A serum metabolomic investigation on hepatocellular carcinoma patients by chemical derivatization followed by gas chromatography/mass spectrometry

Differences in Prediagnostic Serum Metabolomic and Lipidomic Profiles Between Cirrhosis Patients with and without Incident Hepatocellular Carcinoma

Background

Early detection of hepatocellular carcinoma (HCC) is crucial for improving patient outcomes, but we lack robust clinical biomarkers. This study aimed to identify a metabolite and/or lipid panel for early HCC detection.

Methods

We developed a high-resolution liquid chromatography mass spectrometry (LC-MS)-based profiling platform and evaluated differences in the global metabolome and lipidome between 28 pre-diagnostic serum samples from patients with cirrhosis who subsequently developed HCC (cases) and 30 samples from patients with cirrhosis and no HCC (controls). We linked differentially expressed metabolites and lipids to their associated genes, proteins, and transcriptomic signatures in publicly available datasets. We used machine learning models to identify a minimal panel to distinguish between cases and controls.

Results

Among cases compared with controls, 124 metabolites and 246 lipids were upregulated, while 208 metabolites and 73 lipids were downregulated. The top upregulated metabolites were glycoursodeoxycholic acid, 5-methyltetrahydrofolic acid, octanoyl-coenzyme A, and glycocholic acid. Elevated lipids comprised glycerol lipids, cardiolipin, and phosphatidylethanolamine, whereas suppressed lipids included oxidized phosphatidylcholine and lysophospholipids. There was an overlap between differentially expressed metabolites and lipids and previously published transcriptomic signatures, illustrating an association with liver disease severity. A panel of 12 metabolites that distinguished between cases and controls with an area under the receiver operating curve of 0.98 for the support vector machine (interquartile range, 0.9-1).

Conclusion

Using prediagnostic serum samples, we identified a promising metabolites panel that accurately identifies patients with cirrhosis who progressed to HCC. Further validation of this panel is required.

Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma

Liver cancer is a global health challenge, causing a significant social-economic burden. Hepatocellular carcinoma (HCC) is the predominant type of primary liver cancer, which is highly heterogeneous in terms of molecular and cellular signatures. Early-stage or small tumors are typically treated with surgery or ablation. Currently, chemotherapies and immunotherapies are the best treatments for unresectable tumors or advanced HCC. However, drug response and acquired resistance are not predictable with the existing systematic guidelines regarding mutation patterns and molecular biomarkers, resulting in sub-optimal treatment outcomes for many patients with atypical molecular profiles. With advanced technological platforms, valuable information such as tumor genetic alterations, epigenetic data, and tumor microenvironments can be obtained from liquid biopsy. The inter- and intra-tumoral heterogeneity of HCC are illustrated, and these collective data provide solid evidence in the decision-making process of treatment regimens. This article reviews the current understanding of HCC detection methods and aims to update the development of HCC surveillance using liquid biopsy. Recent critical findings on the molecular basis, epigenetic profiles, circulating tumor cells, circulating DNAs, and omics studies are elaborated for HCC diagnosis. Besides, biomarkers related to the choice of therapeutic options are discussed. Some notable recent clinical trials working on targeted therapies are also highlighted. Insights are provided to translate the knowledge into potential biomarkers for detection and diagnosis, prognosis, treatment response, and drug resistance indicators in clinical practice.

Metabolic Signatures: Pioneering the Frontier of Rectal Cancer Diagnosis and Response to Neoadjuvant Treatment with Biomarkers-A Systematic Review

Colorectal cancer (CRC) is one of the most aggressive, heterogenous, and fatal types of human cancer for which screening, and more effective therapeutic drugs are urgently needed. Early-stage detection and treatment greatly improve the 5-year survival rate. In the era of targeted therapies for all types of cancer, a complete metabolomic profile is mandatory before neoadjuvant therapy to assign the correct drugs and check the response to the treatment given. The aim of this study is to discover specific metabolic biomarkers or a sequence of metabolomic indicators that possess precise diagnostic capabilities in predicting the efficacy of neoadjuvant therapy. After searching the keywords, a total of 108 articles were identified during a timeframe of 10 years (2013-2023). Within this set, one article was excluded due to the use of non-English language. Six scientific papers were qualified for this investigation after eliminating all duplicates, publications not referring to the subject matter, open access restriction papers, and those not applicable to humans. Biomolecular analysis found a correlation between metabolomic analysis of colorectal cancer samples and poor progression-free survival rates. Biomarkers are instrumental in predicting a patient's response to specific treatments, guiding the selection of targeted therapies, and indicating resistance to certain drugs.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Essential amino acids as diagnostic biomarkers of hepatocellular carcinoma based on metabolic analysis

Metabolomics, defined as the comprehensive identification of all small metabolites in a biological sample, has the power to shed light on phenotypic changes associated with various diseases, including cancer. To discover potential metabolomic biomarkers of hepatocellular carcinoma (HCC), we investigated the metabolomes of tumor and non-tumor tissue in 20 patients with primary HCC using capillary electrophoresis-time-of-flight mass spectrometry. We also analyzed blood samples taken immediately before and 14 days after hepatectomy to identify associated changes in the serum metabolome. Marked changes were detected in the different quantity of 61 metabolites that could discriminate between HCC tumor and paired non-tumor tissue and additionally between HCC primary tumors and colorectal liver metastases. Among the 30 metabolites significantly upregulated in HCC tumors compared with non-tumor tissues, 10 were amino acids, and 7 were essential amino acids (leucine, valine, tryptophan, isoleucine, methionine, lysine, and phenylalanine). Similarly, the serum metabolomes of HCC patients before hepatectomy revealed a significant increase in 16 metabolites, including leucine, valine, and tryptophan. Our results reveal striking differences in the metabolomes of HCC tumor tissue compared with non-tumor tissue, and identify the essential amino acids leucine, valine, and tryptophan as potential metabolic biomarkers for HCC.

Interplay Between Fatty Acids, Stearoyl-Co-A Desaturase, Mechanistic Target of Rapamycin, and Yes-Associated Protein/Transcriptional Coactivator With PDZ-Binding Motif in Promoting Hepatocellular Carcinoma

Nonalcoholic fatty liver disease has reached pandemic proportions with one of its most consequential complications being hepatocellular carcinoma (HCC). Nonalcoholic fatty liver disease-related HCC is becoming the leading indication for liver transplantation in the United States. Given the scarcity of available organs, early detection and prevention remain key in prevention and management of the disease. Over the years, the yes-associated protein (YAP)/transcriptional coactivator with PDZ-binding motif (TAZ) pathway emerged as a key signal transduction pathway in the pathogenesis of HCC. In this review, we explore the interplay between the YAP/TAZ pathway as a point of convergence in HCC pathogenesis. We review the evidence of how lipid reprogramming and key lipid pathways, saturated and monounsaturated fatty acids (through the rate-limiting enzyme stearoyl Co-A desaturase), the mevalonic acid pathway (the role of statins), and mechanistic target of rapamycin all play critical roles in intricate and complex networks that tightly regulate the YAP/TAZ pro-oncogenic pathway.

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.

Gut Microbiota and Metabolite Changes in Patients With Ulcerative Colitis and Clostridioides difficile Infection

Background

Patients with ulcerative colitis (UC) are at an increased risk of developing Clostridioides difficile infection (CDI), which in turn leads to poor outcomes. The gut microbial structure and metabolites in patients with UC and CDI have been scarcely studied. We hypothesized that CDI changes the gut microbiota and metabolites of patients with UC.

Materials and Methods

This study included 89 patients: 30 healthy controls (HC group), 29 with UC alone (UCN group), and 30 with UC and CDI (UCP group). None of the participants has been exposed to antibiotic treatments during the 3 months before stool collection. Stool samples were analyzed using 16S rRNA gene sequencing of the V3–V4 region and gas chromatography tandem time-of-flight mass spectrometry.

Results

The UCN group displayed lower diversity and richness in gut microbiota and a higher relative abundance of the phylum Proteobacteria than the HC group. There were no significant differences between the UCN and UCP groups in the α-diversity indices. The UCP group contained a higher relative abundance of the genera Clostridium sensu stricto, Clostridium XI, Aggregatibacter, and Haemophilus, and a lower relative abundance of genera Clostridium XIVb and Citrobacter than the UCN group. In the UCP group, the increased metabolites included putrescine, maltose, 4-hydroxybenzoic acid, 4-hydroxybutyrate, and aminomalonic acid. Spearman’s correlation analysis revealed that these increased metabolites negatively correlated with Clostridium XlVb and positively correlated with the four enriched genera. However, the correlations between hemoglobin and metabolites were contrary to the correlations between erythrocyte sedimentation rate and high-sensitivity C-reactive protein and metabolites.

Conclusion

Our study identified 11 differential genera and 16 perturbed metabolites in patients with UC and CDI compared to those with UC alone. These findings may guide the design of research on potential mechanisms and specific treatments for CDI in patients with UC.

Bioinformatics analysis and experimental verification of five metabolism-related lncRNAs as prognostic models for hepatocellular carcinoma

BACKGROUND

The number of patients with hepatocellular carcinoma (HCC) is showing a growing trend all over the world. The metabolic microenvironment has been shown to play a key role in the pathogenesis of HCC in recent studies. The expression of metabolites and metabolic processes in tumor cells can be regulated by gene regulation mediated by long non-coding RNAs (lncRNAs), the abnormal expression of which is closely related to tumor occurrence and metastasis. However, the fundamental mechanism of applying metabolism-related lncRNAs to predicting HCC is still unclear.

METHODS

With the complete RNA sequence data and clinical data obtained from The Cancer Genome Atlas database and metabolism-related genes downloaded from the Kyoto Encyclopedia of Genes and Genomes database, with false discovery rate < 0.001, log fold change > 1.5 selected as the screening criteria for lncRNA, the relationship between the expression level of metabolism-related LncRNAs (MRLs) and the overall survival rate was determined by the Univariate Cox regression analyses with the establishment of the metabolic prognosis model by the application of Multivariate Cox regression analyses, revealing the different biological processes and signaling pathways in both high-risk groups and low-risk groups by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis, leading the expression of lncRNA to be assessed by the reverse transcription-polymerase chain reaction results.

RESULTS

The overall survival rate of HCC patients is significantly correlated with signature of 5-MRLs. The prognosis characteristics of lncRNA reveal the relatively high death rate of patients in the high-risk groups, with the predicted signals by functional and pathway enrichment analysis related to biosynthesis, metabolic process, and metabolic pathway, with the prognostic characteristics of 5-MRLs by the combined analysis showing that it is an independent factor of HCC superior to the traditional clinical indicators in predicting the prognosis. A trend of high-expression was shown in MRLs in tumors by reverse transcription-polymerase chain reaction.

CONCLUSION

The new 5-MRLs as potential biomarkers provide more powerful prognostic information for HCC patients. In the future clinical treatment of HCC, it will provide doctors with more methods.

Metabolomic biomarkers for hepatocellular carcinoma: A systematic review

Hepatocellular carcinoma (HCC) is a highly malignant cancer which lack of effective diagnosis and prognosis biomarkers, therefore surging studies focused on the metabolite candidates for HCC. The current study was designed to systematically review the metabolic studies for HCC, summarize the current available evidence and provide implication for further studies within this area. By systematically screening Pubmed and Embase, and eligibility assessment, we eventually included 55 pieces of studies. After summarized their characteristics, we reviewed them by 3 parts, regarding to the different biofluid they carried out the experiments. By collecting the candidates from all the included studies, we carried out pathway enrichment to see the representative of the reported candidates, as expected the pathway consistent with the current knowledge of HCC. Next, we conduct quality assessment on the included studies. Only 36% of the current evidence grouped as high quality, indicating the quality of metabolic studies needs further improvement.

Characterization of metabolic landscape in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide, accounting for approximately 75%-85% of primary liver cancers. Metabolic alterations have been labeled as an emerging hallmark of tumors. Specially, the last decades have registered a significant improvement in our understanding of the role of metabolism in driving the carcinogenesis and progression of HCC. In this paper, we provide a review of recent studies that investigated the metabolic traits of HCC with a specific focus on three common metabolic alterations involving glycolysis, lipid metabolism, and glutamine addiction which have been gaining much attention in the field of HCC. Next, we describe some representative diagnostic markers or tools, and promising treatment agents that are proposed on the basis of the aforementioned metabolic alterations for HCC. Finally, we present some challenges and directions that may promisingly speed up the process of developing objective diagnostic markers and therapeutic options underlying HCC. Specifically, we recommend future investigations to carefully take into account the influence of heterogeneity, control for study-specific confounds, and invite the validation of existing biomarkers.

Metabolic Groups Related to Blood Vitamin Levels and Inflammatory Biomarkers in Brazilian Children and Adolescents

Certain B-vitamins and vitamin A may be involved in inflammatory pathways associated with homocysteine and omega-3 fatty acids. The aims of this study were (i) to determine whether different metabolic profiles of B-vitamins and vitamin A in Brazilian children and adolescents were positively or negatively related to homocysteine and omega-3 fatty acids using k-means clustering analysis, (ii) compare nutrient intakes and metabolites between the different metabolic profiles, (iii) evaluate if the statistically significant metabolites found between the metabolic groups, can predict the variation of leukotriene A4 hydrolase (LTA4H) levels, a biomarker of low-grade inflammation, in the total studied population. This cross-sectional study included 124 children and adolescents, aged 9-13 y old. Dietary intake was assessed by the food frequency questionnaire and 24-hour recall. Biomarkers for vitamins B2, B6, B12, folate and vitamin A were measured in plasma. Omega-3 fatty acids and homocysteine were measured in red blood cells (RBC). Two different metabolic profiles were found. Thirty of these individuals had overall average higher riboflavin, pyridoxal, and vitamin B12 plasma levels (metabolic group 1) compared to 94 individuals (group 2). Group 2 had lower dietary intake of vitamin B2, vitamin A, and vitamin B12 and higher RBC levels of homocysteine. EPA and DHA erythrocyte levels were not different between metabolic groups. Multiple linear regression analyses showed that blood cobalamin, riboflavin, pyridoxal and homocysteine combined, explained 9.0% of LTA4H levels variation in the total studied population. The metabolic group that had low plasma levels of riboflavin, pyridoxal, and cobalamin also had a lower dietary intake of B-vitamin and higher RBC homocysteine. The combined levels of the riboflavin, pyridoxal, cobalamin and homocysteine biomarkers can predict the variation of LTA4H in the total population studied, but it is not clear how this regulation occurs.

Characterization of Postprandial Effects on CSF Metabolomics: A Pilot Study with Parallel Comparison to Plasma

Cerebrospinal fluid (CSF) metabolites reflect biochemical diffusion/export from the brain and possibly serve as biomarkers related to brain disease severity, pathophysiology, and therapeutic efficacy/toxicity. Metabolomic studies using blood matrices have demonstrated interindividual and preanalytical variation of blood metabolites, whereas those of CSF metabolites remain unclear. In this study, we aimed to delineate the postprandial effects on CSF metabolites because fasting of patients with brain-related disorders is challenging. We collected pre- and postprandial (1.5, 3, and 6 h) plasma and CSF from nine healthy subjects. Using a mass-spectrometry-based global metabolomics approach, 150 and 130 hydrophilic metabolites and 263 and 340 lipids were detected in CSF and plasma, respectively. Principal component analysis of CSF hydrophilic metabolites and lipids primarily classified individual subjects at any time point, suggesting that the postprandial effects had a lower impact than interindividual variations on CSF metabolites. Individually, less than 10% of the CSF metabolites were putatively altered by postprandial effects (with either significant differences or over 2-fold changes, but not both) at any time point. Thus, global CSF metabolite levels are not directly associated with food intake, and except for several putatively altered CSF metabolites, postprandial effects are not a major concern when applying CSF metabolomics to screen biomarkers.

Profiling of plasma metabolomics in patients with hepatitis C-related liver cirrhosis and hepatocellular carcinoma

Aim of the study

The diagnosis of hepatocellular carcinoma (HCC) is usually late, due to the lack of early detection of biomarkers for HCC. Metabolomics analysis has emerged as a useful tool for studying human diseases. The objective of the study was to investigate the differences in plasma metabolites between hepatitis C virus (HCV)-induced cirrhosis and HCC.

Material and methods

22 subjects with HCV-related liver cirrhosis and 22 subjects with HCC were enrolled. Clinical, routine laboratory and imaging studies were done. Gas chromatography/mass spectrometry (GC/MS) was used for metabolomics analysis of patients' plasma samples.

Results

34 known metabolites were detected, of which five metabolites were identified to have the strongest discriminatory power for separation between HCC and cirrhosis groups: octanoic acid (caprylic acid), decanoic (capric acid), oleic acid, oxalic acid and glycine. These are 3 fatty acids (FA), a dicarboxylic acid and a glucogenic amino acid, respectively. No significant correlation was found between the relative intensities of the five metabolites and any of the patient or tumor characteristics (Child-Turcotte-Pugh (CTP) score, Barcelona Clinic Liver Cancer (BCLC) stage, number of focal lesions and size of largest focal lesion). ROC curve analysis was performed and area under the curve (AUC) was calculated, revealing that oleic acid, octanoic (caprylic) acid and glycine had higher positive predictive value than α-fetoprotein.

Conclusions

The study of metabolomics (particularly involving FA) may help define distinct metabolic patterns to distinguish HCV-induced liver cirrhosis from HCC patients. Future research in this field is still needed, particularly concerning HCC treatment strategies which target fatty acid-related metabolic pathways.

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.

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.

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.

Urinary Metabolomic Profiling in Chronic Hepatitis B Viral Infection Using Gas Chromatography/Mass Spectrometry

Background: Chronic hepatitis B (CHB) can lead to cirrhosis and hepatocellular carcinoma. The metabolomic profiling has been shown to be associated with pathogenic mechanisms in many medical conditions including CHB. The purpose of this study was to investigate the urine metabolomic profiles in CHB patients by gas chromatography/mass spectrometry (GC/MS). Methods: Urine samples were collected from CHB patients (n = 20) and normal control subjects (n = 20). Metabolite profiles were assessed using GC/MS in conjunction with multivariate statistical analysis, in order to identify biomarker metabolites. Pathway analysis was performed by MetaboAnalyst 3.0 and KEGG database.Results: Twelve out of 377 metabolites were shown to be significantly different between the CHB and normal control groups (p < 0.05). These include palmitic acid, stearic acid, oleic acid, benzoic acid, butanoic acid, cholesterol, glycine, 3-heptanone, 4-heptanone, hexanal, 1-tetradecanol and naphthalene. Multivariate statistical analysis constructed using these expressed metabolites showed CHB patients can be discriminated from healthy controls with high sensitivity (95%) and specificity (85%). All the metabolic perturbations in this disease are associated with pathways of fatty acid, amino acid, bile acid and gut microbial metabolism. Conclusion: CHB patients have a specific urinary metabolomic profile. The abnormalities of fatty acid, amino acid, bile acid, and gut microbial metabolism lead to the development of disease progression. GC/MS-based assay is a promising tool for the metabolomic study in CHB.

Metabolomics analysis reveals an effect of homocysteine on arachidonic acid and linoleic acid metabolism pathway

An increase in serum homocysteine level has been associated with an increased risk of vascular disease; however, the biochemical mechanisms that underlie these effects remain largely unknown. The present study aimed to use high-performance liquid chromatography-mass spectrometry (HPLC‑MS) to demonstrate the effects of serum homocysteine on human blood metabolites. A total of 75 fasting serum samples were investigated in the present study. Using a threshold of 15 µmol/l serum homocysteine level, samples were divided into high‑ and low‑homocysteine groups, and the serum extracts were analyzed with an HPLC‑MS‑based method. A total of 269 features exhibited significant differences and correlation with serum homocysteine levels in the electrospray ionization‑positive [ESI(+)] mode, and 69 features were identified in the ESI(‑) mode between the two groups. The principal component analysis plot revealed a separation between the high‑ and the low‑homocysteine groups. Metabolite set enrichment analysis identified arachidonic acid metabolism and linoleic acid metabolism as the two pathways with significantly enriched differences. These results revealed that arachidonic acid and linoleic acid metabolism may be associated with serum homocysteine levels and may be involved in homocysteine-induced vascular disease.

Discovery of potential biomarkers in human melanoma cells with different metastatic potential by metabolic and lipidomic profiling

Malignant melanoma, characterized by its ability to metastasize to other organs, is responsible for 90% of skin cancer mortality. To investigate alterations in the cellular metabolome and lipidome related to melanoma metastasis, gas chromatography-mass spectrometry (GC-MS) and direct infusion-mass spectrometry (DI-MS)-based metabolic and lipidomic profiling were performed on extracts of normal human melanocyte (HEMn-LP), low metastatic melanoma (A375, G361), and highly metastatic melanoma (A2058, SK-MEL-28) cell lines. In this study, metabolomic analysis identified aminomalonic acid as a novel potential biomarker to discriminate between different stages of melanoma metastasis. Uptake and release of major metabolites as hallmarks of cancer were also measured between high and low metastatic melanoma cells. Lipid analysis showed a progressive increase in phosphatidylinositol (PI) species with saturated and monounsaturated fatty acyl chains, including 16:0/18:0, 16:0/18:1, 18:0/18:0, and 18:0/18:1, with increasing metastatic potential of melanoma cells, defining these lipids as possible biomarkers. In addition, a partial-least-squares projection to latent structure regression (PLSR) model for the prediction of metastatic properties of melanoma was established, and central metabolic and lipidomic pathways involved in the increased motility and metastatic potential of melanoma cells were identified as therapeutic targets. These results could be used to diagnose and control of melanoma metastasis.

Advanced mass spectrometry-based multi-omics technologies for exploring the pathogenesis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the primary hepatic malignancies and is the third most common cause of cancer related death worldwide. Although a wealth of knowledge has been gained concerning the initiation and progression of HCC over the last half century, efforts to improve our understanding of its pathogenesis at a molecular level are still greatly needed, to enable clinicians to enhance the standards of the current diagnosis and treatment of HCC. In the post-genome era, advanced mass spectrometry driven multi-omics technologies (e.g., profiling of DNA damage adducts, RNA modification profiling, proteomics, and metabolomics) stand at the interface between chemistry and biology, and have yielded valuable outcomes from the study of a diversity of complicated diseases. Particularly, these technologies are being broadly used to dissect various biological aspects of HCC with the purpose of biomarker discovery, interrogating pathogenesis as well as for therapeutic discovery. This proof of knowledge-based critical review aims at exploring the selected applications of those defined omics technologies in the HCC niche with an emphasis on translational applications driven by advanced mass spectrometry, toward the specific clinical use for HCC patients. This approach will enable the biomedical community, through both basic research and the clinical sciences, to enhance the applicability of mass spectrometry-based omics technologies in dissecting the pathogenesis of HCC and could lead to novel therapeutic discoveries for HCC.

Integrative functional genomic analysis unveils the differing dysregulated metabolic processes across hepatocellular carcinoma stages

Hepatocellular carcinoma (HCC) is a highly heterogeneous disease and the development of targeted therapeutics is still at an early stage. The 'omics' based genome-wide profiling comprising the transcriptome, miRNome and proteome are highly useful in identifying the deregulated molecular processes involved in hepatocarcinogenesis. One of the end products and processes of the central dogma being the metabolites and metabolic processes mediate the cellular functions. In recent years, metabolomics based investigations have revealed the major deregulated metabolic processes involved in carcinogenesis. However, the integrative analysis of the holistic metabolic processes with genomics is at an early stage. Since the gene-sets are highly useful in assessing the biological processes and pathways, we made an attempt to infer the deregulated cellular metabolic processes involved in HCC by employing metabolism associated gene-set enrichment analysis. Further, the metabolic process enrichment scores were integrated with the transcriptome profiles of HCC. Integrative analysis shows three distinct metabolic deregulations: i) hepatocyte function related molecular processes involving lipid/fatty acid/bile acid synthesis, ii) inflammatory processes with cytokine, sphingolipid & chondriotin sulphate metabolism and iii) enriched nucleotide metabolic process involving purine/pyrimidine & glucose mediated catabolic process, in hepatocarcinogenesis. The three distinct metabolic processes were found to occur both in tumor and liver cancer cell line profiles. Unsupervised hierarchical clustering of the metabolic processes along with clinical sample information has identified two major clusters based on AFP (alpha-fetoprotein) and metastasis. The study reveals the three major regulatory processes involved in HCC stages.

Hepatocellular carcinoma: Review of disease and tumor biomarkers

Hepatocellular carcinoma (HCC) is a common malignancy and now the second commonest global cause of cancer death. HCC tumorigenesis is relatively silent and patients experience late symptomatic presentation. As the option for curative treatments is limited to early stage cancers, diagnosis in non-symptomatic individuals is crucial. International guidelines advise regular surveillance of high-risk populations but the current tools lack sufficient sensitivity for early stage tumors on the background of a cirrhotic nodular liver. A number of novel biomarkers have now been suggested in the literature, which may reinforce the current surveillance methods. In addition, recent metabonomic and proteomic discoveries have established specific metabolite expressions in HCC, according to Warburg’s phenomenon of altered energy metabolism. With clinical validation, a simple and non-invasive test from the serum or urine may be performed to diagnose HCC, particularly benefiting low resource regions where the burden of HCC is highest.

Acetylcarnitine Is a Candidate Diagnostic and Prognostic Biomarker of Hepatocellular Carcinoma

The identification of serum biomarkers to improve the diagnosis and prognosis of hepatocellular carcinoma has been elusive to date. In this study, we took a mass spectroscopic approach to characterize metabolic features of the liver in hepatocellular carcinoma patients to discover more sensitive and specific biomarkers for diagnosis and progression. Global metabolic profiling of 50 pairs of matched liver tissue samples from hepatocellular carcinoma patients was performed. A series of 62 metabolites were found to be altered significantly in liver tumors; however, levels of acetylcarnitine correlated most strongly with tumor grade and could discriminate between hepatocellular carcinoma tumors and matched normal tissues. Post hoc analysis to evaluate serum diagnosis and progression potential further confirmed the diagnostic capability of serum acetylcarnitine. Finally, an external validation in an independent batch of 58 serum samples (18 hepatocellular carcinoma patients, 20 liver cirrhosis patients, and 20 healthy individuals) verified that serum acetylcarnitine was a meaningful biomarker reflecting hepatocellular carcinoma diagnosis and progression. These findings present a strong new candidate biomarker for hepatocellular carcinoma with potentially significant diagnostic and prognostic capabilities. Cancer Res; 76(10); 2912-20. ©2016 AACR.

Phenotypic Characterization Analysis of Human Hepatocarcinoma by Urine Metabolomics Approach

Hepatocarcinoma (HCC) is one of the deadliest cancers in the world and represents a significant disease burden. Better biomarkers are needed for early detection of HCC. Metabolomics was applied to urine samples obtained from HCC patients to discover noninvasive and reliable biomarkers for rapid diagnosis of HCC. Metabolic profiling was performed by LC-Q-TOF-MS in conjunction with multivariate data analysis, machine learning approaches, ingenuity pathway analysis and receiver-operating characteristic curves were used to select the metabolites which were used for the noninvasive diagnosis of HCC. Fifteen differential metabolites contributing to the complete separation of HCC patients from matched healthy controls were identified involving several key metabolic pathways. More importantly, five marker metabolites were effective for the diagnosis of human HCC, achieved a sensitivity of 96.5% and specificity of 83% respectively, could significantly increase the diagnostic performance of the metabolic biomarkers. Overall, these results illustrate the power of the metabolomics technology which has the potential as a non-invasive strategies and promising screening tool to evaluate the potential of the metabolites in the early diagnosis of HCC patients at high risk and provides new insight into pathophysiologic mechanisms.

Application of 1H NMR spectroscopy-based metabonomics to feces of cervical cancer patients with radiation-induced acute intestinal symptoms

OBJECTIVE

Radiation-induced acute intestinal symptoms (RIAISs) are a common complication of radiotherapy for cervical cancer. The aim of this study was to use (1)H nuclear magnetic resonance ((1)H NMR) combined with chemometric analysis to develop a metabolic profile of patients with RIAISs.

METHODS

Fecal samples were collected from 66 patients with cervical cancer before and after pelvic radiotherapy. After radiotherapy, RIAISs occurred in eleven patients. We selected another 11 patients from participants without RIAISs whose age, stage, histological type and treatment methods are matched with RIAIS patients as the control group. (1)H NMR spectroscopy combined with multivariate pattern recognition analysis was used to generate metabolic profile data, as well as to establish a RIAIS-specific metabolic phenotype.

RESULTS

Orthogonal partial least-squares discriminant analysis was used to distinguish samples between the pre- and post-radiotherapy RIAIS patients and between RIAIS patients and controls. Fecal samples from RIAIS patients after pelvic radiotherapy were characterized by increased concentrations of α-ketobutyrate, valine, uracil, tyrosine, trimethylamine N-oxide, phenylalanine, lysine, isoleucine, glutamine, creatinine, creatine, bile acids, aminohippurate, and alanine, accompanied by reduced concentrations of α-glucose, n-butyrate, methylamine, and ethanol relative to samples from RIAIS patients before pelvic radiotherapy, while in RIAIS patients relative to controls, trimethylamine, n-butyrate, fumarate and acetate were down-regulated and valine, TMAO, taurine, phenylalanine, lactate, isoleucine and creatinine were up-regulated.

CONCLUSIONS

We obtained the metabolic profile of RIAIS patients from fecal samples using NMR-based metabonomics. This profile has the potential to be developed into a novel clinical tool for RIAIS diagnosis or therapeutic monitoring, and could contribute to an improved understanding of the disease mechanism. However, because of the limitations of methods, technique, bacterial contamination of feces and small sample size, further research and verification are needed.

GC-MS Based Plasma Metabolomics for Identification of Candidate Biomarkers for Hepatocellular Carcinoma in Egyptian Cohort

This study evaluates changes in metabolite levels in hepatocellular carcinoma (HCC) cases vs. patients with liver cirrhosis by analysis of human blood plasma using gas chromatography coupled with mass spectrometry (GC-MS). Untargeted metabolomic analysis of plasma samples from participants recruited in Egypt was performed using two GC-MS platforms: a GC coupled to single quadruple mass spectrometer (GC-qMS) and a GC coupled to a time-of-flight mass spectrometer (GC-TOFMS). Analytes that showed statistically significant changes in ion intensities were selected using ANOVA models. These analytes and other candidates selected from related studies were further evaluated by targeted analysis in plasma samples from the same participants as in the untargeted metabolomic analysis. The targeted analysis was performed using the GC-qMS in selected ion monitoring (SIM) mode. The method confirmed significant changes in the levels of glutamic acid, citric acid, lactic acid, valine, isoleucine, leucine, alpha tocopherol, cholesterol, and sorbose in HCC cases vs. patients with liver cirrhosis. Specifically, our findings indicate up-regulation of metabolites involved in branched-chain amino acid (BCAA) metabolism. Although BCAAs are increasingly used as a treatment for cancer cachexia, others have shown that BCAA supplementation caused significant enhancement of tumor growth via activation of mTOR/AKT pathway, which is consistent with our results that BCAAs are up-regulated in HCC.

Biomarker identification and pathway analysis by serum metabolomics of lung cancer

Lung cancer is one of the most common causes of cancer death, for which no validated tumor biomarker is sufficiently accurate to be useful for diagnosis. Additionally, the metabolic alterations associated with the disease are unclear. In this study, we investigated the construction, interaction, and pathways of potential lung cancer biomarkers using metabolomics pathway analysis based on the Kyoto Encyclopedia of Genes and Genomes database and the Human Metabolome Database to identify the top altered pathways for analysis and visualization. We constructed a diagnostic model using potential serum biomarkers from patients with lung cancer. We assessed their specificity and sensitivity according to the area under the curve of the receiver operator characteristic (ROC) curves, which could be used to distinguish patients with lung cancer from normal subjects. The pathway analysis indicated that sphingolipid metabolism was the top altered pathway in lung cancer. ROC curve analysis indicated that glycerophospho-N-arachidonoyl ethanolamine (GpAEA) and sphingosine were potential sensitive and specific biomarkers for lung cancer diagnosis and prognosis. Compared with the traditional lung cancer diagnostic biomarkers carcinoembryonic antigen and cytokeratin 19 fragment, GpAEA and sphingosine were as good or more appropriate for detecting lung cancer. We report our identification of potential metabolic diagnostic and prognostic biomarkers of lung cancer and clarify the metabolic alterations in lung cancer.

Application of GC-MS coupled with chemometrics for scanning serum metabolic biomarkers from renal fibrosis rat

Renal interstitial fibrosis closely relates to chronic kidney disease and is regarded as the final common pathway in most cases of end-stage renal disease. Metabolomic biomarkers can facilitate early diagnosis and allow better understanding of the pathogenesis underlying renal fibrosis. Gas chromatography-mass spectrometry (GC/MS) is one of the most promising techniques for identification of metabolites. However, the existence of the background, baseline offset, and overlapping peaks makes accurate identification of the metabolites unachievable. In this study, GC/MS coupled with chemometric methods was successfully developed to accurately identify and seek metabolic biomarkers for rats with renal fibrosis. By using these methods, seventy-six metabolites from rat serum were accurately identified and five metabolites (i.e., urea, ornithine, citric acid, galactose, and cholesterol) may be useful as potential biomarkers for renal fibrosis.

Proteomic and metabonomic biomarkers for hepatocellular carcinoma: a comprehensive review

Hepatocellular carcinoma (HCC) ranks third in overall global cancer-related mortality. Symptomatic presentation often means advanced disease where potentially curative treatment options become very limited. Numerous international guidelines propose the routine monitoring of those with the highest risk factors for the condition in order to diagnose potential tumourigenesis early. To aid this, the fields of metabonomic- and proteomic-based biomarker discovery have applied advanced tools to identify early changes in protein and metabolite expression in HCC patients vs controls. With robust validation, it is anticipated that from these candidates will rise a high-performance non-invasive test able to diagnose early HCC and related conditions. This review gathers the numerous markers proposed by studies using mass spectrometry and proton nuclear magnetic resonance spectroscopy and evaluates areas of consistency as well as discordance.

Plasma metabolite profiling and chemometric analyses of lung cancer along with three controls through gas chromatography-mass spectrometry

Lung cancer has been the most common death causing cancer in the world for several decades. This study is focused on the metabolite profiling of plasma from lung cancer (LC) patients with three control groups including healthy non-smoker (NS), smokers (S) and chronic obstructive pulmonary disease patients (COPD) samples using gas chromatography-mass spectrometry (GC-MS) in order to identify the comparative and distinguishing metabolite pattern for lung cancer. Metabolites obtained were identified through National Institute of Standards and Technology (NIST) mass spectral (Wiley registry) and Fiehn Retention Time Lock (RTL) libraries. Mass Profiler Professional (MPP) Software was used for the alignment and for all the statistical analysis. 32 out of 1,877 aligned metabolites were significantly distinguished among three controls and lung cancer using p-value ≤ 0.001. Partial Least Square Discriminant Analysis (PLSDA) model was generated using statistically significant metabolites which on external validation provide high sensitivity (100%) and specificity (78.6%). Elevated level of fatty acids, glucose and acids were observed in lung cancer in comparison with control groups apparently due to enhanced glycolysis, gluconeogenesis, lipogenesis and acidosis, indicating the metabolic signature for lung cancer.

The ability of bilirubin in identifying smokers with higher risk of lung cancer: a large cohort study in conjunction with global metabolomic profiling

PURPOSE

We aimed to identify serum metabolites as potential valuable biomarkers for lung cancer and to improve risk stratification in smokers.

EXPERIMENTAL DESIGN

We performed global metabolomic profiling followed by targeted validation of individual metabolites in a case-control design of 386 lung cancer cases and 193 matched controls. We then validated bilirubin, which consistently showed significant differential levels in cases and controls, as a risk marker for lung cancer incidence and mortality in a large prospective cohort composed of 425,660 participants.

RESULTS

Through global metabolomic profiling and following targeted validation, bilirubin levels consistently showed a statistically significant difference among healthy controls and lung cancer cases. In the prospective cohort, the inverse association was only seen in male smokers, regardless of smoking pack-years and intensity. Compared with male smokers in the highest bilirubin group (>1 mg/dL), those in the lowest bilirubin group (<0.75 mg/dL) had 55% and 66% increase in risks of lung cancer incidence and mortality, respectively. For every 0.1 mg/dL decrease of bilirubin, the risks for lung cancer incidence and mortality increased by 5% and 6% in male smokers, respectively (both P < 0.001). There was a significant interaction between low serum bilirubin level and smoking on lung cancer risk (Pinteraction = 0.001).

CONCLUSION

Low levels of serum bilirubin are associated with higher risks of lung cancer incidence and mortality in male smokers and can be used to identify higher risk smokers for lung cancer.

Metabolomics of ApcMin/+ mice genetically susceptible to intestinal cancer

Background

To determine how diets high in saturated fat could increase polyp formation in the mouse model of intestinal neoplasia, Apc^Min/+, we conducted large-scale metabolome analysis and association study of colon and small intestine polyp formation from plasma and liver samples of Apc^Min/+ vs. wild-type littermates, kept on low vs. high-fat diet. Label-free mass spectrometry was used to quantify untargeted plasma and acyl-CoA liver compounds, respectively. Differences in contrasts of interest were analyzed statistically by unsupervised and supervised modeling approaches, namely Principal Component Analysis and Linear Model of analysis of variance. Correlation between plasma metabolite concentrations and polyp numbers was analyzed with a zero-inflated Generalized Linear Model.

Results

Plasma metabolome in parallel to promotion of tumor development comprises a clearly distinct profile in Apc^Min/+ mice vs. wild type littermates, which is further altered by high-fat diet. Further, functional metabolomics pathway and network analyses in Apc^Min/+ mice on high-fat diet revealed associations between polyp formation and plasma metabolic compounds including those involved in amino-acids metabolism as well as nicotinamide and hippuric acid metabolic pathways. Finally, we also show changes in liver acyl-CoA profiles, which may result from a combination of Apc^Min/+-mediated tumor progression and high fat diet. The biological significance of these findings is discussed in the context of intestinal cancer progression.

Conclusions

These studies show that high-throughput metabolomics combined with appropriate statistical modeling and large scale functional approaches can be used to monitor and infer changes and interactions in the metabolome and genome of the host under controlled experimental conditions. Further these studies demonstrate the impact of diet on metabolic pathways and its relation to intestinal cancer progression. Based on our results, metabolic signatures and metabolic pathways of polyposis and intestinal carcinoma have been identified, which may serve as useful targets for the development of therapeutic interventions.

Metabolomic Analysis of Liver Tissue from the VX2 Rabbit Model of Secondary Liver Tumors

Purpose. The incidence of liver neoplasms is rising in USA. The purpose of this study was to determine metabolic profiles of liver tissue during early cancer development. Methods. We used the rabbit VX2 model of liver tumors (LT) and a control group consisting of sham animals implanted with Gelfoam into their livers (LG). After two weeks from implantation, liver tissue from lobes with and without tumor was obtained from experimental animals (LT+/LT−) as well as liver tissue from controls (LG+/LG−). Peaks obtained by Gas Chromatography-Mass Spectrometry were subjected to identification. 56 metabolites were identified and their profiles compared between groups using principal component analysis (PCA) and a mixed-effect two-way ANOVA model. Results. Animals recovered from surgery uneventfully. Analyses identified a metabolite profile that significantly differs in experimental conditions after controlling the False Discovery Rate (FDR). 16 metabolites concentrations differed significantly when comparing samples from (LT+/LT−) to samples from (LG+/LG−) livers. A significant difference was also shown in 20 metabolites when comparing samples from (LT+) liver lobes to samples from (LT−) liver lobes. Conclusion. Normal liver tissue harboring malignancy had a distinct metabolic signature. The role of metabolic profiles on liver biopsies for the detection of early liver cancer remains to be determined.

¹H NMR-based metabolic profiling of human rectal cancer tissue

Background

Rectal cancer is one of the most prevalent tumor types. Understanding the metabolic profile of rectal cancer is important for developing therapeutic approaches and molecular diagnosis.

Methods

Here, we report a metabonomics profiling of tissue samples on a large cohort of human rectal cancer subjects (n = 127) and normal controls (n = 43) using ¹H nuclear magnetic resonance (¹H NMR) based metabonomics assay, which is a highly sensitive and non-destructive method for the biomarker identification in biological systems. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA) were applied to analyze the ¹H-NMR profiling data to identify the distinguishing metabolites of rectal cancer.

Results

Excellent separation was obtained and distinguishing metabolites were observed among the different stages of rectal cancer tissues (stage I = 35; stage II = 37; stage III = 37 and stage IV = 18) and normal controls. A total of 38 differential metabolites were identified, 16 of which were closely correlated with the stage of rectal cancer. The up-regulation of 10 metabolites, including lactate, threonine, acetate, glutathione, uracil, succinate, serine, formate, lysine and tyrosine, were detected in the cancer tissues. On the other hand, 6 metabolites, including myo-inositol, taurine, phosphocreatine, creatine, betaine and dimethylglycine were decreased in cancer tissues. These modified metabolites revealed disturbance of energy, amino acids, ketone body and choline metabolism, which may be correlated with the progression of human rectal cancer.

Conclusion

Our findings firstly identify the distinguishing metabolites in different stages of rectal cancer tissues, indicating possibility of the attribution of metabolites disturbance to the progression of rectal cancer. The altered metabolites may be as potential biomarkers, which would provide a promising molecular diagnostic approach for clinical diagnosis of human rectal cancer. The role and underlying mechanism of metabolites in rectal cancer progression are worth being further investigated.

Investigation of metabolite alteration in dimethylnitrosamine-induced liver fibrosis by GC-MS

BACKGROUND

A metabolomic study of biomarkers associated with dimethylnitrosamine (DMN)-induced hepatic fibrosis in Sprague-Dawley rats was performed using GC-MS. The clinical chemistry of the collected blood and the histopathology of excised liver samples were examined, and urine samples were prepared by solvent extraction.

RESULTS

Through pattern analysis, the DMN-treated group was divided into two subgroups based on the aspartate aminotransferase (AST) levels compared with the control, a moderately higher group (DMN subgroup A) and a significantly higher group (DMN subgroup B). Uric acid, orotic acid, N-phenylacetylglycine and glutaric acid were biomarkers for DMN subgroup A, aminomalonic acid was a biomarker for DMN subgroup B, and arabitol level distinguished control versus DMN treatment regardless of AST level.

CONCLUSION

This study suggests that the identification and profiling of AST level-related metabolites may be useful as a diagnostic tool and for the study of the mechanism of liver fibrosis induced by DMN.

Targeted metabolic profiling of hepatocellular carcinoma and hepatitis C using LC-MS/MS

Hepatitis C virus (HCV) infection of the liver is a global health problem and a major risk factor for the development of hepatocellular carcinoma (HCC). Sensitive methods are needed for the improved and earlier detection of HCC, which would provide better therapy options. Metabolic profiling of the high-risk population (HCV patients) and those with HCC provides insights into the process of liver carcinogenesis and possible biomarkers for earlier cancer detection. Seventy-three blood metabolites were quantitatively profiled in HCC (n = 30) and cirrhotic HCV (n = 22) patients using a targeted approach based on LC-MS/MS. Sixteen of 73 targeted metabolites differed significantly (p < 0.05) and their levels varied up to a factor of 3.3 between HCC and HCV. Four of these 16 metabolites (methionine, 5-hydroxymethyl-2'-deoxyuridine, N2,N2-dimethylguanosine, and uric acid) that showed the lowest p values were used to develop and internally validate a classification model using partial least squares discriminant analysis. The model exhibited high classification accuracy for distinguishing the two groups with sensitivity, specificity, and area under the receiver operating characteristic curve of 97%, 95%, and 0.98, respectively. A number of perturbed metabolic pathways, including amino acid, purine, and nucleotide metabolism, were identified based on the 16 biomarker candidates. These results provide a promising methodology to distinguish cirrhotic HCV patients, who are at high risk to develop HCC, from those who have already progressed to HCC. The results also provide insights into the altered metabolism between HCC and HCV.

Transmission mode direct analysis in real time mass spectrometry for fast untargeted metabolic fingerprinting

RATIONALE

Untargeted metabolic fingerprinting is a discovery tool to better understand biochemical processes involved in detecting and characterizing disease states and responses to environmental stressors. Although current mass spectrometric (MS) methods are very powerful, there is a clear need for more rapid, high-throughput MS approaches for metabolomics studies.

METHODS

A rapid metabolic fingerprinting method for human blood sera that utilizes a new transmission mode direct analysis in real time (TM-DART) sampling technique coupled with quadrupole time of flight mass spectrometry (QTOFMS) is presented. In this approach, the sample is deposited directly on a stainless steel mesh that is held in the ionization region by a custom-built module. As a result, the DART plasma gas stream interacts with the sample in a flow-through fashion, which maximizes the interaction between the sample and ionizing species and minimizes variance in sample positioning.

RESULTS

The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. Ramping the ionizing gas desorption temperature further enhanced analysis by adding a simple separation dimension to this ambient approach. In terms of reproducibility, TM-DART compared favorably with traditional probe mode (PM-) DART analysis, with coefficients of variation as low as 16%. The longer-lasting TM-DART signals enabled the acquisition of metabolite full scan and product ion accurate mass spectra in a single experiment, resulting in greater confidence in metabolite identification.

CONCLUSIONS

TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera.

Applications of metabolomics in cancer research

The first discovery of metabolic changes in cancer occurred almost a century ago. While the genetic underpinnings of cancer have dominated its study since then, altered metabolism has recently been acknowledged as a key hallmark of cancer and metabolism-focused research has received renewed attention. The emerging field of metabolomics – which attempts to profile all metabolites within a cell or biological system – is now being used to analyze cancer metabolism on a system-wide scale, painting a broad picture of the altered pathways and their interactions with each other. While a large fraction of cancer metabolomics research is focused on finding diagnostic biomarkers, metabolomics is also being used to obtain more fundamental mechanistic insight into cancer and carcinogenesis. Applications of metabolomics are also emerging in areas such as tumor staging and assessment of treatment efficacy. This review summarizes contributions that metabolomics has made in cancer research and presents the current challenges and potential future directions within the field.

Response to dietary L-glutamine supplementation in weaned piglets: a serum metabolomic comparison and hepatic metabolic regulation analysis

A novel metabolomic method based on gas chromatography-mass spectrometry was applied to investigate serum metabolites in response to dietary Gln supplementation in piglets. Sixteen, 21-d-old pigs were weaned and assigned randomly to 2 isonitrogenous diets: 1) Gln diet, which contained 1% L-Gln (as-fed basis), and 2) control diet, which contained L-Ala to make this diet isonitrogenous with the Gln diet. Serum samples were collected to characterize metabolites after a 30-d treatment. in addition, 4 liver samples per treatment were collected to examine enzyme activity and gene expression involved in metabolic regulation. Results indicated that 12 metabolites were altered (P < 0.05) by Gln treatment, including carbohydrates, AA, and fatty acids. A leave-one-out cross validation of random forest analysis indicated that Pro was most important among the 12 metabolites. Thus, these data demonstrate that the control and Gln-supplemented pigs differed (P < 0.05) in terms of metabolism of carbohydrates, Pro, Tyr, and glycerophospholipids. Principal component analysis yielded separate clusters of profiles between the Gln and control groups. Metabolic enzyme activities of Ala aminotransferase and hexokinase increased by 26.8% (P = 0.026) and 26.2% (P = 0.004) in the liver of Gln-supplemented pigs vs. control, respectively, whereas pyruvate kinase (PK) activity decreased by 29.1% (P = 0.001). The gene expression of PK in the liver decreased by 66.1% (P = 0.034) by Gln treatment for 30 d. No differences were observed for the mRNA abundance of mammalian target of rapamycin and PPARγ. On the basis of these data, Gln treatment affected carbohydrate, lipid, and AA metabolism in the whole body of the early weaned piglets. These findings provide insight into specific metabolic pathways and lay the groundwork for the complex metabolic alteration in response to dietary Gln supplementation of pigs.

Power of metabolomics in diagnosis and biomarker discovery of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the commonest primary hepatic malignancy and the third most common cause of cancer-related death worldwide. Incidence remains highest in the developing world and is steadily increasing across the developed world. Current diagnostic modalities, of ultrasound and α-fetoprotein, are expensive and lack sensitivity in tumor detection. Because of its asymptomatic nature, HCC is usually diagnosed at late and advanced stages, for which there are no effective therapies. Thus, biomarkers for early detection and molecular targets for treating HCC are urgently needed. Emerging high-throughput metabolomics technologies have been widely applied, aiming at the discovery of candidate biomarkers for cancer staging, prediction of recurrence and prognosis, and treatment selection. Metabolic profiles, which are affected by many physiological and pathological processes, may provide further insight into the metabolic consequences of this severe liver disease. Small-molecule metabolites have an important role in biological systems and represent attractive candidates to understand HCC phenotypes. The power of metabolomics allows an unparalleled opportunity to query the molecular mechanisms of HCC. This technique-driven review aims to demystify the metabolomics pathway, while also illustrating the potential of this technique, with recent examples of its application in HCC.

Fasting serum lipid and dehydroepiandrosterone sulfate as important metabolites for detecting isolated postchallenge diabetes: serum metabolomics via ultra-high-performance LC-MS

BACKGROUND

Isolated postchallenge diabetes (IPD), a subtype of type 2 diabetes mellitus (T2DM) defined as 2-h postprandial plasma glucose ≥ 200 mg/dL (≥ 11.1 mmol/L) and fasting plasma glucose (FPG) <108 mg/dL (<6.0 mmol/L), is often overlooked during screening for diabetes on the basis of FPG concentrations. A key challenge is early identification of IPD by the use of fasting serum, which is critical for large-scale diabetes screening.

METHODS

We applied a nontargeted metabolomic approach using ultra-high-performance liquid chromatography-quadrupole TOF-mass spectrometry (UPLC-QTOF-MS) to analyze serum samples from 51 patients with IPD, 52 with newly diagnosed T2DM, and 49 healthy individuals. We processed metabolite profiles by multivariate analysis to identify potential metabolites, which were further confirmed by tandem MS (MS/MS). We also used GC-MS and ELISA methods to detect potentially important metabolites. A number of independent samples were selected to validate the identified candidates.

RESULTS

We selected 15 metabolites with a view to distinguishing patients with IPD, whereas 11 were identified with an authentic standard. The selected metabolites included linoleic acid, oleic acid, phospholipids, and dehydroepiandrosterone sulfate (DHEA-S). In IPD samples, significantly higher linoleic and oleic acid (P < 0.001) and lower DHEA-S (P < 0.001) concentrations were observed, compared with controls. The area under the curve from a combination of linoleic acid, oleic acid, and DHEA-S in the validation study was 0.849 for the IPD group.

CONCLUSIONS

The current study provides useful information to bridge the gaps in our understanding of the metabolic alterations associated with IPD and might facilitate the characterization of patients with IPD by the use of fasting serum.

1H-NMR based metabonomic profiling of human esophageal cancer tissue

BACKGROUND

The biomarker identification of human esophageal cancer is critical for its early diagnosis and therapeutic approaches that will significantly improve patient survival. Specially, those that involves in progression of disease would be helpful to mechanism research.

METHODS

In the present study, we investigated the distinguishing metabolites in human esophageal cancer tissues (n = 89) and normal esophageal mucosae (n = 26) using a (1)H nuclear magnetic resonance ((1)H-NMR) based assay, which is a highly sensitive and non-destructive method for biomarker identification in biological systems. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) were applied to analyse (1)H-NMR profiling data to identify potential biomarkers.

RESULTS

The constructed OPLS-DA model achieved an excellent separation of the esophageal cancer tissues and normal mucosae. Excellent separation was obtained between the different stages of esophageal cancer tissues (stage II = 28; stage III = 45 and stage IV = 16) and normal mucosae. A total of 45 metabolites were identified, and 12 of them were closely correlated with the stage of esophageal cancer. The downregulation of glucose, AMP and NAD, upregulation of formate indicated the large energy requirement due to accelerated cell proliferation in esophageal cancer. The increases in acetate, short-chain fatty acid and GABA in esophageal cancer tissue revealed the activation of fatty acids metabolism, which could satisfy the need for cellular membrane formation. Other modified metabolites were involved in choline metabolic pathway, including creatinine, creatine, DMG, DMA and TMA. These 12 metabolites, which are involved in energy, fatty acids and choline metabolism, may be associated with the progression of human esophageal cancer.

CONCLUSION

Our findings firstly identify the distinguishing metabolites in different stages of esophageal cancer tissues, indicating the attribution of metabolites disturbance to the progression of esophageal cancer. The potential biomarkers provide a promising molecular diagnostic approach for clinical diagnosis of human esophageal cancer and a new direction for the mechanism study.

Response to weaning and dietary L-glutamine supplementation: metabolomic analysis in piglets by gas chromatography/mass spectrometry

A novel metabolomic method based on gas chromatography/mass spectrometry (GC-MS) was applied to determine the metabolites in the serum of piglets in response to weaning and dietary L-glutamine (Gln) supplementation. Thirty-six 21-d-old piglets were randomly assigned into three groups. One group continued to suckle from the sows (suckling group), whereas the other two groups were weaned and their diets were supplemented with 1% (w/w) Gln or isonitrogenous L-alanine, respectively, representing Gln group or control group. Serum samples were collected to characterize metabolites after a 7-d treatment. Results showed that twenty metabolites were down-regulated significantly (P<0.05) in control piglets compared with suckling ones. These data demonstrated that early weaning causes a wide range of metabolic changes across arginine and proline metabolism, aminosugar and nucleotide metabolism, galactose metabolism, glycerophospholipid metabolism, biosynthesis of unsaturated fatty acid, and fatty acid metabolism. Dietary Gln supplementation increased the levels of creatinine, D-xylose, 2-hydroxybutyric acid, palmitelaidic acid, and α-L-galactofuranose (P<0.05) in early weaned piglets, and were involved in the arginine and proline metabolism, carbohydrate metabolism, and fatty acid metabolism. A leave-one-out cross-validation of random forest analysis indicated that creatinine was the most important metabolite among the three groups. Notably, the concentration of creatinine in control piglets was decreased (P=0.00001) compared to the suckling piglets, and increased (P=0.0003) in Gln-supplemented piglets. A correlation network for weaned and suckling piglets revealed that early weaning changed the metabolic pathways, leading to the abnormality of carbohydrate metabolism, amino acid metabolism, and lipid metabolism, which could be partially improved by dietary Gln supplementation. These findings provide fresh insight into the complex metabolic changes in response to early weaning and dietary Gln supplementation in piglets.

Differentiating hepatocellular carcinoma from hepatitis C using metabolite profiling

Hepatocellular carcinoma (HCC) accounts for most liver cancer cases worldwide. Contraction of the hepatitis C virus (HCV) is considered a major risk factor for liver cancer. In order to identify the risk of cancer, metabolic profiling of serum samples from patients with HCC (n=40) and HCV (n=22) was performed by ¹H nuclear magnetic resonance spectroscopy. Multivariate statistical analysis showed a distinct separation of the two patient cohorts, indicating a distinct metabolic difference between HCC and HCV patient groups based on signals from lipids and other individual metabolites. Univariate analysis showed that three metabolites (choline, valine and creatinine) were significantly altered in HCC. A PLS-DA model based on these three metabolites showed a sensitivity of 80%, specificity of 71% and an area under the receiver operating curve of 0.83, outperforming the clinical marker alpha-fetoprotein (AFP). The robustness of the model was tested using Monte-Carlo cross validation (MCCV). This study showed that metabolite profiling could provide an alternative approach for HCC screening in HCV patients, many of whom have high risk for developing liver cancer.

Recent advances in metabolomics in oncology

The burden of cancer is growing worldwide and with it a more desperate need for better tools to detect, diagnose and monitor the disease is required. It is well recognized that cancer cells are characterized by distinct metabolic perturbations. The metabolomics approach involves the comprehensive profiling of the full complement of low MW compounds in a biological system. By applying advanced analytical and statistical tools, the 'metabolome' is mined for biomarkers that are associated with the state of cancer. This review presents an introduction to the main analytical platforms used in metabolomics analyses, such as NMR spectroscopy and MS, as well as the statistical tools used to mine these datasets. The discussion focuses on 'state-of-the-art' investigations on the four cancer types that have received the most study by metabolomics, namely breast, prostate, colorectal and liver cancer.

Diagnosis of gastroenterological diseases by metabolome analysis using gas chromatography-mass spectrometry

Recently, metabolome analysis has been increasingly applied to biomarker detection and disease diagnosis in medical studies. Metabolome analysis is a strategy for studying the characteristics and interactions of low molecular weight metabolites under a specific set of conditions and is performed using mass spectrometry and nuclear magnetic resonance spectroscopy. There is a strong possibility that changes in metabolite levels reflect the functional status of a cell because alterations in their levels occur downstream of DNA, RNA, and protein. Therefore, the metabolite profile of a cell is more likely to represent the current status of a cell than DNA, RNA, or protein. Thus, owing to the rapid development of mass spectrometry analytical techniques metabolome analysis is becoming an important experimental method in life sciences including the medical field. Here, we describe metabolome analysis using liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry (GC-MS), capillary electrophoresis-mass spectrometry, and matrix assisted laser desorption ionization-mass spectrometry. Then, the findings of studies about GC-MS-based metabolome analysis of gastroenterological diseases are summarized, and our research results are also introduced. Finally, we discuss the realization of disease diagnosis by metabolome analysis. The development of metabolome analysis using mass spectrometry will aid the discovery of novel biomarkers, hopefully leading to the early detection of various diseases.