad-spectrum antitumor antibiotic with strong antitumor effect and definite curative effect. However, cardiotoxicity is the adverse reaction of drug dose cumulative toxicity, but the mechanism is still unclear. In this study, proteomics and metabonomics techniques were used to analyze the tissue and plasma of DOX-induced heart failure (HF) in rats and to clarify the molecular mechanism of the harmful effects of DOX on cardiac metabolism and function in rats from a new point of view. The results showed that a total of 278 proteins with significant changes were identified by quantitative proteomic analysis, of which 118 proteins were significantly upregulated and 160 proteins were significantly downregulated in myocardial tissue. In the metabonomic analysis, 21 biomarkers such as L-octanoylcarnitine, alpha-ketoglutarate, glutamine, creatine, and sphingosine were detected. Correlation analysis showed that DOX-induced HF mainly affected phenylalanine, tyrosine, and tryptophan biosynthesis, D-glutamine and D-glutamate metabolism, phenylalanine metabolism, biosynthesis of unsaturated fatty acids, and other metabolic pathways, suggesting abnormal amino acid metabolism, fatty acid metabolism, and glycerol phospholipid metabolism. It is worth noting that we have found the key upstream target of DOX-induced HF, PTP1B, which inhibits the expression of HIF-1α by inhibiting the phosphorylation of IRS, leading to disorders of fatty acid metabolism and glycolysis, which together with the decrease of Nrf2, SOD, Cytc, and AK4 proteins lead to oxidative stress. Therefore, we think that PTP1B may play an important role in the development of heart failure induced by doxorubicin and can be used as a potential target for the treatment of heart failure.

MicroRNAs in the Evaluation and Potential Treatment of Liver Diseases

Acute and chronic liver disease continue to result in significant morbidity and mortality of patients, along with increasing burden on their families, society and the health care system. This in part is due to increased incidence of liver disease associated factors such as metabolic syndrome; improved survival of patients with chronic predisposing conditions such as HIV; as well as advances in the field of transplantation and associated care leading to improved survival. The fact that one disease can result in different manifestations and outcomes highlights the need for improved understanding of not just genetic phenomenon predisposing to a condition, but additionally the role of epigenetic and environmental factors leading to the phenotype of the disease. It is not surprising that providers continue to face daily challenges pertaining to diagnostic accuracy, prognostication of disease severity, progression, and response to therapies. A number of these challenges can be addressed by incorporating a personalized approach of management to the current paradigm of care. Recent advances in the fields of molecular biology and genetics have paved the way to more accurate, individualized and precise approach to caring for liver disease. The study of microRNAs and their role in both healthy and diseased livers is one example of such advances. As these small, non-coding RNAs work on fine-tuning of cellular activities and organ function in a dynamic and precise fashion, they provide us a golden opportunity to advance the field of hepatology. The study of microRNAs in liver disease promises tremendous improvement in hepatology and is likely to lay the foundation towards a personalized approach in liver disease.

The genetic architecture of NAFLD among inbred strains of mice

To identify genetic and environmental factors contributing to the pathogenesis of non-alcoholic fatty liver disease, we examined liver steatosis and related clinical and molecular traits in more than 100 unique inbred mouse strains, which were fed a diet rich in fat and carbohydrates. A >30-fold variation in hepatic TG accumulation was observed among the strains. Genome-wide association studies revealed three loci associated with hepatic TG accumulation. Utilizing transcriptomic data from the liver and adipose tissue, we identified several high-confidence candidate genes for hepatic steatosis, including Gde1, a glycerophosphodiester phosphodiesterase not previously implicated in triglyceride metabolism. We confirmed the role of Gde1 by in vivo hepatic over-expression and shRNA knockdown studies. We hypothesize that Gde1 expression increases TG production by contributing to the production of glycerol-3-phosphate. Our multi-level data, including transcript levels, metabolite levels, and gut microbiota composition, provide a framework for understanding genetic and environmental interactions underlying hepatic steatosis.