Omics-derived hepatocellular carcinoma risk biomarkers for precision care of chronic liver diseases

METS-IR, a Novel Simple Insulin Resistance Index, is Associated with NAFLD in Patients with Type 2 Diabetes Mellitus

Purpose

Substantial evidence has established a strong association between non-alcoholic fatty liver disease (NAFLD), type 2 diabetes mellitus (T2DM) and insulin resistance (IR). Insulin resistance metabolic score (METS-IR) is a new and more effective comprehensive indicator for measuring IR. Our aim was to investigate the relationship between METS-IR and NAFLD in T2DM population.

Patients and methods

This cross-sectional study included 1097 adult patients with T2DM. Anthropometric measurements and biochemical indicators were collected, and the NAFLD was diagnosed by ultrasound. The METS-IR was calculated. Based on the presence of NAFLD, the population was divided into non-NAFLD and NAFLD groups. The relationship between METS-IR and NAFLD was evaluated.

Results

Compared with the non-NAFLD group, the METS-IR was higher in the NAFLD group (P < 0.001). The incidence rate of NAFLD increased across the quartiles of the METS-IR (P < 0.001). Spearman correlation analysis showed that METS-IR was positively correlated with NAFLD (Correlation Coefficient: 0.441, P < 0.001). The binary logistic regression analysis indicated that METS-IR was independently associated with NAFLD (OR: 1.120, 95% CI 1.080-1.161). Furthermore, the area under the receiver operating characteristic curve of the METS-IR was 0.781 (95% CI 0.746-0.817) and relatively higher than other evaluation variables.

Conclusion

In patients with T2DM, METS-IR is closely associated with NAFLD, and might be a valuable predictor of NAFLD. Further research is needed to verify this association.

Sex differences in diabetes‑induced hepatic and renal damage

Diabetes mellitus (DM) is a disease that affects millions of individuals worldwide and is characterized by abnormal glucose metabolism that can induce severe damage to numerous organs throughout the body. Sex differences have been demonstrated in a number of factors associated with diabetes and its complications, such as diabetic kidney disease and diabetic liver disease. To investigate the sex differences in DM further, the changes in the weight, food and water intake, and blood sugar of mice were recorded for 8 weeks in the present study. Hematoxylin and eosin staining, Masson's trichrome staining and transmission electron microscopy were used to observe the pathological changes of liver and kidney tissues. There is no significant difference in the water intake and blood glucose concentration between db/db female and male mice was observed. However, sex differences in liver and kidney damage including glomerular injury and hepatic fibrosis were found. In conclusion, the present study characterized the features of liver and kidney damage in db/db mice and indicated that sex differences should be taken into account in experiments using female and male experimental animals. Furthermore, sex differences should be taken into account in the selection of drug interventions in experiments and in clinical drug therapy.

m1A Regulatory gene signatures are associated with certain immune cell compositions of the tumor microenvironment and predict survival in kidney renal clear cell carcinoma

Adenosine N1 methylation (m1A) of RNA, a type of post-transcriptional modification, has been shown to play a significant role in the progression of cancer. The objective of the current research was to analyze the genetic alteration and prognostic significance of m1A regulators in kidney renal clear cell carcinoma (KIRC). Genomic and clinicopathological characteristics were obtained from 558 KIRC patients in the Cancer Genome Atlas (TCGA) and Gene Omnibus Expression (GEO) databases. Alterations in the gene expression of ten m1A-regulators were analyzed and survival analysis was performed using the Cox regression method. We also identified three clusters of patients based on their distinct m1A alteration patterns, using integrated analysis of the ten m1A-related regulators, which were significantly related to overall survival (OS), disease-free survival (DFS) and tumor microenvironment (TME) immune cell infiltration cells in KIRC. Our findings showed that m1A alteration patterns have critical roles in determining TME complexity and its immune cell composition. Furthermore, different m1A expression patterns were significantly associated with DFS and OS rates in KIRC patients. In conclusion, the identified m1A RNA modification patterns offer a potentially effective way to classify KIRC patients based on their TME immune cell infiltration, enabling the development of more personalized and successful treatment strategies for these patients.

Diet and Exercise Exert a Differential Effect on Glucose Metabolism Markers According to the Degree of NAFLD Severity

BACKGROUND

Non-Alcoholic Fatty Liver Disease (NAFLD) and Type 2 Diabetes (T2D) are highly prevalent diseases worldwide. Insulin Resistance (IR) is the common denominator of the two conditions even if the precise timing of onset is unknown. Lifestyle change remains the most effective treatment to manage NAFLD. This study aimed to estimate the effect of the Low Glycemic Index Mediterranean Diet (LGIMD) and exercise (aerobic and resistance) over a one-year period on the longitudinal trajectories of glucose metabolism regulatory pathways.

MATERIALS AND METHODS

In this observational study, 58 subjects (aged 18-65) with different degrees of NAFLD severity were enrolled by the National Institute of Gastroenterology-IRCCS "S. de Bellis", to follow a 12-month program of combined exercise and diet.

RESULTS

The mean age was 55 ± 7 years old. Gender was equally distributed among NAFLD categories. There was a statistically significant main effect of time for glycosylated hemoglobin (Hb1Ac) over the whole period (-5.41, 95% CI: -7.51; -3.32). There was a steady, statistically significant decrease of HbA1c in participants with moderate and severe NAFLD whereas this effect was observed after the 9th month in those with mild NAFLD.

CONCLUSIONS

The proposed program significantly improves glucose metabolism parameters, especially HbA1c.

Antitumor Effects and Mechanisms of Metabolic Syndrome Medications on Hepatocellular Carcinoma

Liver cancer has a high incidence and mortality rate worldwide, with hepatocellular carcinoma (HCC) being the most common histological type. With the decrease in the number of newly infected patients and the spread of antiviral therapy, hepatitis virus-negative chronic liver diseases including steatohepatitis are increasingly accounting for a large proportion of HCC, and an important clinical characteristic is the high prevalence of metabolic syndrome including hypertension, type 2 diabetes (T2D), dyslipidemia, and obesity. Since patients with steatohepatitis are less likely to undergo surveillance for early detection of HCC, they may be diagnosed at an advanced stage and have worse prognosis. Therefore, treatment strategies for patients with HCC caused by steatohepatitis, especially in advanced stages, become increasingly important. Further, hypertension, T2D, and dyslipidemia may occur as side effects during systemic treatment, and there will be increasing opportunities to prescribe metabolic syndrome medications, not only for originally comorbid diseases, but also for adverse events during HCC treatment. Interestingly, epidemiological studies have shown that patients taking some metabolic syndrome medications are less likely to develop various types of cancers, including HCC. Basic studies have also shown that these drugs have direct antitumor effects on HCC. In particular, angiotensin II receptor blockers (a drug group for treating hypertension), biguanides (a drug group for treating T2D), and statins (a drug group for treating dyslipidemia) have shown to elucidate antitumor effects against HCC. In this review, we focus on the antitumor effects of metabolic syndrome medications on HCC and their mechanisms based on recent literature. New therapeutic agents are also increasingly being reported. Analysis of the antitumor effects of metabolic syndrome medications on HCC and their mechanisms will be doubly beneficial for HCC patients with metabolic syndrome, and the use of these medications may be a potential strategy against HCC.

Comparing DESI-MSI and MALDI-MSI Mediated Spatial Metabolomics and Their Applications in Cancer Studies

Metabolic heterogeneity of cancer contributes significantly to its poor treatment outcomes and prognosis. As a result, studies continue to focus on identifying new biomarkers and metabolic vulnerabilities, both of which depend on the understanding of altered metabolism in cancer. In the recent decades, the rise of mass spectrometry imaging (MSI) enables the in situ detection of large numbers of small molecules in tissues. Therefore, researchers look to using MSI-mediated spatial metabolomics to further study the altered metabolites in cancer patients. In this review, we examined the two most commonly used spatial metabolomics techniques, MALDI-MSI and DESI-MSI, and some recent highlights of their applications in cancer studies. We also described AFADESI-MSI as a recent variation from the DESI-MSI and compare it with the two major techniques. Specifically, we discussed spatial metabolomics results in four types of heterogeneous malignancies, including breast cancer, esophageal cancer, glioblastoma and lung cancer. Multiple studies have effectively classified cancer tissue subtypes using altered metabolites information. In addition, distribution trends of key metabolites such as fatty acids, high-energy phosphate compounds, and antioxidants were identified. Therefore, while the visualization of finer distribution details requires further improvement of MSI techniques, past studies have suggested spatial metabolomics to be a promising direction to study the complexity of cancer pathophysiology.

The application of artificial intelligence in hepatology: A systematic review

The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21^st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.

Blood-Based Biomarkers in Hepatitis B Virus-Related Hepatocellular Carcinoma, Including the Viral Genome and Glycosylated Proteins

Hepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC) development and is a global public health issue. High performance biomarkers can aid the early detection of HCC development in HBV-infected individuals. In addition, advances in the understanding of the pathogenesis of HBV infection and in clinical laboratory techniques have enabled the establishment of disease-specific tests, prediction of the progression of liver diseases, including HCC, and auxiliary diagnosis of HCC, using blood-based methods instead of biopsies of liver or HCC tissues. Viral factors such as the HBV genotype, HBV genetic mutations, HBV DNA, and HBV-related antigens, as well as host factors, such as tumor-associated proteins and post-translational modifications, especially glycosylated proteins, can be blood-based, disease-specific biomarkers for HCC development in HBV-infected patients. In this review, we describe the clinical applications of viral biomarkers, including the HBV genome and glycosylated proteins, for patients at a risk of HBV-related HCC, based on their molecular mechanisms. In addition, we introduce promising biomarker candidates for practical use, including colony stimulating factor 1 receptor (CSF1R), extracellular vesicles, and cell-free, circulating tumor DNA. The clinical use of such surrogate markers may lead to a better understanding of the risk of disease progression and early detection of HCC in HBV-infected patients, thereby improving their prognosis.

Genomic Landscape of Liquid Biopsy for Hepatocellular Carcinoma Personalized Medicine

Hepatocellular carcinoma (HCC) is the sixth most frequently diagnosed cancer and the third leading cause of cancer-related deaths worldwide. Advanced-stage HCC patients have poor survival rates and this requires the discovery of novel clear biomarkers for HCC early diagnosis and prognosis, identifying risk factors, distinguishing HCC from non-HCC liver diseases, and assessment of treatment response. Liquid biopsy has emerged as a novel minimally invasive approach to enable monitoring tumor progression, metastasis, and recurrence. Since the liquid biopsy analysis has relatively high specificity and low sensitivity in cancer early detection, there is a risk of bias. Next-generation sequencing (NGS) technologies provide accurate and comprehensive gene expression and mutational profiling of liquid biopsies including cell-free circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and genomic components of extracellular vesicles (EVs) including micro-RNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). Since HCC is a highly heterogeneous cancer, HCC patients can display various genomic, epigenomic, and transcriptomic patterns and exhibit varying sensitivity to treatment options. Identification of individual variabilities in genomic signatures in liquid biopsy has the potential to greatly enhance precision oncology capabilities. In this review, we highlight and critically discuss the latest progress in characterizing the genomic landscape of liquid biopsy, which can advance HCC personalized medicine.

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.

Clinical and Molecular Prediction of Hepatocellular Carcinoma Risk

Prediction of hepatocellular carcinoma (HCC) risk becomes increasingly important with recently emerging HCC-predisposing conditions, namely non-alcoholic fatty liver disease and cured hepatitis C virus infection. These etiologies are accompanied with a relatively low HCC incidence rate (~1% per year or less), while affecting a large patient population. Hepatitis B virus infection remains a major HCC risk factor, but a majority of the patients are now on antiviral therapy, which substantially lowers, but does not eliminate, HCC risk. Thus, it is critically important to identify a small subset of patients who have elevated likelihood of developing HCC, to optimize the allocation of limited HCC screening resources to those who need it most and enable cost-effective early HCC diagnosis to prolong patient survival. To date, numerous clinical-variable-based HCC risk scores have been developed for specific clinical contexts defined by liver disease etiology, severity, and other factors. In parallel, various molecular features have been reported as potential HCC risk biomarkers, utilizing both tissue and body-fluid specimens. Deep-learning-based risk modeling is an emerging strategy. Although none of them has been widely incorporated in clinical care of liver disease patients yet, some have been undergoing the process of validation and clinical development. In this review, these risk scores and biomarker candidates are overviewed, and strategic issues in their validation and clinical translation are discussed.

Viral Exposure Signature Associated with Liver Cancer Risk

Virus infection is known to increase the risk of various cancers, including hepatocellular carcinoma (HCC). In a recent article in Cell, Liu et al. report a blood-based viral exposure signature (VES) based on a synthetic human virome to capture the history of virus exposure that is associated with future HCC occurrence.

The Intricate Relationship between Type 2 Diabetes Mellitus (T2DM), Insulin Resistance (IR), and Nonalcoholic Fatty Liver Disease (NAFLD)

Nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM) remain as one of the most global problematic metabolic diseases with rapidly increasing prevalence and incidence. Epidemiological studies noted that T2DM patients have by two-fold increase to develop NAFLD, and vice versa. This complex and intricate association is supported and mediated by insulin resistance (IR). In this review, we discuss the NAFLD immunopathogenesis, connection with IR and T2DM, the role of screening and noninvasive tools, and mostly the impact of the current antidiabetic drugs on steatosis liver and new potential therapeutic targets.