Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma

Metabolomic changes of blood plasma associated with two phases of rat OIR

Although acute hyperoxia/hypoxia alternation can shift sharply physiological processes of vessel development, e.g. oxygen induced retinopathy (OIR), very little is known of metabolic products resulted from the neovascularization disorder. In this study, the influence of abnormal oxygen exposures on the plasma metabolomic profiles of rats with OIR was investigated by the gas chromatography mass spectrometry (GC-MS). Rat pups were divided into four groups, each with 12 individuals: (i) reared in room air and sampled at P12 (CT1); (ii) exposed to high oxygen for 5 days and sampled at P12 (HO1, simulating the vaso-obeliteration process (phase I)); (iii) reared in room air and sampled at P17 (CT2); (iv) exposed to high oxygen for 5 days then followed by room air for 5 days and sampled at P17 (HO2, simulating the neovasculization one (phase II)). Plasma samples were analyzed with GC-MS, resulted in 122 metabolite species. Distinct differences in the plasma metabolome were found between groups of CT1 vs. HO1, and HO1 vs. HO2, by using univariate and multivariate analyses. Alternating hyperoxia/hypoxia conditions induced significant changes of richness of proline, ornithine and glutamine, that were important components of 'arginine and proline metabolism' pathway. These metabolites contributed largely to plasma sample classification, determined with receiver operating characteristic curve analysis and were involved profoundly in the proline-dependent production of reactive oxygen species (ROS) related to the cellular redox reactions. Our results from the rat OIR model suggest proline and 'arginine and proline metabolism' pathway as the potential biomarkers for human retinopathy of prematurity (ROP) diagnosis.

Metabolomic Profiles for HBV Related Hepatocellular Carcinoma Including Alpha-Fetoproteins Positive and Negative Subtypes

Background: Hepatocellular carcinoma (HCC) is very common globally prevalent cancer. Due to its poor clinical prognosis, increasing the diagnostic rate of HCC is urgently needed. Herein, we validate discovered metabolomic biomarkers to distinguish Hepatitis B virus (HBV)-related HCC, including alpha-fetoprotein (AFP) negative (AFP–) and positive (AFP+) individuals.

Methods: We recruited 130 HCC subjects (independent case-control, randomized clinical cohorts) to our study. We separated the subjects randomly into two panels: (1) 58 individuals for the discovery panel; and (2) 72 individuals for the validation panel. For each panel, gender and age-matched hepatitis B group (HBG) and healthy group were included as controls. Plasma samples were collected for metabolic profiling by liquid chromatography—mass spectrometry—based metabolomics assays. We applied both non-targeted metabolomics analyses and targeted metabolomics analyses. Significantly changed metabolites (SCMs) were identified. The power of SCMs to discriminate HCC and HBG or healthy group was determined by receiver operating characteristic curve (ROC) analysis.

Results: Ten SCMs were selected form the discovery panel, and further verified in the validation panel. ROC analyses indicated that 1 SCMs (LysoPC (24:0)) could discriminate HCC from HBG (AUC = 0.765). Further, 8 SCMs including (LysoPC (17:0), LysoPC (20:4(8Z,11Z,14Z,17Z)), LysoPC (22:0), LysoPC (24:0), PE (P-16:0/22:4(7Z,10Z,13Z,16Z)), SM (d18:1/22:1(13Z)), Creatinine, and L-Isoleucine) displayed a heightened ability to discriminate between HCC and healthy controls (AUC were more than 0.800). Most of these SCMs were important in lipid metabolism.

Conclusions: LysoPC (24:0) could distinguished HCC from HBG, and 8 SCMs distinguished HCC from healthy controls. LysoPC and other metabolites have the potential to serve as non-invasive biomarkers for HBV related AFP– and AFP+ HCC.

Plasma metabolite profiling reveals potential biomarkers of giant cell tumor of bone by using NMR-based metabolic profiles: A cross-sectional study

Giant cell tumor (GCT) of bone is a locally aggressive bone tumor, which accounts for 4% to 5% of all primary bone tumors. At present, the early diagnosis and postoperative recurrence monitoring are still more difficult due to the lack of effective biomarkers in GCT. As an effective tool, metabolomics has played an essential role in the biomarkers research of many tumors. However, there has been no related study of the metabolomics of GCT up to now. The purpose of this study was to identify several key metabolites as potential biomarkers for GCT by using nuclear magnetic resonance (NMR)-based metabolic profiles.Patients with GCT in our hospital were recruited in this study and their plasma was collected as the research sample, and plasma collected from healthy subjects was considered as the control. NMR was then utilized to detect all samples. Furthermore, based on correlation coefficients, variable importance for the projection values and P values of metabolites obtained from multidimensional statistical analysis, the most critical metabolites were selected as potential biomarkers of GCT. Finally, relevant metabolic pathways involved in these potential biomarkers were determined by database retrieval, based on which the metabolic pathways were plotted.Finally, 28 GCT patients and 26 healthy volunteers agreed to participate in the study. In the multidimensional statistical analysis, all results showed that there was obvious difference between the GCT group and the control group. Ultimately, 18 metabolites with significant differences met the selection condition, which were identified as potential biomarkers. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Human Metabolome Database (HMD) database searching and literature review, these metabolites were found to be mainly correlated with glucose metabolism, fat metabolism, amino acid metabolism, and intestinal microbial metabolism. These metabolic disorders might, in turn, reflect important pathological processes such as proliferation and migration of tumor cells and immune escape in GCT.Our work showed that these potential biomarkers identified appeared to have early diagnostic and relapse monitoring values for GCT, which deserve to be further investigated. In addition, it also suggested that metabolomics profiling approach is a promising screening tool for the diagnosis and relapse monitoring of GCT patients.

Role of bile acids in the diagnosis and progression of liver cirrhosis: A prospective observational study

The accumulation of toxic bile acids (BAs) is closely related to liver injury, inflammation and tumorigenesis. The aim of the present study was to determine the role of the serum BA spectrum in the diagnosis and progression of liver cirrhosis. This was a prospective observational study involving patients with chronic hepatitis (n=23), liver cirrhosis (n=101), and cirrhosis complicated with hepatocellular carcinoma (CC-HCC; n=56). The 6-month survival of cirrhotic patients was recorded after blood collection. Comparisons of serum total BAs and individual BAs between different groups were performed using the Mann-Whitney U or Kruskal-Wallis tests. Correlation analysis was conducted by Spearman's correlation. Diagnosis and prediction analyses were performed using receiver operating characteristic curves. Survival was analyzed using the Kaplan-Meier method and multivariable Cox regression analysis. The concentrations of total BAs, glycocholic acid (GCA), glycochenodeoxycholic acid (GCDCA), taurocholic acid (TCA), taurochenoxycholic acid and tauroursodeoxycholic acid (TUDCA) were increased significantly in patients with early cirrhosis compared to patients with chronic hepatitis (P<0.05) and were associated with the diagnosis of cirrhosis (P=0.049, 0.004, 0.002, 0.003, 0.010 and 0.009, respectively). The levels of total BAs, primary conjugated BAs, and TUDCA increased as liver cirrhosis progressed (P<0.05). Serum total BAs, GCA, GCDCA, and TCA predicted the 6-month survival of patients with liver cirrhosis (P=0.0003, 0.005, 0.002, and 0.010 respectively). Based on multivariate Cox regression analysis, the level of total BAs was an independent predictor of mortality in cirrhotic patients (hazard ratios, 4.046; 95% CI, 1.620-10.108; P=0.003). In the early-stage cirrhosis group, the concentrations of total BAs and primary conjugated BAs were significantly elevated in patients with CC-HCC compared with patients with cirrhosis alone. In conclusion, total and individual BAs, especially primary conjugated BAs, are effective non-invasive markers in the diagnosis and prognosis of liver cirrhosis, and may be potential indicators in the occurrence of hepatocellular carcinoma in patients with early cirrhosis.

Review and Comparison of Cancer Biomarker Trends in Urine as a Basis for New Diagnostic Pathways

Cancer is one of the major causes of mortality worldwide and its already large burden is projected to increase significantly in the near future with a predicted 22 million new cancer cases and 13 million cancer-related deaths occurring annually by 2030. Unfortunately, current procedures for diagnosis are characterized by low diagnostic accuracies. Given the proved correlation between cancer presence and alterations of biological fluid composition, many researchers suggested their characterization to improve cancer detection at early stages. This paper reviews the information that can be found in the scientific literature, regarding the correlation of different cancer forms with the presence of specific metabolites in human urine, in a schematic and easily interpretable form, because of the huge amount of relevant literature. The originality of this paper relies on the attempt to point out the odor properties of such metabolites, and thus to highlight the correlation between urine odor alterations and cancer presence, which is proven by recent literature suggesting the analysis of urine odor for diagnostic purposes. This investigation aims to evaluate the possibility to compare the results of studies based on different approaches to be able in the future to identify those compounds responsible for urine odor alteration.

Multiple “Omics” data-based biomarker screening for hepatocellular carcinoma diagnosis

The huge prognostic difference between early and late stage hepatocellular carcinoma (HCC) is a challenging diagnostic problem. Alpha-fetoprotein is the mostly widely used biomarker for HCC used in the clinic, however it’s sensitivity and specificity of is not optimal. The development and application of multiple biotechnologies, including next generation sequencing, multiple “omics” data, that include genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics has been used for HCC diagnostic biomarker screening. Effective biomarkers/panels/models have been identified and validated at different clinical levels. A large proportion of these have a good diagnostic performance for HCC, especially for early HCC. In this article, we reviewed the various HCC biomarkers derived from “omics” data and discussed the advantages and disadvantages for diagnosis HCC.

From NASH to HCC: current concepts and future challenges

Caloric excess and sedentary lifestyle have led to a global epidemic of obesity and metabolic syndrome. The hepatic consequence of metabolic syndrome and obesity, nonalcoholic fatty liver disease (NAFLD), is estimated to affect up to one-third of the adult population in many developed and developing countries. This spectrum of liver disease ranges from simple steatosis to nonalcoholic steatohepatitis (NASH) and cirrhosis. Owing to the high prevalence of NAFLD, especially in industrialized countries but also worldwide, and the consequent burden of progressive liver disease, there is mounting epidemiological evidence that NAFLD has rapidly become a leading aetiology underlying many cases of hepatocellular carcinoma (HCC). In this Review, we discuss NAFLD-associated HCC, including its epidemiology, the key features of the hepatic NAFLD microenvironment (for instance, adaptive and innate immune responses) that promote hepatocarcinogenesis and the management of HCC in patients with obesity and associated metabolic comorbidities. The challenges and future directions of research will also be discussed, including clinically relevant biomarkers for early detection, treatment stratification and monitoring as well as approaches to therapies for both prevention and treatment in those at risk or presenting with NAFLD-associated HCC.

Recognition of early and late stages of bladder cancer using metabolites and machine learning

INTRODUCTION

Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and biopsy. This process is expensive, unpleasant, and may have severe side effects. Recent growth in the power and accessibility of machine-learning software has allowed for the development of new, non-invasive diagnostic methods whose accuracy and sensitivity are uncompromising to function.

OBJECTIVES

The goal of this research was to elucidate the biomarkers including metabolites and corresponding genes for different stages of BCa, show their distinguishing and common features, and create a machine-learning model for classification of stages of BCa.

METHODS

Sets of metabolites for early and late stages, as well as common for both stages were analyzed using MetaboAnalyst and Ingenuity® Pathway Analysis (IPA®) software. Machine-learning methods were utilized in the development of a binary classifier for early- and late-stage metabolites of BCa. Metabolites were quantitatively characterized using EDragon 1.0 software. The two modeling methods used are Multilayer Perceptron (MLP) and Stochastic Gradient Descent (SGD) with a logistic regression loss function.

RESULTS

We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is D-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2'-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites. The model was better at predicting early-stage metabolites with accuracies of 72% (18/25) and 95% (19/20) on the early sets, and an accuracy of 65.45% (36/55) on the late-stage metabolite set.

CONCLUSION

By examining the biomarkers present in the urine samples of BCa patients as compared with normal patients, the biomarkers associated with this cancer can be pinpointed and lead to the elucidation of affected metabolic pathways that are specific to different stages of cancer. Development of machine-learning model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of stages of BCa.

Metabolomic Characterization of Human Model of Liver Rejection Identifies Aberrancies Linked to Cyclooxygenase (COX) and Nitric Oxide Synthase (NOS)

BACKGROUND Acute liver rejection (ALR), a significant complication of liver transplantation, burdens patients, healthcare payers, and the healthcare providers due to an increase in morbidity, cost, and resources. Despite clinical resolution, ALR is associated with an increased risk of graft loss. A unique protocol of delayed immunosuppression used in our institute provided a model to characterize metabolomic profiles in human ALR. MATERIAL AND METHODS Twenty liver allograft biopsies obtained 48 hours after liver transplantation in the absence of immunosuppression were studied. Hepatic metabolites were quantitated in these biopsies by liquid chromatography and mass spectroscopy (LC/MS). Metabolite profiles were compared among: 1) biopsies with reperfusion injury but no histological evidence of rejection (n=7), 2) biopsies with histological evidence of moderate or severe rejection (n=5), and 3) biopsies with histological evidence of mild rejection (n=8). RESULTS There were 133 metabolites consistently detected by LC/MS and these were prioritized using variable importance to projection (VIP) analysis, comparing moderate or severe rejection vs. no rejection or mild rejection using partial least squares discriminant statistical analysis (PLS-DA). Twenty metabolites were identified as progressively different. Further PLS-DA using these metabolites identified 3 metabolites (linoleic acid, γ-linolenic acid, and citrulline) which are associated with either cyclooxygenase or nitric oxide synthase functionality. CONCLUSIONS Hepatic metabolic aberrancies associated with cyclooxygenase and nitric oxide synthase function occur contemporaneous with ALR. Additional studies are required to better characterize the role of these metabolic pathways to enhance utility of the metabolomics approach in diagnosis and outcomes of ALR.

Untargeted metabolomic profiling of urine from healthy dogs and dogs with chronic hepatic disease

Chronic hepatic disease can present a diagnostic challenge with different etiologies being associated with similar clinical and laboratory findings. The histopathological assessment of a liver biopsy specimen is usually required in order to make a definitive diagnosis and the availability of non-invasive prognostic biomarkers is limited. The emerging science of metabolomics is used to detect changes in endogenous low molecular weight metabolites in biological samples and offers the possibility of identifying noninvasive markers of disease. The objective of this study was to investigate differences in the urine metabolome between healthy dogs, dogs with chronic hepatitis, dogs with hepatocellular carcinoma, and dogs with a congenital portosystemic shunt. Stored urine samples from 10 healthy dogs, 10 dogs with chronic hepatitis, 6 dogs with hepatocellular carcinoma, and 5 dogs with a congenital portosystemic shunt were analyzed. The urine metabolome was analyzed by gas chromatography–quadrupole time of flight mass spectrometry and 220 known metabolites were identified. Principal component analysis and heat dendrogram plots of the metabolomics data showed clustering between groups. Random forest analysis showed differences in the abundance of various metabolites including putrescine, gluconic acid, sorbitol, and valine. Based on univariate statistics, 37 metabolites were significantly different between groups. In, conclusion, the urine metabolome varies between healthy dogs, dogs with chronic hepatitis, dogs with hepatocellular carcinoma, and dogs with a congenital portosystemic shunt. Further targeted assessment of these metabolites is needed to assess their diagnostic utility.

Effect of Aerobic Exercise on Serum Metabolites in Mice with Hepatocellular Carcinoma After Surgery

Background

Modern medicine has suggested exercise therapy is one of the main treatments for postoperative rehabilitation of tumors. It can influence the recovery of cancer patients by changing the body’s material metabolism and energy metabolism. However, studies on metabolic changes of exercise therapy on hepatocellular carcinoma (HCC) patients after surgery are limited. The aim of this study was to explore the effect of aerobic exercise on mice after orthotopic HCC surgery by serum metabolomics test and explore the related mechanism.

Material/Methods

A total of 60 C57Bl/6 mice were used to establish an orthotopic xenograft model of H22 mouse hepatoma cells. Mice were randomly divided into 6 groups and it was found that the metabolic products of the early postoperative exercise group and sedentary group mainly included L-tryptophan, citric acid, and other energy-related metabolites.

Results

Energy metabolites, such as succinic acid of the high-intensity exercise group were increased after surgery, whereas phospholipid metabolites, including phosphatidylethanolamine (18: 0/0: 0), were decreased. In the moderate-intensity exercise group, the change tendency was consistent, and the level of various metabolites decreased.

Conclusions

Thus, it is likely that aerobic exercise reduced the degree of postoperative stress responses and improved energy metabolism in mice. The underlying mechanism involves improving the tricarboxylic acid cycle, intervening in energy metabolism, reorganization caused by the tumor, reducing the abnormal increase of phospholipase activity caused by the stress of liver cancer, reducing the level of hemolytic phospholipids, thereby inhibiting mitochondrial pathway-initiated apoptosis.

Bile Acids as Potential Biomarkers to Assess Liver Impairment in Polycystic Kidney Disease

Polycystic kidney disease is characterized by the progressive development of kidney cysts and declining renal function with frequent development of cysts in other organs including the liver. The polycystic kidney (PCK) rat is a rodent model of polycystic liver disease that has been used to study hepatorenal disease progression and evaluate pharmacotherapeutic interventions. Biomarkers that describe the cyst progression, liver impairment, and/or hepatic cyst burden could provide clinical utility for this disease. In the present study, hepatic cyst volume was measured by magnetic resonance imaging in PCK rats at 12, 16, and 20 weeks. After 20 weeks, Sprague Dawley (n = 4) and PCK (n = 4) rats were sacrificed and 42 bile acids were analyzed in the liver, bile, serum, and urine by liquid chromatography coupled to tandem mass spectrometry. Bile acid profiling revealed significant increases in total bile acids (molar sum of all measured bile acids) in the liver (13-fold), serum (6-fold), and urine (3-fold) in PCK rats, including those speciated bile acids usually associated with hepatotoxicity. Total serum bile acids correlated with markers of liver impairment (liver weight, total liver bile acids, total hepatotoxic liver bile acids, and cyst volume [ r > 0.75; P < 0.05]). Based on these data, serum bile acids may be useful biomarkers of liver impairment in polycystic hepatorenal disease.

Diagnosis of hepatocellular carcinoma using a novel anti-glycocholic acid monoclonal antibody-based method

Glycocholic acid (GCA) is a novel identified biomarker for hepatocellular carcinoma (HCC). However, clinical pathological study of GCA has not been extensive due to the limited availability of anti-GCA monoclonal antibodies (mAbs) and restricted detection methods. In the present study, using human GCA conjugated with bovine serum albumin as the immunogen to immunize BALB/c mice, a novel anti-GCA mAb was generated and characterized. The isotypes of heavy chain and light chain of anti-GCA mAb were examined to be IgG2a and κ, respectively, with a high affinity constant (2.6×10⁸ mol/l). The anti-GCA mAb binds GCA with high specificity and sensitivity, and the 50% inhibitory rate was 77.09 ng/ml. The present study also established a rapid, sensitive and efficient indirect competitive ELISA analysis using this anti-GCA mAb to detect the level of GCA produced by different HCC cell lines. Therefore, the present study may successfully develop a novel method for early HCC diagnosis, and also provide insights for further research and treatment of HCC.

Dynamic changes of key metabolites during liver fibrosis in rats

BACKGROUND

Fibrosis is the single most important predictor of significant morbidity and mortality in patients with chronic liver disease. Established non-invasive tests for monitoring fibrosis are lacking, and new biomarkers of liver fibrosis and function are needed.

AIM

To depict the process of liver fibrosis and look for novel biomarkers for diagnosis and monitoring fibrosis progression.

METHODS

CCl4 was used to establish the rat liver fibrosis model. Liver fibrosis process was measured by liver chemical tests, liver histopathology, and Masson's trichrome staining. The expression levels of two fibrotic markers including α-smooth muscle actin and transforming growth factor β1 were assessed using immunohistochemistry and real-time polymerase chain reaction. Dynamic changes in metabolic profiles and biomarker concentrations in rat serum during liver fibrosis progression were investigated using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. The discriminatory capability of potential biomarkers was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

To investigate the dynamic changes of metabolites during the process of liver fibrosis, sera from control and fibrosis model rats based on pathological results were analyzed at five different time points. We investigated the association of liver fibrosis with 21 metabolites including hydroxyethyl glycine, L-threonine, indoleacrylic acid, β-muricholic acid (β-MCA), cervonoyl ethanolamide (CEA), phosphatidylcholines, and lysophosphatidylcholines. Two metabolites, CEA and β-MCA, differed significantly in the fibrosis model rats compared to controls (P < 0.05) and showed prognostic value for fibrosis. ROC curve analyses performed to calculate the area under the curve (AUC) revealed that CEA and β-MCA differed significantly in the fibrosis group compared to controls with AUC values exceeding 0.8, and can clearly differentiate early stage from late stage fibrosis or cirrhosis.

CONCLUSION

This study identified two novel biomarkers of fibrosis, CEA and β-MCA, which were effective for diagnosing fibrosis in an animal model.

Function, Detection and Alteration of Acylcarnitine Metabolism in Hepatocellular Carcinoma

Acylcarnitines play an essential role in regulating the balance of intracellular sugar and lipid metabolism. They serve as carriers to transport activated long-chain fatty acids into mitochondria for β-oxidation as a major source of energy for cell activities. The liver is the most important organ for endogenous carnitine synthesis and metabolism. Hepatocellular carcinoma (HCC), a primary malignancy of the live with poor prognosis, may strongly influence the level of acylcarnitines. In this paper, the function, detection and alteration of acylcarnitine metabolism in HCC were briefly reviewed. An overview was provided to introduce the metabolic roles of acylcarnitines involved in fatty acid β-oxidation. Then different analytical platforms and methodologies were also briefly summarised. The relationship between HCC and acylcarnitine metabolism was described. Many of the studies reported that short, medium and long-chain acylcarnitines were altered in HCC patients. These findings presented current evidence in support of acylcarnitines as new candidate biomarkers for studies on the pathogenesis and development of HCC. Finally we discussed the challenges and perspectives of exploiting acylcarnitine metabolism and its related metabolic pathways as a target for HCC diagnosis and prognosis.

Tissue and serum metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma

BACKGROUND AND AIMS

Metabolomics serves as an important tool in distinguishing changes in metabolic pathways and the diagnosis of human disease. Hepatocellular carcinoma (HCC) is a malignance present of heterogeneous metabolic disorder and lack of effective biomarker for surveillance and diagnosis. In this study, we searched for potential metabolite biomarkers of HCC using tissue and serum metabolomics approach.

METHODS

A total of 30 pairs of matched liver tissue samples from HCC patients and 90 serum samples (30 HCC patients, 30 liver cirrhosis patients, and 30 healthy individuals) were assessed. Metabolomics was performed through ultra performance liquid chromatography-mass spectrometry in conjunction with multivariate and univariate statistical analyses.

RESULTS

A total of six differential metabolites including chenodeoxycholic acid (CDCA), glycocholic acid (GCA), LPC20:5, LPE18:0, succinyladenosine and uridine were present in HCC tissue and serum samples. CDCA, LPC20:5, succinyladenosine and uridine were used to construct a diagnostic model based on logistic regression. The four-metabolite panel discriminated HCC from liver cirrhosis with an AUC score of 0.938, sensitivity of 93.3% and specificity of 86.7%. For all HCC and cirrhosis patients, the diagnostic accuracy increased to 96.7% and 90.0%, respectively.

CONCLUSION

The combination of CDCA, LPC20:5, succinyladenosine and uridine can be used as a biomarker panel to improve HCC sensitivity and specificity. This panel significantly benefits HCC diagnostics and reveals new insight into HCC pathogenesis.

Suppression of Hepatic Bile Acid Synthesis by a non-tumorigenic FGF19 analogue Protects Mice from Fibrosis and Hepatocarcinogenesis

Critical regulation of bile acid (BA) pool size and composition occurs via an intensive molecular crosstalk between the liver and gut, orchestrated by the combined actions of the nuclear Farnesoid X receptor (FXR) and the enterokine fibroblast growth factor 19 (FGF19) with the final aim of reducing hepatic BA synthesis in a negative feedback fashion. Disruption of BA homeostasis with increased hepatic BA toxic levels leads to higher incidence of hepatocellular carcinoma (HCC). While native FGF19 has anti-cholestatic and anti-fibrotic activity in the liver, it retains peculiar pro-tumorigenic actions. Thus, novel analogues have been generated to avoid tumorigenic capacity and maintain BA metabolic action. Here, using BA related Abcb4^-/- and Fxr^-/- mouse models of spontaneous hepatic fibrosis and HCC, we explored the role of a novel engineered variant of FGF19 protein, called FGF19-M52, which fully retains BA regulatory activity but is devoid of the pro-tumoral activity. Expression of the BA synthesis rate-limiting enzyme Cyp7a1 is reduced in FGF19-M52-treated mice compared to the GFP-treated control group with consequent reduction of BA pool and hepatic concentration. Treatment with the non-tumorigenic FGF19-M52 strongly protects Abcb4^-/- and Fxr^-/- mice from spontaneous hepatic fibrosis, cellular proliferation and HCC formation in terms of tumor number and size, with significant reduction of biochemical parameters of liver damage and reduced expression of several genes driving the proliferative and inflammatory hepatic scenario. Our data bona fide suggest the therapeutic potential of targeting the FXR-FGF19 axis to reduce hepatic BA synthesis in the control of BA-associated risk of fibrosis and hepatocarcinoma development.

Aberrant Metabolism in Hepatocellular Carcinoma Provides Diagnostic and Therapeutic Opportunities

Hepatocellular carcinoma (HCC) accounts for over 80% of liver cancer cases and is highly malignant, recurrent, drug-resistant, and often diagnosed in the advanced stage. It is clear that early diagnosis and a better understanding of molecular mechanisms contributing to HCC progression is clinically urgent. Metabolic alterations clearly characterize HCC tumors. Numerous clinical parameters currently used to assess liver functions reflect changes in both enzyme activity and metabolites. Indeed, differences in glucose and acetate utilization are used as a valid clinical tool for stratifying patients with HCC. Moreover, increased serum lactate can distinguish HCC from normal subjects, and serum lactate dehydrogenase is used as a prognostic indicator for HCC patients under therapy. Currently, the emerging field of metabolomics that allows metabolite analysis in biological fluids is a powerful method for discovering new biomarkers. Several metabolic targets have been identified by metabolomics approaches, and these could be used as biomarkers in HCC. Moreover, the integration of different omics approaches could provide useful information on the metabolic pathways at the systems level. In this review, we provided an overview of the metabolic characteristics of HCC considering also the reciprocal influences between the metabolism of cancer cells and their microenvironment. Moreover, we also highlighted the interaction between hepatic metabolite production and their serum revelations through metabolomics researches.

Alteration of Serum Free Fatty Acids are Indicators for Progression of Pre-leukaemia Diseases to Leukaemia

Acute Leukaemia (AL) is a neoplasm of WBCs (white blood cells). Being an important class of metabolites, alteration in free fatty acids (FFAs) levels play a key role in cancer development and progression. As they involve in cell signaling, maintain membrane integrity, regulate homeostasis and effect cell and tissue functions. Considering this fact, a comprehensive analysis of FFAs was conducted to monitor their alteration in AL, pre-leukaemic diseases and healthy control. Fifteen FFAs were analyzed in 179 serum samples of myelodysplastic syndrome (MDS), aplastic anemia (APA), acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML) and healthy control using gas chromatography-multiple reaction monitoring-mass spectrometry (GC-MRM-MS). A multivariate statistical method of random forest (RF) was employed for chemometric analysis. Serum level of two FFAs including C18:0 and C14:0 were found discriminative among all five groups, and between ALL and AML, respectively. Moreover, C14:0 was identified as differentiated FFAs for systematic progression of pre-leukaemic conditions towards AML. C16:0 came as discriminated FFAs between APA and MDS/AML. Over all it was identified that FFAs profile not only become altered in leukaemia but also in pre-leukaemic diseases.

Clinical applications of liquid biopsy as prognostic and predictive biomarkers in hepatocellular carcinoma: circulating tumor cells and circulating tumor DNA

Hepatocellular carcinoma (HCC) is a highly malignant disease with a poor prognosis and high mortality due to a low early diagnosis rate, resistance to systemic treatments and progression to late-stage liver disease. Owing to limitations in the detection of HCC and the lack of awareness of healthcare systems, fewer than 40% of HCC patients are eligible for surgery due to advanced stages of the disease at the time of diagnosis and the occurrence of multiple lesions in the cirrhotic or fibrotic liver. At present, the updated American Association for the Study of Liver Disease (AASLD) guidelines no longer recommend alpha-fetoprotein (AFP) testing as a part of diagnostic evaluation. Thus, it is imperative to establish a novel diagnostic strategy with high sensitivity and reliability to monitor risk factors to detect HCC at an early stage. In recent years, “liquid biopsy,” (including circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA)), has emerged as a technique for the characterization of circulating cells, providing a strong basis for the individualized treatment of patients. As a noninvasive detection method, liquid biopsy is expected to play an important role in the early diagnosis, dynamic monitoring of cancer patients and drug screening. In this review, we will focus on the clinical applications, recent studies and future prospects of liquid biopsy, particularly focusing on HCC.

Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning

Clinical prediction of advanced hepatic fibrosis (HF) and cirrhosis has long been challenging due to the gold standard, liver biopsy, being an invasive approach with certain limitations. Less invasive blood test tandem with a cutting-edge machine learning algorithm shows promising diagnostic potential. In this study, we constructed and compared machine learning methods with the FIB-4 score in a discovery dataset (n = 490) of hepatitis B virus (HBV) patients. Models were validated in an independent HBV dataset (n = 86). We further employed these models on two independent hepatitis C virus (HCV) datasets (n = 254 and 230) to examine their applicability. In the discovery data, gradient boosting (GB) stably outperformed other methods as well as FIB-4 scores (p < .001) in the prediction of advanced HF and cirrhosis. In the HBV validation dataset, for classification between early and advanced HF, the area under receiver operating characteristic curves (AUROC) of GB model was 0.918, while FIB-4 was 0.841; for classification between non-cirrhosis and cirrhosis, GB showed AUROC of 0.871, while FIB-4 was 0.830. Additionally, GB-based prediction demonstrated good classification capacity on two HCV datasets while higher cutoffs for both GB and FIB-4 scores were required to achieve comparable specificity and sensitivity. Using the same parameters as FIB-4, the GB-based prediction system demonstrated steady improvements relative to FIB-4 in HBV and HCV cohorts with different cutoff values required in different etiological groups. A user-friendly web tool, LiveBoost, makes our prediction models freely accessible for further clinical studies and applications.

Rapid Detection of Small Molecule Metabolites in Serum of Hepatocellular Carcinoma Patients Using Ultrafast Liquid Chromatography-Ion Trap-Time of Flight Tandem Mass Spectrometry

A method was developed for analyzing broad spectrum small molecule metabolites in the serum of hepatocellular carcinoma (HCC) patients based on ultrafast liquid chromatography-ion trap-time of flight tandem mass spectrometry (UFLC-IT-TOF MS). Serum samples were collected from 80 HCC patients and healthy persons. After pretreatment process for protein precipitation, the supernatant was analyzed with the UFLC-IT-TOF MS to obtain information on the metabonomics of small molecules. The eight compounds of glycocholic acid, choline glycerophosphate, acetyl-L-phenylalanine, oleamide, tetradecanamide, acetylcarnitine, lysolecithin and glycochenodeoxycholic acid in the HCC group were identified with significant differences from those in the health group (P <0.01). By using multidimensional analysis of variation coefficient and principal component analysis for the repeatability and 48 h stability, the method was demonstrated to have good repeatability, excellent precision, and high stability, which can satisfy the metabonomics research requirement. The high throughput and practical usability of the method further shows perspective for metabonomic analysis of large-batch serum samples.

Metabolomics research on potential role for 9-cis-retinoic acid in breast cancer progression

Deciphering the molecular networks that discriminate organ-confined breast cancer from metastatic breast cancer may lead to the identification of critical biomarkers for breast cancer invasion and aggressiveness. Here metabolomics, a global study of metabolites, has been applied to explore the metabolic alterations that characterize breast cancer progression. We profiled a total of 693 metabolites across 87 serum samples related to breast cancer (46 clinically localized and 41 metastatic breast cancer) and 49 normal samples. These unbiased metabolomic profiles were able to distinguish normal individuals, clinically localized and metastatic breast cancer patients. 9-cis-Retinoic acid, an isomer of all-trans retinoic acid, was identified as a differential metabolite that significantly decreased during breast cancer progression to metastasis, and its levels were also reduced in urine samples from biopsy-positive breast cancer patients relative to biopsy-negative individuals and in invasive breast cancer cells relative to benign MCF-10A cells. The addition of exogenous 9-cis-retinoic acid to MDA-MB-231 cells and knockdown of aldehyde dehydrogenase 1 family member A1, a regulatory enzyme for 9-cis-retinoic acid, remarkably impaired cell invasion and migration, presumably through preventing the key regulator cofilin from activation and inhibiting MMP2 and MMP9 expression. Taken together, our study showed the potential inhibitory role for 9-cis-retinoic acid in breast cancer progression by attenuating cell invasion and migration.

A metabolic mechanism analysis of Fuzheng-Huayu formula for improving liver cirrhosis with traditional Chinese medicine syndromes

Fuzheng-Huayu formula (FZHY), a Chinese herbal mixture prescription, has been proven effective in treating liver fibrosis and cirrhosis in both clinical trials and animal experiments. In this study we assessed the metabolic mechanisms of traditional Chinese medicine (TCM) syndrome-based FZHY treatment in liver cirrhosis (LC). A total of 113 participants, including 50 healthy controls and 63 LC patients, were recruited. According to the diagnosis and differentiation of the TCM syndromes, the LC patients were classified into 5 TCM syndrome groups including the liver stagnation syndrome (LSS), spleen deficiency and damp overabundance syndrome (SDDOS), damp-heat accumulation syndrome (DHAS), liver-kidney Yin deficiency syndrome (LKYDS), and blood stagnation syndrome (BSS), and administered FZHY for 6 months. FZHY treatment significantly decreased serum levels of hyaluronic acid (HA), a biochemical marker for LC, as well as TCM syndrome scores (the TCM syndrome scores were decreased in all the groups with significant decreases in the LSS and LKYDS groups). Furthermore, FZHY treatment gradually shifted the metabolic profiles of LC patients from a pathologic state to a healthy state, especially in LC patients with LSS and LKYDS. Twenty-two differently altered metabolites (DAMs) were identified, including carbohydrates, amino acids, fatty acids, etc with 9 DAMs in LSS patients, 9 in LKYDS patients, and 4 in other patients. The metabolic pathways involved in the conversion of amino acids and the body's detoxification process were regulated first, followed by the pathways involved in the body's energy supply process. In conclusion, the evaluation of the effect of TCM syndrome-based FZHY treatment show that FZHY has a better effect on LKYDS and LSS than on the other TCM syndromes, and the metabolic mechanisms might be involved in the increased detoxification function in LKYDS and the improvement of energy supply in LSS, which provides important evidence for the clinical application of TCM syndrome-based treatment.

Deciphering hepatocellular carcinoma through metabolomics: from biomarker discovery to therapy evaluation

Hepatocellular carcinoma (HCC) is the third most common cause of death from cancer, with increasing prevalence worldwide. The mortality rate of HCC is similar to its incidence rate, which reflects its poor prognosis. At present, the diagnosis of HCC is still mostly dependent on invasive biopsy, imaging methods, and serum α-fetoprotein (AFP) testing. Because of the asymptomatic nature of early HCC, biopsy and imaging methods usually detect HCC at the middle-late stages. AFP has limited sensitivity and specificity, as many other nonmalignant liver diseases can also result in a very high serum level of AFP. Therefore, better biomarkers with higher sensitivity and specificity at earlier stages are greatly needed. Since metabolic reprogramming is an essential hallmark of cancer and the liver is the metabolic hub of living systems, it is useful to investigate HCC from a metabolic perspective. As a noninvasive and nondestructive approach, metabolomics provides holistic information on dynamically metabolic responses of living systems to both endogenous and exogenous factors. Therefore, it would be conducive to apply metabolomics in investigating HCC. In this review, we summarize recent metabolomic studies on HCC cellular, animal, and clinicopathologic models with attention to metabolomics as a biomarker in cancer diagnosis. Recent applications of metabolomics with respect to therapeutic and prognostic evaluation of HCC are also covered, with emphasis on the potential of treatment by drugs from natural products. In the last section, the current challenges and trends of future development of metabolomics on HCC are discussed. Overall, metabolomics provides us with novel insight into the diagnosis, prognosis, and therapeutic evaluation of HCC.

Metabolomics: From liver chiromancy to personalized precision medicine in advanced chronic liver disease

Currently there is a lack of accurate biomarkers for diagnosis and prognosis in advanced liver diseases. Either the occurrence of first decompensation, or diagnosis of acute on chronic liver failure, severe alcoholic hepatitis, or hepatocellular carcinoma (HCC), none of the available biomarkers are satisfactory. Metabolomics is the newest of omics, being much closer than the others to the actual phenotype and pathologic changes that characterizes a certain condition. It deals with a much wider spectrum of low molecular weight bio-compounds providing a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication and therapy. Until now metabolomics was applied in a wide spectrum of liver conditions, but the findings were contradictory. This review proposes a synthesis of the existing evidences of metabolomics use in advanced chronic liver diseases, decompensated liver cirrhosis, severe alcoholic hepatitis and HCC.

Small molecule metabolite biomarkers for hepatocellular carcinoma with bile duct tumor thrombus diagnosis

Hepatocellular carcinoma with bile duct tumor thrombus (BDTT) is a malignant disease. The most commonly used diagnosis methods for BDTT are MRCP/ERCP, ultrasonic diagnosis or CT scan. However, BDTT is often misdiagnosed as other bile duct diseases, such as extrahepatic cholangiocarcinoma (EHCC), choledochal cyst (Cyst) and common bile duct stone (Stone). Diagnostic methods, which are more accurate and less destructive, are urgently needed. In this paper, we analyzed the small molecule metabolites in the serum of BDTT, Stone, Cyst and EHCC patients and normal people using untargeted GC-MS, and identified 21 metabolites that show different levels among different samples. Using targeted UHPLC-QQQ-MS analysis, we found that several metabolites are significantly changed. ROC curve analysis revealed two metabolites, L-citrulline and D-aspartic acid, as potential biomarkers that can distinguish BDTT from other bile duct diseases.

Bile acid-microbiota crosstalk in gastrointestinal inflammation and carcinogenesis

Emerging evidence points to a strong association between the gut microbiota and the risk, development and progression of gastrointestinal cancers such as colorectal cancer (CRC) and hepatocellular carcinoma (HCC). Bile acids, produced in the liver, are metabolized by enzymes derived from intestinal bacteria and are critically important for maintaining a healthy gut microbiota, balanced lipid and carbohydrate metabolism, insulin sensitivity and innate immunity. Given the complexity of bile acid signalling and the direct biochemical interactions between the gut microbiota and the host, a systems biology perspective is required to understand the liver-bile acid-microbiota axis and its role in gastrointestinal carcinogenesis to reverse the microbiota-mediated alterations in bile acid metabolism that occur in disease states. An examination of recent research progress in this area is urgently needed. In this Review, we discuss the mechanistic links between bile acids and gastrointestinal carcinogenesis in CRC and HCC, which involve two major bile acid-sensing receptors, farnesoid X receptor (FXR) and G protein-coupled bile acid receptor 1 (TGR5). We also highlight the strategies and cutting-edge technologies to target gut-microbiota-dependent alterations in bile acid metabolism in the context of cancer therapy.

A Large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third most lethal cancer worldwide. The lack of effective biomarkers for the early detection of HCC results in unsatisfactory curative treatments. Here, metabolite biomarkers were identified and validated for HCC diagnosis. A total of 1,448 subjects, including healthy controls and patients with chronic hepatitis B virus infection, liver cirrhosis, and HCC, were recruited from multiple centers in China. Liquid chromatography-mass spectrometry-based metabolomics methods were used to characterize the subjects' serum metabolic profiles and to screen and validate the HCC biomarkers. A serum metabolite biomarker panel including phenylalanyl-tryptophan and glycocholate was defined. This panel had a higher diagnostic performance than did α-fetoprotein (AFP) in differentiating HCC from a high-risk population of cirrhosis, such as an area under the receiver-operating characteristic curve of 0.930, 0.892, and 0.807 for the panel versus 0.657, 0.725, and 0.650 for AFP in the discovery set, test set, and cohort 1 of the validation set, respectively. In the nested case-control study, this panel had high sensitivity (range 80.0%-70.3%) to detect preclinical HCC, and its combination with AFP provided better risk prediction of preclinical HCC before clinical diagnosis. Besides, this panel showed a larger area under the receiver-operating characteristic curve than did AFP (0.866 versus 0.682) to distinguish small HCC, and 80.6% of the AFP false-negative patients with HCC were correctly diagnosed using this panel in the test set, which was corroborated by the validation set. The specificity and biological relevance of the identified biomarkers were further evaluated using sera from another two cancers and HCC tissue specimens, respectively. Conclusion: The discovered and validated serum metabolite biomarker panel exhibits good diagnostic performance for the early detection of HCC from at-risk populations. (Hepatology 2018;67:662-675).

Plasma metabolic profiling analysis of Cortex Periplocae-induced cardiotoxicity based on UPLC/Q-TOF-MS

Cortex Periplocae is a well-known form of traditional medicine with its unique cardiotonic action, anti-tumor activity and immune regulation effect. However, improper use of Cortex Periplocae often leads to cardiac toxicity, which in the most severe cases can even be life-threatening. Biochemical tests and histopathological examinations are primary methods for clinical trials. However, such approaches are time-consuming, lack specificity and have low sensitivity, which can easily lead to negative results in studies. Therefore, a more scientific and systematic evaluation of Cortex Periplocae cardiotoxicity is particularly important. In this study, we established a method that combines metabonomics with trend analysis of a gavage concentration series to find cardiac toxicity biomarkers of Cortex Periplocae. We created rat cardiotoxicity models, in which the toxicity was caused by Cortex Periplocae. We collected data from rat plasma samples based on metabonomics using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS). Multiple statistical analyses, such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), were used to examine metabolite profile changes in plasma samples to screen potential cardiotoxicity biomarkers and metabolic pathways. Compared with the control group, after 7 days administration, the pathological sections showed cardiac toxicity. Moreover, some metabolites in the body changed significantly. Receiver operating characteristic curve (ROC) analysis showed that there are 11 metabolites related with cardiac toxicity, which play a role in "phenylalanine, tyrosine and tryptophan biosynthesis"; "phenylalanine metabolism"; "valine, leucine and isoleucine biosynthesis"; "glycerophospholipid metabolism" as well as "pantothenate and CoA biosynthesis". These metabolites can better explain the cardiotoxicity mechanism of Cortex Periplocae and provide a scientific and systematic method to evaluate the cardiotoxicity of Cortex Periplocae.

Determination of candidate metabolite biomarkers associated with recurrence of HCV-related hepatocellular carcinoma

Hepatitis C virus (HCV) infection is associated with a high risk of developing hepatocellular carcinoma (HCC) and HCC recurrence remains the primary threat to outcomes after curative therapy. In this study, we compared recurrent and non-recurrent HCC patients treated with radiofrequency ablation (RFA) in order to identify characteristic metabolic profile variations associated with HCC recurrence. Gas chromatography-mass spectrometry (GC-MS) -based metabolomic analyses were conducted on serum samples obtained before and after RFA therapy. Significant variations were observed in metabolites in the glycerolipid, tricarboxylic acid (TCA) cycle, fatty acid, and amino acid pathways between recurrent and non-recurrent patients. Observed differences in metabolites associated with recurrence did not coincide before and after treatment except for fatty acids. Based on the comparison of serum metabolomes between recurrent and non-recurrent patients, key discriminatory metabolites were defined by a random forest (RF) test. Two combinations of these metabolites before and after RFA treatment showed outstanding performance in predicting HCV-related HCC recurrence, they were further confirmed by an external validation set. Our study showed that the determined combination of metabolites may be potential biomarkers for the prediction of HCC recurrence before and after RFA treatment.

The effects of Curcumin on HCT-116 cells proliferation and apoptosis via the miR-491/PEG10 pathway

Paternally expressed gene-10 (PEG10) could participate in several carcinomas and might be regulated by miR-491. To now, miR-491 was found to play an important role in the sensitivity and mechanism of drug usage in the treatment of colorectal cancer, and drug resistance is a key factor to affect the disease healing. In this study, miR-491, PEG10, Wnt1, and β-catenin expression levels and their correlation with colorectal cancer were assessed in cancer tissues and adjacent parts. And the target relationship between PEG10 and miR-491 was verified. Meanwhile, the impaction of Curcumin on miR-491, PEG10, and Wnt/β-catenin signaling pathway were analyzed in HCT-116 cells. The effects of PEG10 and Curcumin on human HCT-116 cells proliferation and apoptosis were investigated by MTT and flow cytometry assay. Results showed that the expression of miR-491 in colon cancer tissues was decreased, but PEG10, Wnt1, and β-catenin were higher than that in adjacent tissues. The PEG10 gene 3' UTR could combine with miR-491 seed sequence and miR-491 overexpression could cause a decrease in PEG10, Wnt1, and β-catenin levels in human HCT-116 cells. Furthermore, PEG10 overexpression increased the expression levels of Wnt1 and β-catenin, thereby promoting cell proliferation and inhibiting apoptosis. In addition, Curcumin could up-regulate miR-491, inhibit PEG10, and Wnt/β-catenin signaling pathway. Consequently, Curcumin reduced HCT-116 cells proliferation and promoted cells apoptosis via the miR-491/PEG10 pathway. In conclusion, PEG10 was a target gene of miR-491, miR-491/PEG10 strengthen the sensitivity of Curcumin in HCT-116 cells proliferation and apoptosis, which might act as an ideal diagnostic biomarker treatment methods.

Biomarker development for hepatocellular carcinoma early detection: current and future perspectives

Early detection of hepatocellular carcinoma (HCC) leads to improved survival; however, current early detection strategies for HCC surveillance are ineffective. Thus, there has been interest in developing biomarkers to aid in the early detection HCC. In this review, we discuss the five phases of biomarker discovery that are necessary for clinical implementation. We also describe the most promising investigational biomarkers and their phase of discovery. We review several promising technologies for the early detection of HCC, including miRNA, metabolomics and proteomics. Promisingly, there are samples from multiple longitudinal cohorts of patients with cirrhosis in the USA that are being collected in order to validate candidate biomarkers for HCC. A biomarker-based strategy has the potential to become the primary surveillance method for HCC detection.

Long-term Administration of Nuclear Bile Acid Receptor FXR Agonist Prevents Spontaneous Hepatocarcinogenesis in Abcb4-/- Mice

Altered bile acid (BA) signaling is associated with hepatotoxicity. The farnesoid X receptor (FXR) is a nuclear receptor that transcriptionally regulates BA homeostasis. Mice with FXR ablation present hepatocarcinoma (HCC) due to high toxic BA levels. Mice with Abcb4 ablation accumulate toxic BA within the bile ducts and present HCC. We have previously shown that intestinal specific activation of FXR by transgenic VP16-FXR chimera is able to reduce BA pool size and prevent HCC. Here we tested chemical FXR activation by administering for 15 months the dual FXR/ membrane G protein-coupled receptor (TGR5) agonist INT-767 (6α-ethyl-3α,7α,23-trihydroxy-24-nor-5β-cholan-23-sulphate) to Fxr-/- and Abcb4-/- mice. HCC number and size were significantly reduced by INT-767 administration. In contrast, no changes in HCC tumor number and size were observed in Fxr-/- mice fed with or without INT-767. Notably, INT-767 preserved the hepatic parenchyma, improved hepatic function and down-regulated pro-inflammatory cytokines. Moreover, in Abcb4-/- mice INT-767 prevented fibrosis by reducing collagen expression and deposition. Thus, long term activation of FXR is able to reduce BA pool, reprogram BA metabolism and prevent HCC. These data provide the impetus to address the bona fide therapeutic potential of FXR activation in disease with BA-associated development of HCC.

Amine Metabolism Is Influenced by Dietary Protein Source

Growth in world population will inevitably leads to increased demand for protein for humans and animals. Protein from insects and blood plasma are being considered as possible alternatives, but more research on their nutritional quality and health effects is needed. Here, we studied the effect of dietary protein source on metabolism and metabolic amine profiles in serum and urine of mice. Groups of mice were fed semi-purified diets containing 300 g/kg of soybean meal, casein, partially delactosed whey powder, spray-dried plasma protein, wheat gluten meal, and yellow mealworm. Feed and water intake as well as body weight gain were measured for 28 days. After 14 and 28 days, serum and urine samples were collected for measurement of a large panel of amine metabolites. MetaboAnalyst 3.0 was used for analysis of the raw metabolic data. Out of 68 targeted amine metabolites, we could detect 54 in urine and 41 in blood serum. Dietary protein sources were found to have profound effects on host metabolism, particularly in systemic amine profiles, considered here as an endophenotype. We recommend serum over urine to screen for the amine metabolic endophenotype based on partial least squares discriminant analysis. We concluded that metabolites like alpha-aminobutyric acid and 1-methylhistidine are sensitive indicators of too much or too little availability of specific amino acids in the different protein diets. Furthermore, we concluded that amine metabolic profiles can be useful for assessing the nutritional quality of different protein sources.

Metabolic Biomarkers for Prognostic Prediction of Pre-diabetes: results from a longitudinal cohort study

To investigate the metabolic biomarkers of predicting the transition from pre-diabetes (pre-DM) to normal glucose regulation (NGR) and diabetes (DM) in a longitudinal cohort study. 108 participants with pre-DM were followed up for ten years and divided into 3 groups according to different glycemic outcomes. 20 participants progressed to DM, 20 regressed to NGR, and 68 remained at pre-DM. Alterations in plasma metabolites in these groups were evaluated by untargeted ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). Twenty three metabolites related to glycerophospholipid metabolism, oxidation and antioxidation were associated with the process from pre-DM to NGR, while twenty two metabolites related to amino acid metabolism, glycerophospholipid metabolism and mitochondrial β-oxidation played important roles in the progression to DM. Results from stepwise logistic regression analysis showed that five biomarkers (20-Hydroxy-leukotriene E4, Lysopc(20:4), 5-methoxytryptamine, Endomorphin-1, Lysopc(20:3)) were good prediction for the restoration to NGR, and five biomarkers (Iso-valeraldehyde, linoleic acid, Lysopc(18:1), 2-Pyrroloylglycine, Dityrosine) for the development of DM. The findings suggest that the combination of these potential metabolites may be used for the prognosis of pre-DM. Targeting the pathways that involved in these prognostic biomarkers would be beneficial for the regression to NGR and the early prevention of DM among pre-DM.

Rats' urinary metabolomes reveal the potential roles of functional foods and exercise in obesity management

The complexity of the metabolic changes in obese individuals still presents a challenge for the understanding of obesity-related metabolic disruptions and for obesity management. In this study, a gas chromatography mass spectrometry (GC-MS) based metabolomics approach targeting urine metabolism has been applied to assess the potential roles of functional foods and exercise for obesity management in rats. Male albino rats diagnosed as obese via histopathology and biochemical assays were administered functional foods in common use for obesity management including pomegranate, grapefruit, and red cabbage juice extracts in parallel with swimming exercise. Urine samples were collected from these rats, and likewise from healthy control animals, for metabolite analysis using (GC-MS) coupled to multivariate data analysis. The results revealed a significant elevation in oxalate and phosphate levels in obese rat urine concurrent with lower lactate levels as compared to the control group. Furthermore, and to pinpoint the bioactive agents in the administered functional foods, ultra performance liquid chromatography (UPLC) coupled to high resolution time-of-flight mass spectrometry (TOF-MS) was employed for secondary metabolite profiling. The different phenolic classes found in the examined functional foods, viz. ellagitannins in pomegranate, flavanones in grapefruit and flavonols in red cabbage, are likely to mediate their anti-obesity effects. The results indicate that these functional foods and exercise were quite effective in reverting obesity-related metabolic disruptions back to normal status, as revealed by orthogonal partial least squares-discriminant analysis (OPLS-DA).

A Computational Selection of Metabolite Biomarkers Using Emerging Pattern Mining: A Case Study in Human Hepatocellular Carcinoma

The biomarker development in metabolomics aims at discriminating diseased from normal subjects and at creating a predictive model that can be used to diagnose new subjects. From a case study on human hepatocellular carcinoma (HCC), we studied for the first time the potential usefulness of the emerging patterns (EPs) that come from the data mining domain. When applied to a metabolomics data set labeled with two classes (e.g., HCC patients vs healthy subjects), EP mining can capture differentiating combinations of metabolites between the two classes. We observed that the so-called jumping emerging patterns (JEPs), which correspond to the combinations of metabolites that occur in only one of the two classes, achieved better performance than individual biomarkers. Particularly, the implementation of the JEPs in a rules-based diagnostic tool drastically reduced the false positive rate, i.e., the rate of healthy subjects predicted as HCC patients.

Unique and Novel Urinary Metabolomic Features in Malignant versus Benign Adrenal Neoplasms

Purpose: Adrenal incidentalomas must be differentiated from adrenocortical cancer (ACC). Currently, size, growth, and imaging characteristics determine the potential for malignancy but are imperfect. The aim was to evaluate whether urinary small molecules (<800 Da) are associated with ACC.Experimental Design: Preoperative fasting urine specimens from patients with ACC (n = 19) and benign adrenal tumors (n = 46) were analyzed by unbiased ultraperformance liquid chromatography/mass spectrometry. Creatinine-normalized features were analyzed by Progenesis, SIMCA, and unpaired t test adjusted by FDR. Features with an AUC >0.8 were identified through fragmentation patterns and database searches. All lead features were assessed in an independent set from patients with ACC (n = 11) and benign adrenal tumors (n = 46) and in a subset of tissue samples from patients with ACC (n = 15) and benign adrenal tumors (n = 15) in the training set.Results: Sixty-nine features were discovered and four known metabolites identified. Urinary creatine riboside was elevated 2.1-fold (P = 0.0001) in patients with ACC. L-tryptophan, Nε,Nε,Nε-trimethyl-L-lysine, and 3-methylhistidine were lower 0.33-fold (P < 0.0001), 0.56-fold (P < 0.0001), and 0.33-fold (P = 0.0003) in patients with ACC, respectively. Combined multivariate analysis of the four biomarkers showed an AUC of 0.89 [sensitivity 94.7% (confidence interval {CI}, 73.9%-99.1%), specificity 82.6% (CI, 68.6%-92.2%), PPV 69.2% (CI, 48.2%-85.6%), and NPV 97.4% (CI, 86.5%-99.6%)] for distinguishing ACC from benign tumors. Of the four, creatine riboside and four unknown features were validated. Creatine riboside, Nε,Nε,Nε-trimethyl-L-lysine, and two unknown features were elevated in ACC tumors.Conclusions: There are unique urinary metabolic features in patients with ACC with some metabolites present in patient tumor samples. Urinary creatine riboside can differentiate benign adrenal neoplasms from ACC. Clin Cancer Res; 23(17); 5302-10. ©2017 AACR.

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.

Serum lipid alterations identified in chronic hepatitis B, hepatitis B virus-associated cirrhosis and carcinoma patients

The incidences of chronic hepatitis B (CHB), Hepatitis B virus (HBV)-associated cirrhosis and HBV-associated carcinoma are high and increasing. This study was designed to evaluate serum lipid metabolite changes that are associated with the progression from CHB to HBV-associated cirrhosis and ultimately to HBV-associated HCC. A targeted metabolomic assay was performed in fasting sera from 136 CHB patients, 104 HBV-associated cirrhosis, and 95 HBV-associated HCC using ultra-performance liquid chromatography triple quadrupole mass spectrometry. A total of 140 metabolites were identified. Clear separations between each two groups were obtained using the partial least squares discriminate analysis of 9 lipid metabolites. Progressively lower levels of long-chain lysophosphatidylcholines (lysoPC a C18:2, lysoPC a C20:3, lysoPC a C20:4) were observed from CHB to cirrhosis to carcinoma; lower levels of lysoPC a C20:4 were found in patients with higher model for end-stage liver disease in the same disease group; and lysoPC a C20:3 levels were lower in Child-Pugh Class C than in Class A and Class B in HBV-associated cirrhosis and HBV-associated HCC groups. The octadecadienyl carnitine level was higher in HBV-associated cirrhosis group than in other two groups. Serum levels of selected long-chain lysoPCs are promising markers for the progression of HBV-associated liver diseases.

Human plasma metabolomics for identifying differential metabolites and predicting molecular subtypes of breast cancer

PURPOSE

This work aims to identify differential metabolites and predicting molecular subtypes of breast cancer (BC).

METHODS

Plasma samples were collected from 96 BC patients and 79 normal participants. Metabolic profiles were determined by liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry based on multivariate statistical data analysis.

RESULTS

We observed 64 differential metabolites between BC and normal group. Compared to human epidermal growth factor receptor 2 (HER2)-negative patients, HER2-positive group showed elevated aerobic glycolysis, gluconeogenesis, and increased fatty acid biosynthesis with reduced Krebs cycle. Compared with estrogen receptor (ER)-negative group, ER-positive patients showed elevated alanine, aspartate and glutamate metabolism, decreased glycerolipid catabolism, and enhanced purine metabolism. A panel of 8 differential metabolites, including carnitine, lysophosphatidylcholine (20:4), proline, alanine, lysophosphatidylcholine (16:1), glycochenodeoxycholic acid, valine, and 2-octenedioic acid, was identified for the classification of BC subtypes. These markers showed potential diagnostic value with average area under the curve at 0.925 (95% CI 0.867-0.983) for the training set (n=51) and 0.893 (95% CI 0.847-0.939) for the test set (n=45).

CONCLUSION

Human plasma metabolomics is useful in identifying differential metabolites and predicting breast cancer subtypes.

Metabonomics Research Progress on Liver Diseases

Metabolomics as the new omics technique develops after genomics, transcriptomics, and proteomics and has rapid development at present. Liver diseases are worldwide public health problems. In China, chronic hepatitis B and its secondary diseases are the common liver diseases. They can be diagnosed by the combination of history, virology, liver function, and medical imaging. However, some patients seldom have relevant physical examination, so the diagnosis may be delayed. Many other liver diseases, such as drug-induced liver injury (DILI), alcoholic liver disease (ALD) and nonalcoholic fatty liver disease (NAFLD), and autoimmune liver diseases, still do not have definite diagnostic markers; the diagnosis consists of history, medical imaging, and the relevant score. As a result, the clinical work becomes very complex. So it has broad prospects to explore the specific and sensitive biomarkers of liver diseases with metabolomics. In this paper, there are several summaries which are related to the current research progress and application of metabolomics on biomarkers of liver diseases.

Chromatographic determination of some biomarkers of liver cirrhosis and hepatocellular carcinoma in Egyptian patients

Metabolomics has been shown to be an effective tool for disease diagnosis, biomarker screening and characterization of biological pathways. A total of 140 subjects were included in this study; urine metabolomes of patients with liver cirrhosis (LC, n = 40), patients with hepatocellular carcinoma (HCC; n = 55) and healthy male subjects (n = 45) as a control group were studied. Gas chromatography/mass spectrometry-based urine metabolomics profiles were investigated for all participants. Diagnostic models were constructed with a combination of marker metabolites, using principal components analysis and receiver operator characteristic curves. A total of 57 peaks could be auto-identified of which 13 marker metabolites (glycine, serine, threonine, proline, urea, phosphate, pyrimidine, arabinose, xylitol, hippuric acid, citric acid, xylonic acid and glycerol) were responsible for the separation of HCC group from healthy subjects. Also, eight markers metabolites (glycine, serine, threonine, proline, citric acid, urea, xylitol and arabinose) showed significant differences between the LC group and healthy subjects. No significant difference was detected between HCC and LC groups regarding all these metabolites. Metabolomic profile using GC-MS established an optimized diagnostic model to discriminate between HCC patients and healthy subjects; also it could be useful for diagnosis of LC patients. However, it failed to differentiate between HCC and LC patients.