Human liver tissue metabolic profiling research on hepatitis B virus-related hepatocellular carcinoma

Microbiota and metabolite alterations in pancreatic head and body/tail cancer patients

Pancreatic head cancer (PHC) and pancreatic body/tail cancer (PBTC) have distinct clinical and biological behaviors. The microbial and metabolic differences in PHC and PBTC have not been studied. The pancreatic microbiota and metabolome of 15 PHC and 8 PBTC tissues and their matched nontumor tissues were characterized using 16S rRNA amplicon sequencing and untargeted metabolomics. At the genus level, Bradyrhizobium was increased while Corynebacterium and Ruminococcus were decreased in the PHC tissues (Head T) compared with the matched nontumor tissues (Head N) significantly. Shuttleworthia, Bacillus, and Bifidobacterium were significantly decreased in the PBTC tissues (Body/Tail T) compared with the matched nontumor tissues (Body/Tail N). Significantly, Ileibacterium was increased whereas Pseudoxanthomonas was decreased in Head T and Body/Tail T, and Lactobacillus was increased in Head T but decreased in Body/Tail T. A total of 102 discriminative metabolites were identified between Head T and Head N, which were scattered through linoleic acid metabolism and purine metabolism pathways. However, there were only four discriminative metabolites between Body/Tail T and Body/Tail N, which were related to glycerophospholipid metabolism and autophagy pathways. The differential metabolites in PHC and PBTC were commonly enriched in alpha-linolenic acid metabolism and choline metabolism in cancer pathways. Eubacterium decreased in Head T was positively correlated with decreased linoleic acid while negatively correlated with increased arachidyl carnitine and stearoylcarnitine. Bacillus decreased in Body/Tail T was negatively correlated with increased L-carnitine. These microbiota and metabolites deserve further investigations to reveal their roles in the pathogenesis of PHC and PBTC, providing clues for future treatments.

Multiomics characterization of fatty acid metabolism for the clinical management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignancy and there is a lack of effective biomarkers for HCC diagnosis. Living organisms are complex, and different omics molecules interact with each other to implement various biological functions. Genomics and metabolomics, which are the top and bottom of systems biology, play an important role in HCC clinical management. Fatty acid metabolism is associated with malignancy, prognosis, and immune phenotype in cancer, which is a potential hallmark in malignant tumors. In this study, the genes and metabolites related to fatty acid metabolism were thoroughly investigated by a dynamic network construction algorithm named EWS-DDA for the early diagnosis and prognosis of HCC. Three gene ratios and eight metabolite ratios were identified by EWS-DDA as potential biomarkers for HCC clinical management. Further analysis using biological analysis, statistical analysis and document validation in the discovery and validation sets suggested that the selected potential biomarkers had great clinical prognostic value and helped to achieve effective early diagnosis of HCC. Experimental results suggested that in-depth evaluation of fatty acid metabolism from different omics viewpoints can facilitate the further understanding of pathological alterations associated with HCC characteristics, improving the performance of early diagnosis and clinical prognosis.

Comparison of surface-enhanced Raman spectral data sets of filtrate portions of serum samples of hepatitis B and Hepatitis C infected patients obtained by centrifugal filtration

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is an efficient technique which has been used for the analysis of filtrate portions of serum samples of Hepatitis B (HBV) and Hepatitis C (HCV) virus.

OBJECTIVES

The main reason for this study is to differentiate and compare HBV and HCV serum samples for disease diagnosis through SERS. Hepatitis B and hepatitis C disease biomarkers are more predictable in their centrifuged form as compared in their uncentrifuged form. For differentiation of SERS spectral data sets of hepatitis B, hepatitis C and healthy person principal component analysis (PCA) proved to be a helpful. Centrifugally filtered serum samples of hepatitis B and hepatitis C are clearly differentiated from centrifugally filtered serum samples of healthy individuals by using partial least square discriminant analysis (PLS-DA).

METHODOLOGY

Serum sample of HBV, HCV and healthy patients were centrifugally filtered to separate filtrate portion for studying biochemical changes in serum sample. The SERS of these samples is performed using silver nanoparticles as substrates to identify specific spectral features of both viral diseases which can be used for the diagnosis and differentiation of these diseases. The purpose of centrifugal filtration of the serum samples of HBV and HCV positive and control samples by using filter membranes of 50 KDa size is to eliminate the proteins bigger than 50 KDa so that their contribution in the SERS spectrum is removed and disease related smaller proteins may be observed. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are statistical tools which were used for the further validation of SERS.

RESULTS

HBV and HCV centrifugally filtered serum sample were compared and biomarkers including (uracil, phenylalanine, methionine, adenine, phosphodiester, proline, tyrosine, tryptophan, amino acid, thymine, fatty acid, nucleic acid, triglyceride, guanine and hydroxyproline) were identified through PCA and PLS-DA. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used as a multivariate data analysis tool for the diagnosis of the characteristic SERS spectral features associated with both types of viral diseases. For the classification and differentiation of centrifugally filtered HBV, HCV, and control serum samples, Principal component analysis is found helpful. Moreover, PLS-DA can classify these two distinct sets of SERS spectral data with 0.90 percent specificity, 0.85 percent precision, and 0.83 percent accuracy.

CONCLUSIONS

Surface enhanced Raman spectroscopy along with chemometric analysis like PCA and PLS-DA have been successfully differentiated HBV and HCV and healthy individuals' serum samples.

Optimized Systematic Review Tool: Application to Candidate Biomarkers for the Diagnosis of Hepatocellular Carcinoma

This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analyzed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, precirrhotic liver disease, cirrhosis) were eligible. Tumor tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke "risk of bias" tool for metabolomic studies was developed adjusting for analytic quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine, and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines, and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardization of reporting of metabolomic data. Validation studies, standardized study designs, and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.

Surface-enhanced Raman spectral analysis for comparison of PCR products of hepatitis B and hepatitis C

BACKGROUND

Surface-enhanced Raman spectroscopy is a reliable tool for identification and differentiation of two diseases showing similar symptoms, hepatitis B (HBV) and hepatitis C (HCV).

OBJECTIVES

To develop a polymerase chain reaction technique (PCR) based SERS technique for differentiation of two human pathological conditions sharing the same symptoms using multivariate data analysis techniques e.g. principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA).

METHODS

PCR products of HBV and HCV were differentiated by SERS using silver nanoparticles (AgNPs) as a SERS substrate. For this analysis, PCR products of both the diseases with predetermined viral loads were collected and analyzed under SERS instrument and unique SERS spectra of HBV and HCV was compared showing many differences at various points. Diseased classes of HBV and HCV and their negative control classes (viral load less than 1) were compared. PCR products of true healthy DNA and RNA were also compared, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe.

RESULTS

SERS spectral data of HBV and HCV showed clear differences and were significantly separated using PCA. Negative control samples of both disorders and their true healthy samples of DNA and RNA were separated according to 1^st principle component. By analyzing data using partial least square discriminate analysis, differentiation of two disease classes was considered more valid with sensitivity, specificity and accuracy value of 96%, 94% and 98% respectively. Value of area under curve (AUROC) was 0.7527.

CONCLUSION

SERS can be employed for identification and comparison of two human pathological conditions sharing the same symptomology.

SERS-based viral load quantification of hepatitis B virus from PCR products

Hepatitis B is a contagious liver disorder caused by hepatitis B virus and if not treated at an early stage, it becomes chronic and results in liver cirrhosis and hepatocellular carcinoma which can even lead to death. In present study, surface-enhanced Raman spectroscopy (SERS) is employed for the analysis of polymerase chain reaction (PCR) products of DNA extracted from hepatitis B virus (HBV) infected patients in comparison with healthy individuals. SERS spectral features are identified which are solely present in the HBV positive samples and consistently increase in intensities with increase in viral load which can be considered as a SERS spectral marker for HBV infection. For sake of understanding, these various levels of viral loads in this study are classified as low (1-1000 IU), medium (1000-10,000 IU), high (above 10,000 IU) and negative control (>1). In order to explore the efficiency of SERS for discrimination of SERS spectral datasets of different samples of varying viral loads and healthy individuals, principal component analysis (PCA) is applied. PCA is used for comparison of these classes including low, medium and high levels of viral loads with each other and with healthy class. Moreover, partial least square discriminant analysis and partial least square regression analysis are employed for the classification of different levels of viral loads in the HBV positive samples and prediction of viral loads in the unknown samples, respectively. PLS-DA is applied for validity of classification and its sensitivity and specificity was found to be 89% and 98% respectively. PLSR model was constructed for prediction of viral loads on the bases of SERS spectral markers of HBV infection with goodness value of 0.9031 and value of root means square error (RMSE) 0.2923. PLSR model also proved to be valid for prediction of blind sample.

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.

Tissue and serum metabolomic phenotyping for diagnosis and prognosis of hepatocellular carcinoma

More than two-thirds of patients with hepatocellular carcinoma (HCC) cannot receive curative therapy and have poor survival due to late diagnosis and few prognostic directions. In our study, nontargeted and targeted metabolomics analyses were conducted by liquid chromatography-mass spectrometry to characterize metabolic features of HCC and identify diagnostic and prognostic biomarker candidate incorporating liver tissue and serum metabolites. A total of 552 subjects, including 432 with liver tissue and 120 with serum specimens, were recruited in China. In the discovery cohort, a series of 138 metabolites were identified to discriminate HCC tissues from matched nontumor tissues. Retinol presented with the highest area under the curve (AUC) of 0.991 and associated with Edmondson grade. In the validation cohort, all metabolites in retinol metabolism pathway were examined and the levels of retinol and retinal in tumor tissue and serum decreased in the order of normal to cirrhosis to HCC of Edmondson Grades I to IV. Retinol and retinal levels could also differentiate between HCC and cirrhosis, with AUCs of 0.996 and 0.994, respectively, in tissue and 0.812 and 0.744, respectively, in serum. The AUC of the combined retinol and retinal panel in serum was 0.852. Univariate and multivariate Cox regression identified this panel as an independent predictor for HCC and showed that low expression of retinol and retinal correlated with decreased survival time. In conclusion, the retinol metabolic signature had considerable diagnostic and prognostic value for identifying HCC patients who would benefit from prompt therapy and optimal prognostic direction.

Metabolomic Analysis of Liver Tissues for Characterization of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) causes more than half a million annual deaths worldwide. Understanding the mechanisms contributing to HCC development is highly desirable for improved surveillance, diagnosis, and treatment. Liver tissue metabolomics has the potential to reflect the physiological changes behind HCC development. Also, it allows identification of biomarker candidates for future evaluation in biofluids and investigation of racial disparities in HCC. Tumor and nontumor tissues from 40 patients were analyzed by both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) platforms to increase the metabolome coverage. The levels of the metabolites extracted from solid liver tissue of the HCC area and adjacent non-HCC area were compared. Among the analytes detected by GC-MS and LC-MS with significant alterations, 18 were selected based on biological relevance and confirmed metabolite identification. These metabolites belong to TCA cycle, glycolysis, purines, and lipid metabolism and have been previously reported in liver metabolomic studies where high correlation with HCC progression is implied. We demonstrated that metabolites related to HCC pathogenesis can be identified through liver tissue metabolomic analysis. Additionally, this study has enabled us to identify race-specific metabolites associated with HCC.

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.

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.

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.

Model-driven discovery of long-chain fatty acid metabolic reprogramming in heterogeneous prostate cancer cells

Epithelial-mesenchymal-transition promotes intra-tumoral heterogeneity, by enhancing tumor cell invasiveness and promoting drug resistance. We integrated transcriptomic data for two clonal subpopulations from a prostate cancer cell line (PC-3) into a genome-scale metabolic network model to explore their metabolic differences and potential vulnerabilities. In this dual cell model, PC-3/S cells express Epithelial-mesenchymal-transition markers and display high invasiveness and low metastatic potential, while PC-3/M cells present the opposite phenotype and higher proliferative rate. Model-driven analysis and experimental validations unveiled a marked metabolic reprogramming in long-chain fatty acids metabolism. While PC-3/M cells showed an enhanced entry of long-chain fatty acids into the mitochondria, PC-3/S cells used long-chain fatty acids as precursors of eicosanoid metabolism. We suggest that this metabolic reprogramming endows PC-3/M cells with augmented energy metabolism for fast proliferation and PC-3/S cells with increased eicosanoid production impacting angiogenesis, cell adhesion and invasion. PC-3/S metabolism also promotes the accumulation of docosahexaenoic acid, a long-chain fatty acid with antiproliferative effects. The potential therapeutic significance of our model was supported by a differential sensitivity of PC-3/M cells to etomoxir, an inhibitor of long-chain fatty acid transport to the mitochondria.

The coexistence within the same tumor of a variety of subpopulations, featuring different phenotypes (intra-tumoral heterogeneity) represents a challenge for diagnosis, prognosis and targeted therapies. In this work, we have explored the metabolic differences underlying tumor heterogeneity by building cell-type-specific genome-scale metabolic models that integrate transcriptome and metabolome data of two clonal subpopulations derived from the same prostate cancer cell line (PC-3). These subpopulations display either highly proliferative, cancer stem cell (PC-3/M) or highly invasive, epithelial-mesenchymal-transition-like phenotypes (PC-3/S). Our model-driven analysis and experimental validations have unveiled a differential utilization of the long-chain fatty acids pool in both subpopulations. More specifically, our findings show an enhanced entry of long-chain fatty acids into the mitochondria in PC-3/M cells, while in PC-3/S cells, long-chain fatty acids are used as precursors of eicosanoid metabolism. The different utilization of long-chain fatty acids between subpopulations endows PC-3/M cells with a highly proliferative phenotype while enhances PC-3/S invasive phenotype. The present work provides a tool to unveil key metabolic nodes associated with tumor heterogeneity and highlights potential subpopulation-specific targets with important therapeutic implications.

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.

Metabolomics: Strategies to Define the Role of Metabolism in Virus Infection and Pathogenesis

Metabolomics is an analytical profiling technique for measuring and comparing large numbers of metabolites present in biological samples. Combining high-throughput analytical chemistry and multivariate data analysis, metabolomics offers a window on metabolic mechanisms. Because they intimately utilize and often rewire host metabolism, viruses are an excellent choice to study by metabolomics techniques. Studies of the effects of viruses on metabolism during replication in vitro and infection in animal models or human subjects have provided novel insights into these networks and provided new targets for therapy and biomarker development. Identifying the common metabolic pathways utilized by viruses has the potential to reveal those that can be targeted by broad-spectrum antiviral and vaccine approaches.

Plasma Metabolic Profile Determination in Young ST-segment Elevation Myocardial Infarction Patients with Ischemia and Reperfusion: Ultra-performance Liquid Chromatography and Mass Spectrometry for Pathway Analysis

Background:

This study was to establish a disease differentiation model for ST-segment elevation myocardial infarction (STEMI) youth patients experiencing ischemia and reperfusion via ultra-performance liquid chromatography and mass spectrometry (UPLC/MS) platform, which searches for closely related characteristic metabolites and metabolic pathways to evaluate their predictive value in the prognosis after discharge.

Methods:

Forty-seven consecutive STEMI patients (23 patients under 45 years of age, referred to here as “youth,” and 24 “elderly” patients) and 48 healthy control group members (24 youth, 24 elderly) were registered prospectively. The youth patients were required to provide a second blood draw during a follow-up visit one year after morbidity (n = 22, one lost). Characteristic metabolites and relative metabolic pathways were screened via UPLC/MS platform base on the Kyoto encyclopedia of genes and genomes (KEGG) and Human Metabolome Database. Receiver operating characteristic (ROC) curves were drawn to evaluate the predictive value of characteristic metabolites in the prognosis after discharge.

Results:

We successfully established an orthogonal partial least squares discriminated analysis model (R₂X = 71.2%, R₂Y = 79.6%, and Q₂ = 55.9%) and screened out 24 ions; the sphingolipid metabolism pathway showed the most drastic change. The ROC curve analysis showed that ceramide [Cer(d18:0/16:0), Cer(t18:0/12:0)] and sphinganine in the sphingolipid pathway have high sensitivity and specificity on the prognosis related to major adverse cardiovascular events after youth patients were discharged. The area under curve (AUC) was 0.671, 0.750, and 0.711, respectively. A follow-up validation one year after morbidity showed corresponding AUC of 0.778, 0.833, and 0.806.

Conclusions:

By analyzing the plasma metabolism of myocardial infarction patients, we successfully established a model that can distinguish two different factors simultaneously: pathological conditions and age. Sphingolipid metabolism is the top most altered pathway in young STEMI patients and as such may represent a valuable prognostic factor and potential therapeutic target.

Nuclear Magnetic Resonance technique in tumor metabolism

Cancer is one of the most serious diseases that cause an enormous number of deaths all over the world. Tumor metabolism has great discrimination from that of normal tissues. Exploring the tumor metabolism may be one of the best ways to find biomarkers for cancer detection, diagnosis and to provide novel insights into internal physiological state where subtle changes may happen in metabolite concentrations. Nuclear Magnetic Resonance (NMR) technique nowadays is a popular tool to analyze cell extracts, tissues and biological fluids, etc, since it is a relatively fast and an accurate technique to supply abundant biochemical information at molecular levels for tumor research. In this review, approaches in tumor metabolism are discussed, including sample collection, data profiling and multivariate data analysis methods etc. Some typical applications of NMR are also summarized in tumor metabolism.

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

The most effective diagnostic tool for the majority of hepatocellular carcinoma (HCC) patients is determining the differentiation grade of their tumors. However liver biopsies, which are currently the most effective way of determining tumor differentiation grade, have several limitations. The present study was designed to select serum characteristic metabolites that correlate with the differentiation grades of hepatitis B virus (HBV)-related HCC, and so could be used in the clinic as a non-invasive method of differentiating patients with different grades of HCC. A total of 58 patients with HBV-related HCC were included in the present study, and divided into three groups according to their tumor differentiation grade. A further 20 patients with HBV-related liver cirrhosis and 19 healthy volunteers were enrolled. Ultra-performance liquid chromatography-mass spectrometry was used to analyze endogenous metabolites. Multivariate statistical analysis was used to examine the data using MZmine 2.0 software. The 14 metabolites that were highly correlated with specific differentiation grades of HCC were then selected for additional study. Receiver operator characteristic curve analysis was used to evaluate their clinical value. In total, 5 metabolites were finally identified, including lysophosphatidylcholine (16:0), oleamide, monoglyceride (0:0/15:0/0:0), lysophosphatidylcholine (18:0) and lysophosphatidylcholine [22:5(7Z,10Z,13Z,16Z,19Z)]. All these metabolites exhibited an excellent ability to distinguish different types of HCC with various differentiation grades and the area under the curve of these metabolites was up to 0.942, showing promising clinical value.

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.

Post-acquisition spectral stitching. An alternative approach for data processing in untargeted metabolomics by UHPLC-ESI(-)-HRMS

INTRODUCTION

In the case of the MS-based metabolomics, the large number of false positives remains a fundamental issue.

OBJECTIVE

The aim of this study was to develop a new strategy, which highlights the number of the reliable features i.e. the detected features that correspond to a consistent peak according to chromatographic and mass spectrometric criteria.

METHOD

For the analysis blood samples from 20 chickens, which were administrated with naringin and 9 samples from control, were analyzed by UHPLC-HRMS (Orbitrap Velos). Two methodologies have been compared for data processing. In the first one (classical approach), all data in the 100-900 m/z mass-to charge range were included for the data processing procedure whereas for the newly developed methodology, the data were shred in 100Da slices generating 8 datasets, which have been then subjected to the downstream MS data processing. Each dataset was treated separately and the m/z_t_R features obtained by either VIP's or t-test values were merged and used as the input for the construction of the general model.

RESULTS

The new methodology resulted to a 4-fold increase of the peaks that could be considered chromatographically and mass spectrometrically valid.

CONCLUSION

A new strategy was reported on the detection of chromatographically reliable features during a metabolomic approach. The shredding of the LC-MS chromatograms into multiple m/z ranges increased the number of the identified chromatographically reliable features.

Ceramide and polyunsaturated phospholipids are strongly reduced in human hepatocellular carcinoma

Lipid composition affects membrane function, cell proliferation and cell death and is changed in cancer tissues. Hepatocellular carcinoma (HCC) is an aggressive cancer and this study aimed at a comprehensive characterization of hepatic and serum lipids in human HCC. Cholesteryl ester were higher in tumorous tissues (TT) compared to adjacent non-tumorous tissues (NT). Free cholesterol exerting cytotoxic effects was not changed. Phosphatidylethanolamine, -serine (PS) and -inositol but not phosphatidylcholine (PC) and lysophosphatidylcholine (LPC) were reduced in HCC tissues. Saturated species mostly increased and polyunsaturated species were diminished in all of these phospholipids. Ceramide (Cer) was markedly reduced in HCC tissues and higher levels of sphingomyelin suggest impaired sphingomyelinase activity as one of the underlying mechanisms. Importantly, ceramide in NT increased in HCC stage T3. Ceramide released from hepatocytes attracts immune cells and a positive association of the macrophage specific receptor CD163 with NT ceramide was identified. HCC associated lipid changes did not differ in patients suffering from type 2 diabetes. Protein levels of p53 were induced in TT and negatively correlated with Cer d18:1/16:0 and PS 36:1. Of the lipid species changed in HCC tissues only TT Cer d18:1/16:0, Cer d18:1/24:1, PC 38:6 and LPC 22:6 correlated with the respective serum levels. Our study demonstrates a considerably altered hepatic lipidome in HCC tissues. Ceramide was markedly reduced in HCC tissues, and therefore, raising ceramide levels specifically in the tumor represents a reasonable therapeutic approach for the treatment of this malignancy.

Serum Metabonomics of Mild Acute Pancreatitis

BACKGROUND

Mild acute pancreatitis (MAP) is a common acute abdominal disease, and exhibits rising incidence in recent decades. As an important component of systemic biology, metabonomics is a new discipline developed following genomics and proteomics. In this study, the objective was to analyze the serum metabonomics of patients with MAP, aiming to screen metabolic markers with potential diagnostic values.

METHODS

An analysis platform with ultra performance liquid chromatography-high-resolution mass spectrometry was used to screen the difference metabolites related to MAP diagnosis and disease course monitoring.

RESULTS

A total of 432 endogenous metabolites were screened out from 122 serum samples, and 49 difference metabolites were verified, among which 12 difference metabolites were identified by nonparametric test. After material identification, eight metabolites exhibited reliable results, and their levels in MAP serum were higher than those in healthy serum. Four metabolites exhibited gradual downward trend with treatment process going on, and the differences were statistically significant (P < 0.05).

CONCLUSION

Metabonomic analysis has revealed eight metabolites with potential diagnostic values toward MAP, among which four metabolites can be used to monitor the disease course.

Metabonomic window into hepatitis B virus-related hepatic diseases

Metabonomics has recently been widely used to discover the pathogenesis and find potential metabolic markers with high sensitivity and specificity. Furthermore, it develops new diagnosis and treatment methods, increases early phase diagnosis rates of certain diseases and provides a new basis for targeted therapy. This review mainly analyzes the research progress of the metabonomics of hepatitis B virus (HBV)-related hepatic diseases, hoping to discover some potential metabolic markers for identification of HBV-related hepatic diseases from other etiologies and for HBV-related hepatitis, liver cirrhosis and hepatocellular carcinoma. This can contribute to early discovery, diagnosis and treatment, eventually increasing the survival rate of HBV-related hepatic diseases.

Optimization of parameters affecting signal intensity in an LTQ-orbitrap in negative ion mode: A design of experiments approach

A multistage optimization of all the parameters affecting detection/response in an LTQ-orbitrap analyzer was performed, using a design of experiments methodology. The signal intensity, a critical issue for mass analysis, was investigated and the optimization process was completed in three successive steps, taking into account the three main regions of an orbitrap, the ion generation, the ion transmission and the ion detection regions. Oleuropein and hydroxytyrosol were selected as the model compounds. Overall, applying this methodology the sensitivity was increased more than 24%, the resolution more than 6.5%, whereas the elapsed scan time was reduced nearly to its half. A high-resolution LTQ Orbitrap Discovery mass spectrometer was used for the determination of the analytes of interest. Thus, oleuropein and hydroxytyrosol were infused via the instruments syringe pump and they were analyzed employing electrospray ionization (ESI) in the negative high-resolution full-scan ion mode. The parameters of the three main regions of the LTQ-orbitrap were independently optimized in terms of maximum sensitivity. In this context, factorial design, response surface model and Plackett-Burman experiments were performed and analysis of variance was carried out to evaluate the validity of the statistical model and to determine the most significant parameters for signal intensity. The optimum MS conditions for each analyte were summarized and the method optimum condition was achieved by maximizing the desirability function. Our observation showed good agreement between the predicted optimum response and the responses collected at the predicted optimum conditions.

Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort

BACKGROUND

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is difficult to diagnose and has limited treatment options with a low survival rate. Aside from a few key risk factors, such as hepatitis, high alcohol consumption, smoking, obesity, and diabetes, there is incomplete etiologic understanding of the disease and little progress in identification of early risk biomarkers.

METHODS

To address these aspects, an untargeted nuclear magnetic resonance metabolomic approach was applied to pre-diagnostic serum samples obtained from first incident, primary HCC cases (n = 114) and matched controls (n = 222) identified from amongst the participants of a large European prospective cohort.

RESULTS

A metabolic pattern associated with HCC risk comprised of perturbations in fatty acid oxidation and amino acid, lipid, and carbohydrate metabolism was observed. Sixteen metabolites of either endogenous or exogenous origin were found to be significantly associated with HCC risk. The influence of hepatitis infection and potential liver damage was assessed, and further analyses were made to distinguish patterns of early or later diagnosis.

CONCLUSION

Our results show clear metabolic alterations from early stages of HCC development with application for better etiologic understanding, prevention, and early detection of this increasingly common cancer.

Metabolomics reveals that aldose reductase activity due to AKR1B10 is upregulated in hepatitis C virus infection

To understand the changes in the metabolome of hepatitis C virus (HCV)-infected persons, we conducted a metabolomic investigation in both plasma and urine of 30 HCV-positive individuals using plasmas from 30 HCV-negative blood donors and urines from 30 healthy volunteers. Samples were analysed by gas chromatography-mass spectrometry and data subjected to multivariate analysis. The plasma metabolomic phenotype of HCV-positive persons was found to have elevated glucose, mannose and oleamide, together with depressed plasma lactate. The urinary metabolomic phenotype of HCV-positive persons comprised reduced excretion of fructose and galactose combined with elevated urinary excretion of 6-deoxygalactose (fucose) and the polyols sorbitol, galactitol and xylitol. HCV-infected persons had elevated galactitol/galactose and sorbitol/glucose urinary ratios, which were highly correlated. These observations pointed to enhanced aldose reductase activity, and this was confirmed by real-time quantitative polymerase chain reaction with AKR1B10 gene expression elevated sixfold in the liver. In contrast, AKR1B1 gene expression was reduced 40% in HCV-positive livers. Interestingly, persons who were formerly HCV infected retained the metabolomic phenotype of HCV infection without reverting to the HCV-negative metabolomic phenotype. This suggests that the effects of HCV on hepatic metabolism may be long lived. Hepatic AKR1B10 has been reported to be elevated in hepatocellular carcinoma and in several premalignant liver diseases. It would appear that HCV infection alone increases AKR1B10 expression, which manifests itself as enhanced urinary excretion of polyols with reduced urinary excretion of their corresponding hexoses. What role the polyols play in hepatic pathophysiology of HCV infection and its sequelae is currently unknown.

Analytical protocols based on LC-MS, GC-MS and CE-MS for nontargeted metabolomics of biological tissues

Invasive, site-specific metabolite information could be better obtained from tissues. Hence, highly sensitive mass spectrometry-based metabolomics coupled with separation techniques are increasingly in demand in clinical research for tissue metabolomics application. Applying these techniques to nontargeted tissue metabolomics provides identification of distinct metabolites. These findings could help us to understand alterations at the molecular level, which can also be applied in clinical practice as screening markers for early disease diagnosis. However, tissues as solid and heterogeneous samples pose an additional analytical challenge that should be considered in obtaining broad, reproducible and representative analytical profiles. This manuscript summarizes the state of the art in tissue (human and animal) treatment (quenching, homogenization and extraction) for nontargeted metabolomics with mass spectrometry.

Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery

Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics.

Metabolomics for biomarker discovery in gastroenterological cancer

The study of the omics cascade, which involves comprehensive investigations based on genomics, transcriptomics, proteomics, metabolomics, etc., has developed rapidly and now plays an important role in life science research. Among such analyses, metabolome analysis, in which the concentrations of low molecular weight metabolites are comprehensively analyzed, has rapidly developed along with improvements in analytical technology, and hence, has been applied to a variety of research fields including the clinical, cell biology, and plant/food science fields. The metabolome represents the endpoint of the omics cascade and is also the closest point in the cascade to the phenotype. Moreover, it is affected by variations in not only the expression but also the enzymatic activity of several proteins. Therefore, metabolome analysis can be a useful approach for finding effective diagnostic markers and examining unknown pathological conditions. The number of studies involving metabolome analysis has recently been increasing year-on-year. Here, we describe the findings of studies that used metabolome analysis to attempt to discover biomarker candidates for gastroenterological cancer and discuss metabolome analysis-based disease diagnosis.

Macro- and micro-environmental factors in clinical hepatocellular cancer

We previously developed a network phenotyping strategy (NPS), a graph theory-based transformation of clinical practice data, for recognition of two primary subgroups of hepatocellular cancer (HCC), called S and L, which differed significantly in their tumor masses. In the current study, we have independently validated this result on 641 HCC patients from another continent. We identified the same HCC subgroups with mean tumor masses 9 cm x n (S) and 22 cm x n (L), P<10(-14). The means of survival distribution (not available previously) for this new cohort were also significantly different (S was 12 months, L was 7 months, P<10(-5)). We characterized nine unique reference patterns of interactions between tumor and clinical environment factors, identifying four subtypes for S and five subtypes for L phenotypes, respectively. In L phenotype, all reference patterns were portal vein thrombosis (PVT)-positive, all platelet/alpha fetoprotein (AFP) levels were high, and all were chronic alcohol consumers. L had phenotype landmarks with worst survival. S phenotype interaction patterns were PVT-negative, with low platelet/AFP levels. We demonstrated that tumor-clinical environment interaction patterns explained how a given parameter level can have a different significance within a different overall context. Thus, baseline bilirubin is low in S1 and S4, but high in S2 and S3, yet all are S subtype patterns, with better prognosis than in L. Gender and age, representing macro-environmental factors, and bilirubin, prothrombin time, and AST levels representing micro-environmental factors, had a major impact on subtype characterization. Clinically important HCC phenotypes are therefore represented by complete parameter relationship patterns and cannot be replaced by individual parameter levels.

Metabolome analysis for discovering biomarkers of gastroenterological cancer

Improvements in analytical technologies have made it possible to rapidly determine the concentrations of thousands of metabolites in any biological sample, which has resulted in metabolome analysis being applied to various types of research, such as clinical, cell biology, and plant/food science studies. The metabolome represents all of the end products and by-products of the numerous complex metabolic pathways operating in a biological system. Thus, metabolome analysis allows one to survey the global changes in an organism's metabolic profile and gain a holistic understanding of the changes that occur in organisms during various biological processes, e.g., during disease development. In clinical metabolomic studies, there is a strong possibility that differences in the metabolic profiles of human specimens reflect disease-specific states. Recently, metabolome analysis of biofluids, e.g., blood, urine, or saliva, has been increasingly used for biomarker discovery and disease diagnosis. Mass spectrometry-based techniques have been extensively used for metabolome analysis because they exhibit high selectivity and sensitivity during the identification and quantification of metabolites. Here, we describe metabolome analysis using liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Furthermore, the findings of studies that attempted to discover biomarkers of gastroenterological cancer are also outlined. Finally, we discuss metabolome analysis-based disease diagnosis.