Metabolic profiling of hepatitis B virus-related hepatocellular carcinoma with diverse differentiation grades

CT-based radiomics for predicting pathological grade in hepatocellular carcinoma

Objective

To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade predictions based on contrast-enhanced CT (CECT).

Methods

Patients with pathologically confirmed HCC after surgery and underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation datasets. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

In total 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation datasets, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model were evaluated as the best for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training dataset; AUC = 0.842 in the validation dataset) and was used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency.

Conclusions

Low- and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model.

Metabolomics Analysis of Viral Therapeutics

Virotherapy, enabled by recent advances in the transdisciplinary field of biotechnology, has emerged as a powerful tool for use in anticancer treatment, gene therapy, immunotherapy, etc. Examining the effects of viruses and virus-derived immune-modulating therapeutics is of great fundamental and clinical interest. Here we describe a sample preparation protocol for metabolite extraction from virus-infected tissue, in addition to liquid chromatography-mass spectrometry conditions essential for subsequent analysis. This metabolomics approach delivers highly sensitive and specific metabolite information on various biospecimens. Such an approach may be adopted to monitor biological changes in over 30 relevant metabolic pathways in response to viral infection and also viral therapeutics.

Imaging of lysophosphatidylcholine in an induced pluripotent stem cell-derived endothelial cell network

Introduction

Vascular endothelial cell disorders are closely related to cardiovascular disease (CVD) and pulmonary diseases. Abnormal lipid metabolism in the endothelium leads to changes in cell signalling, and the expression of genes related to immunity and inflammation. It is therefore important to investigate the pathophysiology of vascular endothelial disorders in terms of lipid metabolism, using a disease model of endothelium.

Methods

Human induced pluripotent stem cell-derived endothelial cells (iECs) were cultured on a matrigel to form an iEC network. Lipids in the iEC network were investigated by matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) analysis. Ion fragments obtained by mass spectrometry were analysed using an infusion method, involving precursor ion scanning with fragment ion.

Results

The MALDI TOF IMS analysis revealed co-localized intensity of peaks at m/z 592.1 and 593.1 in the iEC network. Tandem mass spectrometry (MS/MS) analysis by MALDI-imaging, in conjunction with precursor ion scanning using an infusion method with lipid extracts, identified that these precursor ions were lysophosphatidylcholine (LPC) (22:5) and its isotype.

Conclusion

The MALDI-imaging analysis showed that LPC (22:5) was abundant in an iEC network. As an in vitro test model for disease and potential therapy, present analysis methods using MALDI-imaging combined with, for example, mesenchymal stem cells (MSC) to a disease derived iEC network may be useful in revealing the changes in the amount and distribution of lipids under various stimuli.

Role of lipids in pathophysiology, diagnosis and therapy of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is an aggressive and widespread cancer. Patients with liver cirrhosis of different aetiologies are at a risk to develop HCC. It is important to know that in approximately 20% of cases primary liver tumors arise in a non-cirrhotic liver. Lipid metabolism is variable in patients with chronic liver diseases, and lipid metabolites involved therein do play a role in the development of HCC. Of note, lipid composition of carcinogenic tissues differs from non-affected liver tissues. High cholesterol and low ceramide levels in the tumors protect the cells from oxidative stress and apoptosis, and do also promote cell proliferation. So far, detailed characterization of the mechanisms by which lipids enable the development of HCC has received little attention. Evaluation of the complex roles of lipids in HCC is needed to better understand the pathophysiology of HCC, the later being of paramount importance for the development of urgently needed therapeutic interventions. Disturbed hepatic lipid homeostasis has systemic consequences and lipid species may emerge as promising biomarkers for early diagnosis of HCC. The challenge is to distinguish lipids specifically related to HCC from changes simply related to the underlying liver disease. This review article discusses aberrant lipid metabolism in patients with HCC.

Furosine, a Maillard Reaction Product, Triggers Necroptosis in Hepatocytes by Regulating the RIPK1/RIPK3/MLKL Pathway

As one of the typical Maillard reaction products, furosine has been widely reported in a variety of heat-processed food. Though furosine was shown to be toxic on organs, its toxicity mechanism is still unclear. The present study aimed to investigate the toxicity mechanism of furosine in liver tissue. An intragastric gavage mice model (42-day administration, 0.1/0.25/0.5 g/kg of furosine per day) and a mice primary hepatocyte model were employed to investigate the toxicity mechanism of furosine on mice liver tissue. A metabonomics analysis of mice liver, serum, and red blood cells (RBC) was performed. The special metabolic mediator of furosine, lysophosphatidylcholine 18:0 (LPC (18:0)) was identified. Then, the effect of the upstream gene phospholipase A2 gamma (PLA2-3) on LPC (18:0), as well as the effect of furosine (100 mg/L) on the receptor-interacting serine/threonine-protein kinase (RIPK)1/RIPK3/mixed lineage kinase domain-like protein (MLKL) pathway and inflammatory factors, was determined in liver tissue and primary hepatocytes. PLA2-3 was found to regulate the level of LPC (18:0) and activate the expression of RIPK1, RIPK3, P-MLKL, and of the inflammatory factors including tumor necrosis factor α (TNF-α) and interleukin (IL-1β), both in liver tissue and in primary hepatocytes. Upon treatment with furosine, the upstream sensor PLA2-3 activated the RIPK1/RIPK3/MLKL necroptosis pathway and caused inflammation by regulating the expression of LPC (18:0), which further caused liver damage.

Liver function and energy metabolism in hepatocellular carcinoma developed in patients with hepatitis B-related cirrhosis

Energy metabolism in patients with Hepatocellular carcinoma (HCC) accompanying by hepatitis B cirrhosis is unknown.To compare the differences in liver functions and energy metabolism between patients with hepatitis B-related cirrhosis and patients with HCC.This was a retrospective study of patients with hepatitis B-related cirrhosis (LC group, n = 75) and patients with HCC accompanying by hepatitis B cirrhosis (HCC group, n = 80) treated in Beijing You'an Hospital between January 2013 and June 2017. The resting energy expenditure (REE), respiratory quotient (RQ), carbohydrate oxidation rate (CHO%), fat oxidation rate (FAT%), and protein oxidation rate (PRO%) were measured using a metabolic cart. Liver function, renal function, blood coagulation, etc. were collected.Compared to the LC group, patients with HCC had normal metabolism, but RQ (0.83 ± 0.07 vs 0.85 ± 0.08, P = .073) and CHO% (35.5% vs 49%, P = .013) were lower and FAT% was higher (41% vs 33%, P = .030). Compared with patients with LC group, albumin (ALB), γ-glutamyltranspeptadase (GGT), alkaline phosphatase (AKP), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and prothrombin time activity (PTA) were elevated in the HCC group, while total bilirubin (TB), total bile acid (TBA), and international normalized ratio (INR) were reduced (P < .05). Cholinesterase (CHE) was positively correlated with RQ, CHO, and CHO% (P < .05), while negatively correlated with FAT and FAT% (P < .05). AKP was negatively correlated with RQ, CHO, and CHO% (P < .05), while positively correlated with FAT and FAT% (P < .05). TBA was negatively correlated with RQ and CHO (P < .05), while positively correlated with FAT (P < .05).HCC leads to increased liver synthetic function and improve the liver functions of patients with LC, at least to some extent, but the nutritional metabolism was poor.

A Study on Tissue-Specific Metabolite Variations in Polygonum cuspidatum by High-Resolution Mass Spectrometry-Based Metabolic Profiling

Polygonum cuspidatum Sieb. et Zucc. is a traditional Chinese herbal medicine widely used to treat tussis, hepatitis and arthralgia. This study identified and quantitatively described the bioactive compounds in different P. cuspidatum tissues. Metabolic profiles of root, stem, leaf, flower, rhizome and seed were determined using high-resolution mass spectrometry in combination with multivariate analyses. In total, 53 metabolites, 8 reported for the first time in this species, were putatively identified and classified mainly as stilbenes, anthraquinones and flavonoids. A principal component analysis, cluster analysis and heatmap were used to depict the correlations between specimens and the relative abundance levels of these compounds in different plant tissues. An orthogonal partial least square discriminant analysis found that 13 metabolites showed distinct differences among the six plant tissues, making them potential discriminative tissue-identification markers. This study will provide guidance in comparing, selecting and exploiting the medicinal uses of different P. cuspidatum tissues.

The Robust Classification Model Based on Combinatorial Features

Analyzing the disease data from the view of combinatorial features may better characterize the disease phenotype. In this study, a novel method is proposed to construct feature combinations and a classification model (CFC-CM) by mining key feature relationships. CFC-CM iteratively tests for differences in the feature relationship between different groups. To do this, it uses a modified $k$k-top-scoring pair (M-$k$k-TSP) algorithm and then selects the most discriminative feature pairs in the current feature set to infer the combinatorial features and build the classification model. Compared with support vector machines, random forests, least absolute shrinkage and selection operator, elastic net, and M-$k$k-TSP, the superior performance of CFC-CM on nine public gene expression datasets validates its potential for more precise identification of complex diseases. Subsequently, CFC-CM was applied to two metabolomics datasets, it obtained accuracy rates of $88.73\pm 2.06\%$88.73±2.06% and $79.11\pm 2.70\%$79.11±2.70% in distinguishing between hepatocellular carcinoma and hepatic cirrhosis groups and between acute kidney injury (AKI) and non-AKI samples, results superior to those of the other five methods. In summary, the better results of CFC-CM show that in contrast to molecules and combinations constituted by just two features, the combinations inferred by appropriate number of features could better identify the complex diseases.

Robust Regression Analysis of GCMS Data Reveals Differential Rewiring of Metabolic Networks in Hepatitis B and C Patients

About one in 15 of the world’s population is chronically infected with either hepatitis virus B (HBV) or C (HCV), with enormous public health consequences. The metabolic alterations caused by these infections have never been directly compared and contrasted. We investigated groups of HBV-positive, HCV-positive, and uninfected healthy controls using gas chromatography-mass spectrometry analyses of their plasma and urine. A robust regression analysis of the metabolite data was conducted to reveal correlations between metabolite pairs. Ten metabolite correlations appeared for HBV plasma and urine, with 18 for HCV plasma and urine, none of which were present in the controls. Metabolic perturbation networks were constructed, which permitted a differential view of the HBV- and HCV-infected liver. HBV hepatitis was consistent with enhanced glucose uptake, glycolysis, and pentose phosphate pathway metabolism, the latter using xylitol and producing threonic acid, which may also be imported by glucose transporters. HCV hepatitis was consistent with impaired glucose uptake, glycolysis, and pentose phosphate pathway metabolism, with the tricarboxylic acid pathway fueled by branched-chain amino acids feeding gluconeogenesis and the hepatocellular loss of glucose, which most probably contributed to hyperglycemia. It is concluded that robust regression analyses can uncover metabolic rewiring in disease states.

Metabolomic and transcriptomic profiling of hepatocellular carcinomas in Hras12V transgenic mice

Activation of the Ras/MAPK pathway is prevalently involved in the occurrence and development of hepatocellular carcinoma (HCC). However, its effects on the deregulated cellular metabolic processes involved in HCC in vivo remain unknown. In this study, a mouse model of HCC induced by hepatocyte-specific expression of the Hras12V oncogene was investigated using an integrative analysis of metabolomics and transcriptomics data. Consistent with the phenotype of abundant lipid droplets in HCC, the lipid biosynthesis in HCC was significantly enhanced by (1) a sufficient supply of acetyl-CoA from enhanced glycolysis and citrate shuttle activity; (2) a sufficient supply of NADPH from enhanced pentose phosphate pathway (PPP) activity; (3) upregulation of key enzymes associated with lipid biosynthesis; and (4) downregulation of key enzymes associated with bile acid biosynthesis. In addition, glutathione (GSH) was significantly elevated, which may result from a sufficient supply of 5-oxoproline and L-glutamate as well as an enhanced reduction in the process of GSSG being turned into GSH by NADPH. The high level of GSH along with elevated Bcl2 and Ucp2 expression may contribute to a normal level of reactive oxygen species (ROS) in HCC. In conclusion, our results suggest that the lipid metabolism, glycolysis, PPP, tricarboxylic acid (TCA) cycle, citrate shuttle activity, bile acid synthesis, and redox homeostasis in the HCC induced by ras oncogene are significantly perturbed, and these altered metabolic processes may play crucial roles in the carcinogenesis, development, and pathological characteristics of HCC.

Comparative Analysis for Glycopatterns and Complex-Type N-Glycans of Glycoprotein in Sera from Chronic Hepatitis B- and C-Infected Patients

Background: Chronic infection with HBV (CHB) or HCV (CHC) is the most common chronic viral hepatitis that can lead to cirrhosis and hepatocellular carcinoma in humans, their infections have distinct pathogenic processes, however, little is known about the difference of glycoprotein glycopatterns in serum between hepatitis B virus (HBV)- and hepatitis C virus (HCV)-infected patients.

Methods: A method combining the lectin microarrays, letin-mediated affinity capture glycoproteins, and MALDI-TOF/TOF-MS was employed to analyze serum protein glycopatterns and identify the glycan structures from patients with CHB (n = 54) or CHC(n = 47), and healthy volunteers (HV, n = 35). Lectin blotting was further utilized to validate and assess the expression levels of their serum glycopatterns. Finally, the differences of the glycoprotein glycopatterns were systematically compared between CHB and CHC patients.

Conclusions: As a result, there were 11 lectins (e.g., HHL, GSL-II, and EEL) exhibited significantly increased expression levels, and three lectins (LCA, VVA, and ACA) exhibited significantly decreased expression levels of serum protein glycopatterns only in the CHB patients. However, DBA exhibited significantly decreased expression levels, and two lectins (WGA and SNA) exhibited significantly increased expression levels of serum glycopatterns only in the CHC patients. Furthermore, LEL and MAL-I showed a coincidentally increasing trend in both CHC and CHB patients compared with the HV. The individual analysis demonstrated that eight lectins (MPL, GSL-I, PTL-II, UEA-I, WGA, LEL, VVA, and MAL-I) exhibited a high degree of consistency with the pooled serum samples of HV, CHB, and CHC patients. Besides, a complex-type N-glycans binder PHA-E+L exhibited significantly decreased NFIs in the CHB compared with HV and CHC subjects (p < 0.01). The MALDI-TOF/TOF-MS results of N-linked glycans from the serum glycoproteins isolated by PHA-E+L-magnetic particle conjugates showed that there was an overlap of 23 N-glycan peaks (e.g., m/z 1419.743, 1663.734, and 1743.581) between CHB, and CHC patients, 5 glycan peaks (e.g., m/z 1850.878, 1866.661, and 2037.750) were presented in virus-infected hepatitis patients compared with HV, 3 glycan peaks (1460.659, 2069.740, and 2174.772) were observed only in CHC patients. Our data provide useful information to find new biomarkers for distinguishing CHB and CHC patients based on the precision alteration of their serum glycopatterns.