Serum Proteomic Changes as Candidate Biomarkers of Intermediate Liver Fibrosis in Chronic Hepatitis B Infection

Fibrinolysis-Mediated Pathways in Acute Liver Injury

Acute liver injury (ALI), that is, the development of reduced liver function in patients without preexisting liver disease, can result from a wide range of causes, such as viral or bacterial infection, autoimmune disease, or adverse reaction to prescription and over-the-counter medications. ALI patients present with a complex coagulopathy, characterized by both hypercoagulable and hypocoagulable features. Similarly, ALI patients display a profound dysregulation of the fibrinolytic system with the vast majority of patients presenting with a hypofibrinolytic phenotype. Decades of research in experimental acute liver injury in mice suggest that fibrinolytic proteins, including plasmin(ogen), plasminogen activators, fibrinolysis inhibitors, and fibrin(ogen), can contribute to initial hepatotoxicity and/or stimulate liver repair. This review summarizes major experimental findings regarding the role of fibrinolytic factors in ALI from the last approximately 30 years and identifies unanswered questions, as well as highlighting areas for future research.

Advances in multi-omics research on viral hepatitis

Viral hepatitis is a major global public health problem that affects hundreds of millions of people and is associated with significant morbidity and mortality. Five biologically unrelated hepatotropic viruses account for the majority of the global burden of viral hepatitis, including hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). Omics is defined as the comprehensive study of the functions, relationships and roles of various types of molecules in biological cells. The multi-omics analysis has been proposed and considered key to advancing clinical precision medicine, mainly including genomics, transcriptomics and proteomics, metabolomics. Overall, the applications of multi-omics can show the origin of hepatitis viruses, explore the diagnostic and prognostics biomarkers and screen out the therapeutic targets for viral hepatitis and related diseases. To better understand the pathogenesis of viral hepatitis and related diseases, comprehensive multi-omics analysis has been widely carried out. This review mainly summarizes the applications of multi-omics in different types of viral hepatitis and related diseases, aiming to provide new insight into these diseases.

Ficolin-2 Plasma Level Assesses Liver Fibrosis in Non-Alcoholic Fatty Liver Disease

Fibrosis is the strongest predictor for disease-specific mortality in non-alcoholic fatty liver diseases (NAFLD), but the need for liver biopsy limits its diagnosis. We assessed the performance of plasma ficolin-2 (FCN-2) as a biomarker of fibrosis identified by an in silico discovery strategy. Two hundred and thirty-five morbidly obese (MO) subjects with biopsy-proven NAFLD stratified by fibrosis stage (F0, n = 44; F1, n = 134; F2, n = 46; F3/F4, n = 11) and 40 cirrhotic patients were enrolled. The cohort was subdivided into discovery (n = 76) and validation groups (n = 159). The plasma level of FCN-2 and other candidate markers was determined. FCN-2 was inversely correlated with the stage of liver fibrosis (ρ = −0.49, p < 0.001) independently of steatosis (p = 0.90), inflammation (p = 0.57), and ballooning (p = 0.59). In the global cohort, FCN-2 level decreased significantly in a stepwise fashion from F0/F1 (median 4753 ng/mL) to F2−F3−F4 (2760 ng/mL) and in cirrhotic subjects (1418 ng/mL). The diagnostic performance of FCN-2 in detecting F ≥ 2 was higher than other indexes (APRI, FIB-4) (AUROC 0.82, 0.68, and 0.6, respectively). The accuracy improved when combined with APRI score and HDL values (FCNscore, AUROC 0.85). Overall, the FCN-2 plasma level can accurately discriminate liver fibrosis status (minimal vs. moderate/advanced) significantly improving the fibrosis diagnostic algorithms.

Non-invasive precise staging of liver fibrosis using deep residual network model based on plain CT images

PURPOSE

The aim of this study was to explore the application of five-class deep residual network models based on plain CT images and clinical features for the precise staging of liver fibrosis.

METHODS

This retrospective clinical study included 347 patients who underwent liver CT, with pathological staging of liver fibrosis as the gold standard. We established three ResNet models to stage liver fibrosis. The output diagnosis labels of models were 0, 1, 2, 3 and 4, which correspond to F0, F1, F2, F3, and F4 stages. Confusion matrices were used to evaluate the performances of models to precisely stage liver fibrosis. The performance for diagnosing cirrhosis (F4), advanced fibrosis (≥ F3) and significant fibrosis (≥ F2) of models was evaluated with receiver operating characteristic (ROC) analyses.

RESULTS

The kappa coefficients of the five-class ResNet model (based on plain CT images), the five-class ResNet clinical model (based on clinical features), and the five-class mixed ResNet model (based on plain CT images and clinical features) for precise staging liver fibrosis were 0.566, 0.306, and 0.63, respectively. The recall rates and precision rates for F0, F1, F2, and F3 of three models were lower than 60%. The ROC AUC values of the five-class ResNet model, the five-class ResNet clinical model, and the five-class mixed ResNet model for diagnosing cirrhosis, advanced fibrosis, and significant fibrosis were 0.95, 0.88, and 0.82, 0.80, 0.72, and 0.70, 0.95, 0.90, and 0.83, respectively.

CONCLUSIONS

The five-class ResNet models are of high value in the diagnosis of liver cirrhosis, advanced liver fibrosis, and significant liver fibrosis. However, for the precise staging of liver fibrosis, the models cannot accurately distinguish other liver fibrosis stages except F4. Plain CT images combined with clinical features have the potential to improve the performance of the ResNet models in diagnosing liver fibrosis.

Toward Personalized Medicine in Radiotherapy of Hepatocellular Carcinoma: Emerging Radiomic Biomarker Candidates of Response and Toxicity

Radiology and radiotherapy are currently undergoing radical transformation with use of biomarkers and digital technologies such as artificial intelligence. These current and upcoming changes in radiology speak of an overarching new vision for personalized medicine. This is particularly evident in the case of radiotherapy of cancers, and of liver cancer in particular. The development of modern radiotherapy with stereotactic body radiotherapy allows targeted treatments to be delivered to the tumor site, limiting the dose to surrounding healthy organs, thus becoming a new therapeutic alternative for hepatocellular carcinoma and other liver tumors. However, not all patients have the same response to radiotherapy or display the same side-effect profile. Biomarkers of response and toxicity in liver radiotherapy would facilitate the vision and practice of personalized medicine. This expert review examines the available molecular, radiomic, and radiogenomic biomarker candidates for acute liver toxicity with potential use for prediction of radiotherapy-induced liver toxicity. To this end, I highlight for oncologists and life scientists that radiomics allows diagnostic images to be analyzed using computer algorithms to extract information imperceptible to the human eye and of relevance to forecasting clinical outcomes. This article underscores particularly (1) the microRNA-based biomarker candidates as among the most promising predictors of radiation-induced liver toxicity and (2) the texture features in radiomic analyses for response prediction. Radiotherapy of hepatocellular carcinoma is edging toward personalized medicine with emerging radiomic biomarker candidates. Future large-scale biomarker studies are called for to enable personalized medicine in liver cancers.

DNA and RNA Sequencing Recapitulated Aberrant Tumor Metabolism in Liver Cancer Cell Lines

AIM

Metabolic reprogramming has recently attracted extensive attention for understanding cancer development. We aimed to demonstrate a genomic and transcriptomic landscape of metabolic reprogramming underlying liver cancer cell lines.

METHODS

We investigated metabolic aberrant at both the transcriptome and genome levels using transcriptome and whole-exome sequencing data from 12 human liver cancer cell lines (hLCCLs) and one normal liver cell line.

RESULTS

Three subgroups of hLCCLs characterized from transcriptome sequencing data exhibit significantly different aberrations in various metabolic processes, including amino acid, lipid, energy, and carbohydrate metabolism. Furthermore, whole-exome sequencing revealed distinct mutational signatures among different subgroups of hLCCLs and identified a total of 19 known driver genes implicated in metabolism.

CONCLUSION

Our findings highlighted differential metabolic mechanisms in the development of liver cancer and provided a resource for further investigating its metabolic mechanisms.