1H NMR-based metabonomic and pattern recognition analysis for detection of oral squamous cell carcinoma

Metabolomics of head and neck cancer in biofluids: an integrative systematic review

INTRODUCTION

Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC.

OBJECTIVES

To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine.

METHODS

A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine).

RESULTS

Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented.

CONCLUSION

The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

Unsupervised Hierarchical Clustering of Head and Neck Cancer Patients by Pre-Treatment Plasma Metabolomics Creates Prognostic Metabolic Subtypes

There is growing evidence that the metabolism is deeply intertwined with head and neck squamous cell carcinoma (HNSCC) progression and survival but little is known about circulating metabolite patterns and their clinical potential. We performed unsupervised hierarchical clustering of 209 HNSCC patients via pre-treatment plasma metabolomics to identify metabolic subtypes. We annotated the subtypes via pathway enrichment analysis and investigated their association with overall and progression-free survival. We stratified the survival analyses by smoking history. High-resolution metabolomics extracted 186 laboratory-confirmed metabolites. The optimal model created two patient clusters, of subtypes A and B, corresponding to 41% and 59% of the study population, respectively. Fatty acid biosynthesis, acetyl-CoA transport, arginine and proline, as well as the galactose metabolism pathways differentiated the subtypes. Relative to subtype B, subtype A patients experienced significantly worse overall and progression-free survival but only among ever-smokers. The estimated three-year overall survival was 61% for subtype A and 86% for subtype B; log-rank p = 0.001. The association with survival was independent of HPV status and other HNSCC risk factors (adjusted hazard ratio = 3.58, 95% CI: 1.46, 8.78). Our findings suggest that a non-invasive metabolomic biomarker would add crucial information to clinical risk stratification and raise translational research questions about testing such a biomarker in clinical trials.

Plasma metabolomics of oral squamous cell carcinomas based on NMR and MS approaches provides biomarker identification and survival prediction

Metabolomics has proven to be an important omics approach to understand the molecular pathways underlying the tumour phenotype and to identify new clinically useful markers. The literature on cancer has illustrated the potential of this approach as a diagnostic and prognostic tool. The present study aimed to analyse the plasma metabolic profile of patients with oral squamous cell carcinoma (OSCC) and controls and to compare patients with metastatic and primary tumours at different stages and subsites using nuclear magnetic resonance and mass spectrometry. To our knowledge, this is the only report that compared patients at different stages and subsites and replicates collected in diverse institutions at different times using these methodologies. Our results showed a plasma metabolic OSCC profile suggestive of abnormal ketogenesis, lipogenesis and energy metabolism, which is already present in early phases but is more evident in advanced stages of the disease. Reduced levels of several metabolites were also associated with an unfavorable prognosis. The observed metabolomic alterations may contribute to inflammation, immune response inhibition and tumour growth, and may be explained by four nonexclusive views-differential synthesis, uptake, release, and degradation of metabolites. The interpretation that assimilates these views is the cross talk between neoplastic and normal cells in the tumour microenvironment or in more distant anatomical sites, connected by biofluids, signalling molecules and vesicles. Additional population samples to evaluate the details of these molecular processes may lead to the discovery of new biomarkers and novel strategies for OSCC prevention and treatment.

Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part II: Applications to the Diagnosis and Prognostic Monitoring of Oral and Systemic Cancers

Human saliva offers many advantages over other biofluids regarding its use and value as a bioanalytical medium for the identification and prognostic monitoring of human diseases, mainly because its collection is largely non-invasive, is relatively cheap, and does not require any major clinical supervision, nor supervisory input. Indeed, participants donating this biofluid for such purposes, including the identification, validation and quantification of surrogate biomarkers, may easily self-collect such samples in their homes following the provision of full collection details to them by researchers. In this report, the authors have focused on the applications of metabolomics technologies to the diagnosis and progressive severity monitoring of human cancer conditions, firstly oral cancers (e.g., oral cavity squamous cell carcinoma), and secondly extra-oral (systemic) cancers such as lung, breast and prostate cancers. For each publication reviewed, the authors provide a detailed evaluation and critical appraisal of the experimental design, sample size, ease of sample collection (usually but not exclusively as whole mouth saliva (WMS)), their transport, length of storage and preparation for analysis. Moreover, recommended protocols for the optimisation of NMR pulse sequences for analysis, along with the application of methods and techniques for verifying and resonance assignments and validating the quantification of biomolecules responsible, are critically considered. In view of the authors’ specialisms and research interests, the majority of these investigations were conducted using NMR-based metabolomics techniques. The extension of these studies to determinations of metabolic pathways which have been pathologically disturbed in these diseases is also assessed here and reviewed. Where available, data for the monitoring of patients’ responses to chemotherapeutic treatments, and in one case, radiotherapy, are also evaluated herein. Additionally, a novel case study featured evaluates the molecular nature, levels and diagnostic potential of ¹H NMR-detectable salivary ‘acute-phase’ glycoprotein carbohydrate side chains, and/or their monomeric saccharide derivatives, as biomarkers for cancer and inflammatory conditions.

Pretreatment Glasgow Prognostic Score Correlated with Serum Histidine Level and Three-Year Mortality of Patients with Locally Advanced Head and Neck Squamous Cell Carcinoma and Optimal Performance Status

Few prospective cohort trials have investigted the effect of pretreatment nutritional and inflammatory status on the clinical outcome of patients with cancer and optimal performance status and assessed the interplay between nutrition, inflammation, body composition, and circulating metabolites before treatment. Here, 50 patients with locally advanced head and neck squamous cell carcinoma (LAHNSCC) and Eastern Cooperative Oncology Group performance status (ECOG PS) ≤ 2 were prospectively recruited along with 43 healthy participants. Before concurrent chemoradiotherapy, compared with healthy controls, the cancer group showed lower levels of histidine, leucine, and phenylalanine and had low values in anthropometric and body composition measurements; however, the group displayed higher ornithine levels, more malnutrition, and severe inflammation. Pretreatment advanced Glasgow prognostic score (1 and 2) status was the sole prognostic factor for 3-year mortality rate and was associated with age and serum histidine levels in patients with cancer. Thus, even at the same tumor stage and ECOG PS, patients with LAHNSCC, poor nutrition, and high inflammation severity at baseline may have inferior survival outcomes than those with adequate nutrition and low inflammation severity. Assessment of pretreatment nutritional and inflammatory status should be included in the enrollment criteria in future studies.

Treatment-Interval Changes in Serum Levels of Albumin and Histidine Correlated with Treatment Interruption in Patients with Locally Advanced Head and Neck Squamous Cell Carcinoma Completing Chemoradiotherapy under Recommended Calorie and Protein Provision

We investigated risk factors for treatment interruption (TI) in patients with locally advanced head and neck squamous-cell carcinoma (LAHNSCC) following concurrent chemoradiotherapy (CCRT), under the provision of recommended calorie and protein intake; we also evaluated the associations between clinicopathological variables, calorie and protein supply, nutrition–inflammation biomarkers (NIBs), total body composition change (TBC), and a four-serum-amino-acid metabolite panel (histidine, leucine, ornithine, and phenylalanine) among these patients. Patients with LAHNSCC who completed the entire planned CCRT course and received at least 25 kcal/kg/day and 1 g of protein/kg/day during CCRT were prospectively recruited. Clinicopathological variables, anthropometric data, blood NIBs, CCRT-related factors, TBC data, and metabolite panels before and after treatment were collected; 44 patients with LAHNSCC were enrolled. Nine patients (20.4%) experienced TIs. Patients with TIs experienced greater reductions in hemoglobin, serum levels of albumin, uric acid, histidine, and appendicular skeletal mass, and suffered from more grade 3/4 toxicities than those with no TI. Neither increased daily calorie supply (≥30 kcal/kg/day) nor feeding tube placement was correlated with TI. Multivariate analysis showed that treatment-interval changes in serum albumin and histidine levels, but not treatment toxicity, were independently associated with TI. Thus, changes in serum levels of albumin and histidine over the treatment course could cause TI in patients with LAHNSCC following CCRT.

Plasma metabolic phenotypes of HPV-associated vs smoking-associated head and neck cancer and patient survival

BACKGROUND

Metabolic differences between human papillomavirus (HPV)-associated head and neck squamous cell carcinoma (HNSCC) and smoking-associated HNSCC may partially explain differences in prognosis. The former relies on mitochondrial oxidative phosphorylation (OXPHOS) while the latter relies on glycolysis. These differences have not been studied in blood.

METHODS

We extracted metabolites using untargeted liquid chromatography high-resolution mass spectrometry from pretreatment plasma in a cohort of 55 HPV-associated and 82 smoking-associated HNSCC subjects. Metabolic pathway enrichment analysis of differentially expressed metabolites produced pathway-based signatures. Significant pathways (P<0.05) were reduced via principal components analysis and assessed with overall survival via Cox models. We classified each subject as glycolytic or OXPHOS phenotype and assessed it with survival.

RESULTS

Of 2,410 analyzed metabolites, 191 were differentially expressed. Relative to smoking-associated HNSCC, bile acid biosynthesis (P<0.0001) and octadecatrienoic acid beta-oxidation (P=0.01), were upregulated in HPV-associated HNSCC, while galactose metabolism (P=0.001) and vitamin B6 metabolism (P=0.01) were downregulated; the first two suggest an OXPHOS phenotype while the latter two suggest glycolytic. First principal components of bile acid biosynthesis (HR=0.52 per standard deviation, 95% CI:0.38-0.72, P<0.001) and octadecatrienoic acid beta-oxidation (HR=0.54 per sd, 95% CI:0.38-0.78, P<0.001) were significantly associated with overall survival independent of HPV and smoking. The glycolytic vs OXPHOS phenotype was also independently associated with survival (HR=3.17, 95% CI:1.07-9.35; P=0.04).

CONCLUSIONS

Plasma metabolites related to glycolysis and mitochondrial OXPHOS may be biomarkers of HNSCC patient prognosis independent of HPV or smoking. Future investigations should determine if they predict treatment efficacy.

IMPACT

Blood metabolomics may be a useful marker to aid HNSCC patient prognosis.

Serum metabolite signatures of epithelial ovarian cancer based on targeted metabolomics

BACKGROUND

Epithelial ovarian cancer (EOC) is a common gynecological cancer with high mortality rates. The main objective of this study was to investigate the serum amino acid and organic acid profiles to distinguish key metabolites for screening EOC patients.

METHODS

In total, 39 patients with EOC and 31 healthy controls were selected as the training set. Serum amino acid and organic acid profiles were determined using the targeted metabolomics approach. Metabolite profiles were processed via multivariate analysis to identify potential metabolites and construct a metabolic network. Finally, a test dataset derived from 29 patients and 28 healthy controls was constructed to validate the potential metabolites.

RESULTS

Distinct amino acid and organic acid profiles were obtained between EOC and healthy control groups. Methionine, glutamine, asparagine, glutamic acid and glycolic acid were identified as potential metabolites to distinguish EOC from control samples. The areas under the curve for methionine, glutamine, asparagine, glutamic acid and glycolic acid were 0.775, 0 778, 0.955, 0.874 and 0.897, respectively, in the validation study. Metabolic network analysis of the training set indicated key roles of alanine, aspartate and glutamate metabolism as well as D-glutamine and D-glutamate metabolism in the pathogenesis of EOC.

CONCLUSIONS

Amino acid and organic acid profiles may serve as potential screening tools for EOC. Data from this study provide useful information to bridge gaps in the understanding of the amino acid and organic acid alterations associated with epithelial ovarian cancer.

Metabolic landscape of oral squamous cell carcinoma

BACKGROUND

Head and neck cancers are the seventh most common type of cancer worldwide, with almost half of the cases affecting the oral cavity. Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, showing poor prognosis and high mortality. OSCC molecular pathogenesis is complex, resulting from a wide range of events that involve the interplay between genetic mutations and altered levels of transcripts, proteins, and metabolites. Metabolomics is a recently developed sub-area of omics sciences focused on the comprehensive analysis of small molecules involved in several biological pathways by high throughput technologies.

AIM OF REVIEW

This review summarizes and evaluates studies focused on the metabolomics analysis of OSCC and oral premalignant disorders to better interpret the complex process of oral carcinogenesis. Additionally, the metabolic biomarkers signatures identified so far are also included. Moreover, we discuss the limitations of these studies and make suggestions for future investigations.

KEY SCIENTIFIC CONCEPTS

Although many questions about the metabolic features of OSCC have already been answered in metabolomic studies, further validation and optimization are still required to translate these findings into clinical applications.

Multi‑organ assessment via a 9.4‑Tesla MRS evaluation of metabolites during the embryonic development of cleft palate induced by dexamethasone

The aim of the present study was to determine the association between maternal metabolism and development of the fetal palate, and to suggest a potential non‑invasive prenatal diagnostic method for fetal cleft palate (CP). Dexamethasone (DXM) was used to create a CP mouse model. A 9.4‑Tesla (T) magnetic resonance spectroscopy (MRS) imager was used to measure an array of metabolites in the maternal serum, placental tissue, amniotic fluid and fetal palates. Multivariate statistical analysis was performed using SIMCA‑P 14.1 software. Following DXM treatment, variations were detected in multiple metabolites in the female mice and their fetuses based on 9.4T MRS. It was indicated that in the experimental group during CP formation, leucine, valine, creatine, acetate and citrate levels in the palatal tissue were lower, whereas lactate, alanine, proline/inositol and glutamate‑containing metabolite levels were higher, compared with the levels in the control group. In placental tissue and amniotic fluid, succinate and choline levels were lower in the experimental group. The relative concentrations of cholesterol and lipids in palatal tissues from mice treated with DXM were higher compared with the concentrations in tissues from mice in the control group, with the exception of (CH2)n lipids. In the placental tissue, the alteration in cholesterol level exhibited the opposite trend. Lipid levels for the different lipid forms varied and most of them were unsaturated lipids.

Metabolomics study of oral cancers

BACKGROUND

Oral cancer is one of the most frequently occurring cancers. Metabolic reprogramming is an important hallmark of cancer. Metabolomics characterizes all the small molecules in a biological sample, and a complete set of small molecules in such sample is referred as metabolome. Nuclear magnetic resonance spectroscopy and mass spectrometry are two widely used techniques in metabolomics studies. Increasing evidence demonstrates that metabolomics techniques can be used to explore the metabolic signatures in oral cancer. Elucidation of metabolic alterations in oral cancer is also important for the understanding of its pathological mechanisms.

AIM OF REVIEW

In this paper, we summarize the latest progress of metabolomics study in oral cancer and provide the suggestions for the future studies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The metabolomics studies in saliva, serum, and tumor tissues revealed the existence of metabolic signatures in bio-fluids and tissues of oral cancer, and several tumor-specific metabolites identified in individual study could discriminate oral cancer from healthy controls or precancerous lesions, which are potential biomarkers for the screening or early diagnosis of oral cancer. Metabolomics study of oral cancers in the future should aim to establish a routine procedure with high sensitivity, profile intracellular metabolites to find out the metabolic characteristics of tumor cells, and investigate the mechanism behind metabolomic alterations and the metabolic response of cancer cells to chemotherapy.

NMR-based metabolomics to select a surgical method for treating papillary thyroid carcinoma

OBJECTIVE

This study aims to investigate differences in the metabolomic profiles of patients who received different surgeries for papillary thyroid carcinoma (PTC).

METHODS

Two surgical methods, i.e., unilateral and total thyroidectomy, were employed according to different disease conditions. Sera from patients who were treated with levothyroxine sodium tablets before and after surgery was analyzed with a Bruker 500 Hz nuclear magnetic resonance (NMR) spectrometer. Data were analyzed via principal component analysis (PCA) and partial least squares discriminate analysis (PLS-DA) with SIMCA-P+ 11.0 software, and metabolites were obtained and compared. The first and second principal components were selected from PCA, PLS-DA, and orthogonal partial least squares discriminate analysis (OPLS-DA). A p-value less than 0.05 was considered statistically significant.

RESULTS

There were significant differences in serum metabolomics before and after surgery. Compared with unilateral thyroidectomy, total thyroidectomy reversed some highly increased metabolite levels (e.g., taurine and betaine). More significant variations in abnormal metabolites were noted after total thyroidectomy than after unilateral thyroidectomy (e.g., alanine, choline, hippurate, and formic acid).

CONCLUSIONS

The choice of surgical method for PTC patients should be based not only on the tumor condition but also on the potential consequences of metabolic variations. Total thyroidectomy reversed some increased metabolite levels but led to accumulation of some other metabolites due to the loss of thyroid function; thus, metabolic disturbances caused by thyroid hormone deficiency should be prevented in advance.

Delineating metabolic dysfunction in cellular metabolism of oral submucous fibrosis using 1H nuclear magnetic resonance spectroscopy

OBJECTIVE

To delineate the metabolism involved in oral submucous fibrosis progression towards carcinogenesis by ¹H nuclear magnetic resonance spectroscopy.

METHODS

The proposed study was designed using ¹H-NMR by comparing the metabolites in the serum sample of oral submucous fibrosis (n = 20) compared to the normal group (n = 20) using ¹H nuclear magnetic resonance spectroscopy. Various statistical analysis like multivariate statistical analysis, Principle component analysis, Partial least squares Discriminant Analysis, Hierarchical cluster analysis was applied to analyze potential serum metabolites.

RESULTS

The results generated from the principle component analysis, partial least squares discriminant analysis and hierarchical cluster analysis are sufficient to distinguish between oral submucous fibrosis group and normal group. A total of 15 significant metabolites associated with main pathways were identified, which correlated with the progression of cancer. Up-regulation of glucose metabolism-related metabolites indicated the high energy demand due to enhanced cell division rate in the oral submucous fibrosis group. A significant increase in lipid metabolism-related metabolites revealed the reprogramming of the fatty acids metabolic pathway to fulfilling the need for cell membrane formation in cancer cells. On the other hand, metabolites related to choline phosphocholine, the metabolic pathway was also altered.

CONCLUSION

Our findings could identify the differentiating metabolites in the oral submucous fibrosis group. Significant alteration in metabolites in the oral submucous fibrosis group exhibited deregulation in metabolic events. The findings reported in the study can be beneficial to further explain the molecular aspects that lead to the progression of oral submucous fibrosis towards carcinogenesis.

Salivary metabolite profiling distinguishes patients with oral cavity squamous cell carcinoma from normal controls

Oral cavity squamous cell carcinoma (OCC) and oropharyngeal squamous cell carcinoma (OPC) are among the most common cancers worldwide and are associated with high mortality and morbidity. The purpose of this study is to identify potential biomarkers to distinguish OCC/OPC from normal controls and to distinguish OCC patients with and without nodal metastasis. We tested saliva samples from 101 OCC, 58 OPC, and 35 normal controls using four analytical platforms (NMR, targeted aqueous by LC-MS/MS, global aqueous and global lipidomics by LC-Q-TOF). Samples from OCC and normal controls were divided into discovery and validation sets. Using linear regression adjusting for age, sex, race and experimental batches, we found the levels of two metabolites (glycine and proline) to be significantly different between OCC and controls (FDR < 0.1 for both discovery and validation sets) but did not find any appreciable differences in metabolite levels between OPC and controls or between OCC with and without nodal metastasis. Four metabolites, including glycine, proline, citrulline, and ornithine were associated with early stage OCC in both discovery and validation sets. Further study is warranted to confirm these results in the development of salivary metabolites as diagnostic markers.

[Link between sortase A function and cariogenicity of Streptococcus mutans: a preliminary metabolomics analysis]

OBJECTIVE

This study intends to explore the mechanism underlying the support of sortase A (SrtA) of the cariogenicity of Streptococcus mutans (S. mutans).

METHODS

We performed a metabonomics study based on ¹H nuclear magnetic resonance spectroscopy (NMR), in which we compared the extracellular metabolites of wild-type S. mutans UA159 with those of its SrtA-deficient strain. Metabolite differences among strains were identified using a combination of principal component analysis and orthogonality partial least square discriminant analysis.

RESULTS

Several differences corresponding mostly to unknown metabolites were identified. Some amino acids such as leucine and valine (δ 0.92×10⁻⁶-1.20×10⁻⁶), lactic acid ( δ1.28×10⁻⁶), oxoglutaric acid (δ 3.00×10⁻⁶), and glycine (δ 3.60×10⁻⁶) differed among strains.

CONCLUSIONS

This work establishes the feasibility of using ¹H NMR-based metabonomics to provide leads for research into molecular factors that promote caries. The database of microbial metabolites should be also improved in further studies.

"Omics" in oral cancer: New approaches for biomarker discovery

OBJECTIVES

In this review paper, we explored the application of "omics" approaches in the study of oral cancer (OC). It will provide a better understanding of how "omics" approaches may lead to novel biomarker molecules or molecular signatures with potential value in clinical practice. A future direction of "omics"-driven research in OC is also discussed.

METHODS

Studies on "omics"-based approaches [genomics/proteomics/transcriptomics/metabolomics] were investigated for differentiating oral squamous cell carcinoma,oral sub-mucous fibrosis, oral leukoplakia, oral lichen planus, oral erythroplakia from normal cases. Electronic databases viz., PubMed, Springer, and Google Scholar were searched.

RESULTS

One eighty-one studies were included in this review. The review shows that the fields of genomics, transcriptomics, proteomics, and metabolomics-based marker identification have implemented advanced tools to screen early changes in DNA, RNA, protein, and metabolite expression in OC population.

CONCLUSIONS

It may be concluded that despite advances in OC therapy, symptomatic presentation occurs at an advanced stage, where various curative treatment options become very limited. A molecular level study is essential for detecting an OC biomarker at an early stage. Modern "Omics" strategies can potentially make a major contribution to meet this need.

Head and Neck Squamous Cell Carcinoma Metabolism Draws on Glutaminolysis, and Stemness Is Specifically Regulated by Glutaminolysis via Aldehyde Dehydrogenase

Cancer cells use alternate energetic pathways; however, cancer stem cell (CSC) metabolic energetic pathways are unknown. The purpose of this study was to define the metabolic characteristics of head and neck cancer at different points of its pathogenesis with a focus on its CSC compartment. UPLC-MS/MS-profiling and GC-MS-validation studies of human head and neck cancer tissue, saliva, and plasma were used in conjunction with in vitro and in vivo models to carry out this investigation. We identified metabolite biomarker panels that distinguish head and neck cancer from healthy controls, and confirmed involvement of glutamate and glutaminolysis. Glutaminase, which catalyzes glutamate formation from glutamine, and aldehyde dehydrogenase (ALDH), a stemness marker, were highly expressed in primary and metastatic head and neck cancer tissues, tumorspheres, and CSC versus controls. Exogenous glutamine induced stemness via glutaminase, whereas inhibiting glutaminase suppressed stemness in vitro and tumorigenesis in vivo. Head and neck CSC (CD44hi/ALDHhi) exhibited higher glutaminase, glutamate, and sphere levels than CD44lo/ALDHlo cells. Glutaminase drove transcriptional and translational ALDH expression, and glutamine directed even CD44lo/ALDHlo cells toward stemness. Glutaminolysis regulates tumorigenesis and CSC metabolism via ALDH. These findings indicate that glutamate is an important marker of cancer metabolism whose regulation via glutaminase works in concert with ALDH to mediate cancer stemness. Future analyses of glutaminolytic-ALDH driven mechanisms underlying tumorigenic transitions may help in the development of targeted therapies for head and neck cancer and its CSC compartment.

Metabolomics of Head and Neck Cancer: A Mini-Review

Metabolomics is used in systems biology to enhance the understanding of complex disease processes, such as cancer. Head and neck cancer (HNC) is an epithelial malignancy that arises in the upper aerodigestive tract and affects more than half a million people worldwide each year. Recently, significant effort has focused on integrating multiple “omics” technologies for oncological research. In particular, research has been focused on identifying tumor-specific metabolite profiles using different sample types (biological fluids, cells and tissues) and a variety of metabolomic platforms and technologies. With our current understanding of molecular abnormalities of HNC, the addition of metabolomic studies will enhance our knowledge of the pathogenesis of this disease and potentially aid in the development of novel strategies to prevent and treat HNC. In this review, we summarize the proposed hypotheses and conclusions from publications that reported findings on the metabolomics of HNC. In addition, we address the potential influence of host-microbe metabolomics in cancer. From a systems biology perspective, the integrative use of genomics, transcriptomics and proteomics will be extremely important for future translational metabolomic-based research discoveries.

Metabonomic profiles reveal dose-dependent effects of Bu-Shen-Gu-Chi-Wan on the serum in experimental periodontitis of rat model

ETHNO-PHARMACOLOGICAL RELEVANCE

Bu-Shen-Gu-Chi-Wan is a Chinese patent medicine for the treatment of chronic periodontitis. It has an anti-inflammatory function in experimental periodontitis and can improve alveolar bone remodeling.

AIM OF THE STUDY

This study aims to explore the effects of Bu-Shen-Gu-Chi-Wan on serum metabolism in a rat model of periodontitis using ¹H nuclear magnetic resonance (¹H NMR) based metabonomics.

MATERIALS AND METHODS

The model of experimental periodontitis in a rat was established by steel wire ligation, plus a high glucose diet and Porphyromonas gingivalis inoculation. When the models had been established, 6-week-old Sprague-Dawley female rats (n=31) were divided into 5 groups: high dose group (Group H), medium dose group (Group M), low dose group (Group L), periodontitis group (Group P) and healthy control group (Group N). Rats in Group H, M and L were given the Bu-Shen-Gu-Chi-Wan solution (0.8, 2 and 4g/kg of body weight) daily for 60 days. Rats in Group P and N were administered normal saline (10ml/kg of body weight) in the same period. All rats were sacrificed at the end of the study and serum samples were collected. The metabolites in the serum were analyzed using ¹H NMR in conjunction with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

RESULTS

The metabonomic profiles of five groups showed the clustering of individual dataset in every group, and the 1st principal component and the 2nd principal component could significantly differentiate the datasets of medium dose or high dose group from other groups. The chemical shift regions at δ 1.22ppm, 1.86ppm, 2.26ppm, 2.34ppm and 2.42ppm showed the most obvious differences among the five groups. The correspondent metabolites were high density lipoprotein, pyruvate/oxaloacetate, arginine and glutamine.

CONCLUSION

The effects of Bu-Shen-Gu-Chi-Wan on the rat serum metabolites were dose dependent. High density lipoprotein, pyruvate/oxaloacetate, arginine and glutamine may be the serum biomarkers of Bu-Shen-Gu-Chi-Wan.

Translational metabolomics in cancer research

Over the last decade there has been a bottleneck in the introduction of new validated cancer metabolic biomarkers into clinical practice. Unfortunately, there are no biomarkers with adequate sensitivity for the early detection of cancer, and there remain a reliance on cancer antigens for monitoring treatment. The need for new diagnostics has led to the exploration of untargeted metabolomics for discovery of early biomarkers of specific cancers and targeted metabolomics to elucidate mechanistic aspects of tumor progression. The successful translation of such strategies to the treatment of cancer would allow earlier intervention to improve survival. We have reviewed the methodology that is being used to achieve these goals together with recent advances in implementing translational metabolomics in cancer.

1H nuclear magnetic resonance-based extracellular metabolomic analysis of multidrug resistant Tca8113 oral squamous carcinoma cells

A major obstacle of successful chemotherapy is the development of multidrug resistance (MDR) in the cancer cells, which is difficult to reverse. Metabolomic analysis, an emerging approach that has been increasingly applied in various fields, is able to reflect the unique chemical fingerprints of specific cellular processes in an organism. The assessment of such metabolite changes can be used to identify novel therapeutic biomarkers. In the present study, ¹H nuclear magnetic resonance (NMR) spectroscopy was used to analyze the extracellular metabolomic spectrum of the Tca8113 oral squamous carcinoma cell line, in which MDR was induced using the carboplatin (CBP) and pingyangmycin (PYM) chemotherapy drugs in vitro. The data were analyzed using the principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods. The results demonstrated that the extracellular metabolomic spectrum of metabolites such as glutamate, glycerophosphoethanol amine, α-Glucose and β-Glucose for the drug-induced Tca8113 cells was significantly different from the parental Tca8113 cell line. A number of biochemicals were also significantly different between the groups based on their NMR spectra, with drug-resistant cells presenting relatively higher levels of acetate and lower levels of lactate. In addition, a significantly higher peak was observed at δ 3.35 ppm in the spectrum of the PYM-induced Tca8113 cells. Therefore, ¹H NMR-based metabolomic analysis has a high potential for monitoring the formation of MDR during clinical tumor chemotherapy in the future.

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.

Analysis of plasma metabolic biomarkers in the development of 4-nitroquinoline-1-oxide-induced oral carcinogenesis in rats

The aim of the present study was to identify time-dependent changes in the expression of metabolic biomarkers during the various stages of oral carcinogenesis to provide an insight into the sequential mechanism of oral cancer development. An ¹H nuclear magnetic resonance (NMR)-based metabolomics approach was used to analyze the blood plasma samples of Sprague-Dawley rats exhibiting various oral lesions induced by the administration of 4-nitroquinoline-1-oxide (4NQO) in drinking water. The ¹H NMR spectra were processed by principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) to determine the metabolic differences between the three developmental stages of oral mucosa cancer (health, oral leukoplakia [OLK] and oral squamous cell carcinoma [OSCC]). The variable importance in projection (VIP) score derived from the PLS-DA model was used to screen for important metabolites, whose significance was further verified through analysis of variance (ANOVA). Data from the present study indicated that 4NQO-induced rat oral carcinogenesis produced oral pre-neoplastic and neoplastic lesions and provided an effective model for analyzing sequential changes in the ¹H NMR spectra of rat blood plasma. The ¹H NMR-based metabolomics approach clearly differentiates between healthy, OLK and OSSC rats in the PCA and PLS-DA models. Furthermore, lactic acid, choline, glucose, proline, valine, isoleucine, aspartic acid and 2-hydroxybutyric acid demonstrated VIP>1 in the PLS-D model and P<0.05 with ANOVA. It was also identified that increases in lactic acid, choline and glucose, and decreases in proline, valine, isoleucine, aspartic acid and 2-hydroxybutyric acid may be relative to the characteristic mechanisms of oral carcinogenesis. Therefore, these plasma metabolites may serve as metabolic biomarkers in oral carcinogenesis and assist in the early diagnosis and preventive treatment of oral cancer.

Measurement of salivary metabolite biomarkers for early monitoring of oral cancer with ultra performance liquid chromatography-mass spectrometry

This study aimed to set-up an ultra performance liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS) method for the determination of salivary L-phenylalanine and L-leucine for early diagnosis of oral squamous cell carcinoma (OSCC). In addition, the diagnostic accuracy for both biomarkers was established by using receiver operating characteristic (ROC) analysis. Mean recoveries of l-phenylalanine and L-leucine ranged from 88.9 to 108.6% were obtained. Intra- and inter-day precision for both amino acids was less than 7%, with acceptable accuracy. Linear regression coefficients of both biomarkers were greater than 0.99. The diagnostic accuracy for both biomarkers was established by analyzing 60 samples from apparently healthy individuals and 30 samples from OSCC patients. Both potential biomarkers demonstrated significant differences in concentrations in distinguishing OSCC from control (P<0.05). As a single biomarker, L-leucine might have better predictive power in OSCC with T1-2 (early stage of OSCC including stage I and II), and L-phenylalanine might be used for screening and diagnosis of OSCC with T3-4 (advanced stage of OSCC including stage III and IV). The combination of L-phenylalanine and L-leucine will improve the sensitivity (92.3%) and specificity (91.7%) for early diagnosis of OSCC. The possibility of salivary metabolite biomarkers for OSCC diagnosis is successfully demonstrated in this study. This developed method shows advantages with non-invasive, simple, reliable, and also provides lower detection limits and excellent precision and accuracy. These non-invasive salivary biomarkers may lead to a simple clinical tool for the early diagnosis of OSCC.

Metabolomic approach to human brain spectroscopy identifies associations between clinical features and the frontal lobe metabolome in multiple sclerosis

Proton magnetic resonance spectroscopy ((1)H-MRS) is capable of noninvasively detecting metabolic changes that occur in the brain tissue in vivo. Its clinical utility has been limited so far, however, by analytic methods that focus on independently evaluated metabolites and require prior knowledge about which metabolites to examine. Here, we applied advanced computational methodologies from the field of metabolomics, specifically partial least squares discriminant analysis and orthogonal partial least squares, to in vivo (1)H-MRS from frontal lobe white matter of 27 patients with relapsing-remitting multiple sclerosis (RRMS) and 14 healthy controls. We chose RRMS, a chronic demyelinating disorder of the central nervous system, because its complex pathology and variable disease course make the need for reliable biomarkers of disease progression more pressing. We show that in vivo MRS data, when analyzed by multivariate statistical methods, can provide reliable, distinct profiles of MRS-detectable metabolites in different patient populations. Specifically, we find that brain tissue in RRMS patients deviates significantly in its metabolic profile from that of healthy controls, even though it appears normal by standard MRI techniques. We also identify, using statistical means, the metabolic signatures of certain clinical features common in RRMS, such as disability score, cognitive impairments, and response to stress. This approach to human in vivo MRS data should promote understanding of the specific metabolic changes accompanying disease pathogenesis, and could provide biomarkers of disease progression that would be useful in clinical trials.

Identification of plasma metabolomic profiling for diagnosis of esophageal squamous-cell carcinoma using an UPLC/TOF/MS platform

Epidemiological studies indicated that esophageal squamous-cell carcinoma (ESCC) is still one of the most common causes of cancer incidence in the world. Searching for valuable markers including circulating endogenous metabolites associated with the risk of esophageal cancer, is extremely important A comparative metabolomics study was performed by using ultraperformance liquid chromatography-electrospray ionization-accurate mass time-of-flight mass spectrometry to analyze 53 pairs of plasma samples from ESCC patients and healthy controls recruited in Huaian, China. The result identified a metabolomic profiling of plasma including 25 upregulated metabolites and five downregulated metabolites, for early diagnosis of ESCC. With a database-based verification protocol, 11 molecules were identified, and six upregulated molecules of interest in ESCC were found to belong to phospholipids as follows: phosphatidylserine, phosphatidic acid, phosphatidyl choline, phosphatidylinositol, phosphatidyl ethanolamine, and sphinganine 1-phosphate. Clinical estimation of metabolic biomarkers through hierarchical cluster analysis in plasma samples from 17 ESCC patients and 29 healthy volunteers indicated that the present metabolite profile could distinguish ESCC patients from healthy individuals. The cluster of aberrant expression of these metabolites in ESCC indicates the critical role of phospholipid metabolism in the oncogenesis of ESCC and suggests its potential ability to assess the risk of ESCC development in addition to currently used risk factors.

Metabolomics reveals differential levels of oral metabolites in HIV-infected patients: toward novel diagnostic targets

The objective of the current study was to characterize the profile of oral metabolites in HIV-infected patients using metabolomics. Oral wash samples were collected from 12 HIV-infected and 12 healthy individuals (matched for age, sex, and ethnicity), processed, and analyzed by metabolomics. We detected 198 identifiable and 85 nonidentifiable metabolites; 27 identifiable metabolites were differentially present (12 increased, 15 decreased) in HIV-infected patients. Elevated metabolites included p-cresol sulfate, nucleotides (e.g., allantoin), and amino acids (e.g., phenylalanine, tryptophan), whereas decreased oral metabolites included fucose, fumarate, and N-acetylglucosamine. Pathway network analysis revealed the largest multinode network in healthy versus HIV-infected patients to involve carbohydrate biosynthesis and degradation. HIV-infected patients on antiretroviral therapy (ART) showed the largest number (12) of statistically significant metabolite correlation differences compared with healthy controls. Interestingly, the oral phenlyalanine:tyrosine ratio increased in ART-naive HIV-infected patients (mean ± SEM = 2.58 ± 0.87) compared with healthy individuals (1.33 ± 0.10, p = 0.062) or ART-experienced patients (1.78 ± 0.30, p = 0.441). This is the first study to reveal differential levels of oral metabolites in HIV-infected patients compared withj healthy volunteers, and that oral phenlyalanine:tyrosine ratio may be a useful marker for noninvasive monitoring of the immune status during HIV infection.

Identification of potential biomarkers for ovarian cancer by urinary metabolomic profiling

To evaluate the application of urinary metabolomics on discovering potential biomarkers for epithelial ovarian cancer (EOC), urine samples from 40 preoperative EOC patients, 62 benign ovarian tumor (BOT) patients, and 54 healthy controls were collected and analyzed with ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). Good separations were obtained for EOC vs BOT, EOC vs healthy controls analyzed by partial least-squares discriminant analysis, or principal component analysis. Twenty-two ascertained metabolomic biomarkers were found to be disturbed in several metabolic pathways among EOC patients, including nucleotide metabolism (pseudouridine, N4-acetylcytidine), histidine metabolism (L-histidine, imidazol-5-yl-pyruvate), tryptophan metabolism (3-indolelactic acid), and mucin metabolism (3'-sialyllactose and 3-sialyl-N-acetyllactosamine). In addition, the concentrations of some urinary metabolites of 18 postoperative EOC patients among the 40 EOC patients changed significantly compared with those of their preoperative condition, and four of them suggested recovery tendency toward normal level after surgical operation, including N4-acetylcytidine, pseudouridine, urate-3-ribonucleoside, and succinic acid. These metabolites would be highly postulated to be associated with EOC. In conclusion, our study demonstrated that urinary metabolomics analysis by UPLC-QTOF/MS, performed in a minimally noninvasive and convenient manner, possessed great potential in biomarker discovery for EOC.

Human metabolic profiles are stably controlled by genetic and environmental variation

A comprehensive variation map of the human metabolome identifies genetic and stable-environmental sources as major drivers of metabolite concentrations. The data suggest that sample sizes of a few thousand are sufficient to detect metabolite biomarkers predictive of disease.

We designed a longitudinal twin study to characterize the genetic, stable-environmental, and longitudinally fluctuating influences on metabolite concentrations in two human biofluids—urine and plasma—focusing specifically on the representative subset of metabolites detectable by ¹H nuclear magnetic resonance (¹H NMR) spectroscopy.

We identified widespread genetic and stable-environmental influences on the (urine and plasma) metabolomes, with (30 and 42%) attributable on average to familial sources, and (47 and 60%) attributable to longitudinally stable sources.

Ten of the metabolites annotated in the study are estimated to have >60% familial contribution to their variation in concentration.

Our findings have implications for the design and interpretation of ¹H NMR-based molecular epidemiology studies. On the basis of the stable component of variation quantified in the current paper, we specified a model of disease association under which we inferred that sample sizes of a few thousand should be sufficient to detect disease-predictive metabolite biomarkers.

Metabolites are small molecules involved in biochemical processes in living systems. Their concentration in biofluids, such as urine and plasma, can offer insights into the functional status of biological pathways within an organism, and reflect input from multiple levels of biological organization—genetic, epigenetic, transcriptomic, and proteomic—as well as from environmental and lifestyle factors. Metabolite levels have the potential to indicate a broad variety of deviations from the ‘normal' physiological state, such as those that accompany a disease, or an increased susceptibility to disease. A number of recent studies have demonstrated that metabolite concentrations can be used to diagnose disease states accurately. A more ambitious goal is to identify metabolite biomarkers that are predictive of future disease onset, providing the possibility of intervention in susceptible individuals.

If an extreme concentration of a metabolite is to serve as an indicator of disease status, it is usually important to know the distribution of metabolite levels among healthy individuals. It is also useful to characterize the sources of that observed variation in the healthy population. A proportion of that variation—the heritable component—is attributable to genetic differences between individuals, potentially at many genetic loci. An effective, molecular indicator of a heritable, complex disease is likely to have a substantive heritable component. Non-heritable biological variation in metabolite concentrations can arise from a variety of environmental influences, such as dietary intake, lifestyle choices, general physical condition, composition of gut microflora, and use of medication. Variation across a population in stable-environmental influences leads to long-term differences between individuals in their baseline metabolite levels. Dynamic environmental pressures lead to short-term fluctuations within an individual about their baseline level. A metabolite whose concentration changes substantially in response to short-term pressures is relatively unlikely to offer long-term prediction of disease. In summary, the potential suitability of a metabolite to predict disease is reflected by the relative contributions of heritable and stable/unstable-environmental factors to its variation in concentration across the healthy population.

Studies involving twins are an established technique for quantifying the heritable component of phenotypes in human populations. Monozygotic (MZ) twins share the same DNA genome-wide, while dizygotic (DZ) twins share approximately half their inherited DNA, as do ordinary siblings. By comparing the average extent of phenotypic concordance within MZ pairs to that within DZ pairs, it is possible to quantify the heritability of a trait, and also to quantify the familiality, which refers to the combination of heritable and common-environmental effects (i.e., environmental influences shared by twins in a pair). In addition to incorporating twins into the study design, it is useful to quantify the phenotype in some individuals at multiple time points. The longitudinal aspect of such a study allows environmental effects to be decomposed into those that affect the phenotype over the short term and those that exert stable influence.

For the current study, urine and blood samples were collected from a cohort of MZ and DZ twins, with some twins donating samples on two occasions several months apart. Samples were analysed by ¹H nuclear magnetic resonance (¹H NMR) spectroscopy—an untargeted, discovery-driven technique for quantifying metabolite concentrations in biological samples. The application of ¹H NMR to a biological sample creates a spectrum, made up of multiple peaks, with each peak's size quantitatively representing the concentration of its corresponding hydrogen-containing metabolite.

In each biological sample in our study, we extracted a full set of peaks, and thereby quantified the concentrations of all common plasma and urine metabolites detectable by ¹H NMR. We developed bespoke statistical methods to decompose the observed concentration variation at each metabolite peak into that originating from familial, individual-environmental, and unstable-environmental sources.

We quantified the variability landscape across all common metabolite peaks in the urine and plasma ¹H NMR metabolomes. We annotated a subset of peaks with a total of 65 metabolites; the variance decompositions for these are shown in Figure 1. Ten metabolites' concentrations were estimated to have familial contributions in excess of 60%. The average proportion of stable variation across all extracted metabolite peaks was estimated to be 47% in the urine samples and 60% in the plasma samples; the average estimated familiality was 30% for urine and 42% for plasma. These results comprise the first quantitative variation map of the ¹H NMR metabolome. The identification and quantification of substantive widespread stability provides support for the use of these biofluids in molecular epidemiology studies. On the basis of our findings, we performed power calculations for a hypothetical study searching for predictive disease biomarkers among ¹H NMR-detectable urine and plasma metabolites. Our calculations suggest that sample sizes of 2000–5000 should allow reliable identification of disease-predictive metabolite concentrations explaining 5–10% of disease risk, while greater sample sizes of 5000–20 000 would be required to identify metabolite concentrations explaining 1–2% of disease risk.

¹H Nuclear Magnetic Resonance spectroscopy (¹H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top-down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non-identical twin pairs donated plasma and urine samples longitudinally. We acquired ¹H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common-environmental), individual-environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual-environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in ¹H NMR-detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker-discovery studies. We provide a power-calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect ¹H NMR-based biomarkers quantifying predisposition to disease.

Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma

In this work, the variations in the metabolic profile of blood plasma from lung cancer patients and healthy controls were investigated through NMR-based metabonomics, to assess the potential of this approach for lung cancer screening and diagnosis. PLS-DA modeling of CPMG spectra from plasma, subjected to Monte Carlo Cross Validation, allowed cancer patients to be discriminated from controls with sensitivity and specificity levels of about 90%. Relatively lower HDL and higher VLDL + LDL in the patients' plasma, together with increased lactate and pyruvate and decreased levels of glucose, citrate, formate, acetate, several amino acids (alanine, glutamine, histidine, tyrosine, valine), and methanol, could be detected. These changes were found to be present at initial disease stages and could be related to known cancer biochemical hallmarks, such as enhanced glycolysis, glutaminolysis, and gluconeogenesis, together with suppressed Krebs cycle and reduced lipid catabolism, thus supporting the hypothesis of a systemic metabolic signature for lung cancer. Despite the possible confounding influence of age, smoking habits, and other uncontrolled factors, these results indicate that NMR-based metabonomics of blood plasma can be useful as a screening tool to identify suspicious cases for subsequent, more specific radiological tests, thus contributing to improved disease management.

NMR-based metabolic profiling for serum of mouse exposed to source water

(1)H nuclear magnetic resonance (NMR) based metabonomic method was used to characterize the profile of low-molecular-weight endogenous metabolites in mouse (Mus musculus) serum following exposure to Taihu Lake source water for 90 days. The (1)H NMR spectra of mice sera were recoded and a total of 21 metabolites were identified. Data reduction and latent biomarkers identification were processed by pattern recognition (PR) analysis. The principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) identified differences in metabolic profiles between control and treatment groups. A number of serum metabolic perturbations were observed in sera of source water treatment mice compared to control mice, including decreased levels of acetone, pyruvate, glutamine, lysine and citrate. These results indicated that Taihu Lake source water could induce energy metabolism changes in mouse related to fatty acid β-oxidation, tricarboxylic acid (TCA) cycle, citric acid cycle, and metabolism of some amino acids. (1)H NMR-based metabolic profiling provides new insight into the toxic effect of Taihu Lake source water, and suggests potential biomarkers for noninvasive monitoring of health risk.

NMR-based metabonomic study of the sub-acute toxicity of titanium dioxide nanoparticles in rats after oral administration

As titanium dioxide nanoparticles (TiO(2) NPs) are widely used commercially, their potential toxicity on human health has attracted particular attention. In the present study, the oral toxicological effects of TiO(2) NPs (dosed at 0.16, 0.4 and 1 g kg( - 1), respectively) were investigated using conventional approaches and metabonomic analysis in Wistar rats. Serum chemistry, hematology and histopathology examinations were performed. The urine and serum were investigated by (1)H nuclear magnetic resonance (NMR) using principal components and partial least squares discriminant analysis. The metabolic signature of urinalysis in TiO(2) NP-treated rats showed increases in the levels of taurine, citrate, hippurate, histidine, trimethylamine-N-oxide (TMAO), citrulline, alpha-ketoglutarate, phenylacetylglycine (PAG) and acetate; moreover, decreases in the levels of lactate, betaine, methionine, threonine, pyruvate, 3-D-hydroxybutyrate (3-D-HB), choline and leucine were observed. The metabonomics analysis of serum showed increases in TMAO, choline, creatine, phosphocholine and 3-D-HB as well as decreases in glutamine, pyruvate, glutamate, acetoacetate, glutathione and methionine after TiO(2) NP treatment. Aspartate aminotransferase (AST), creatine kinase (CK) and lactate dehydrogenase (LDH) were elevated and mitochondrial swelling in heart tissue was observed in TiO(2) NP-treated rats. These findings indicate that disturbances in energy and amino acid metabolism and the gut microflora environment may be attributable to the slight injury to the liver and heart caused by TiO(2) NPs. Moreover, the NMR-based metabolomic approach is a reliable and sensitive method to study the biochemical effects of nanomaterials.

Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles

Saliva is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases including cardiovascular, renal, and autoimmune diseases, viral and bacterial infections and, importantly, cancers. Saliva-based diagnostics, particularly those based on metabolomics technology, are emerging and offer a promising clinical strategy, characterizing the association between salivary analytes and a particular disease. Here, we conducted a comprehensive metabolite analysis of saliva samples obtained from 215 individuals (69 oral, 18 pancreatic and 30 breast cancer patients, 11 periodontal disease patients and 87 healthy controls) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). We identified 57 principal metabolites that can be used to accurately predict the probability of being affected by each individual disease. Although small but significant correlations were found between the known patient characteristics and the quantified metabolites, the profiles manifested relatively higher concentrations of most of the metabolites detected in all three cancers in comparison with those in people with periodontal disease and control subjects. This suggests that cancer-specific signatures are embedded in saliva metabolites. Multiple logistic regression models yielded high area under the receiver-operating characteristic curves (AUCs) to discriminate healthy controls from each disease. The AUCs were 0.865 for oral cancer, 0.973 for breast cancer, 0.993 for pancreatic cancer, and 0.969 for periodontal diseases. The accuracy of the models was also high, with cross-validation AUCs of 0.810, 0.881, 0.994, and 0.954, respectively. Quantitative information for these 57 metabolites and their combinations enable us to predict disease susceptibility. These metabolites are promising biomarkers for medical screening. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0178-y) contains supplementary material, which is available to authorized users.

Serum metabolome analysis by 1H-NMR reveals differences between chronic lymphocytic leukaemia molecular subgroups

Chronic lymphocytic leukaemia (CLL) is a heterogeneous disease exhibiting variable clinical course and survival rates. Mutational status of the immunoglobulin heavy chain variable regions (IGHVs) of CLL cells offers useful prognostic information for high-risk patients, but time and economical costs originally prevented it from being routinely used in a clinical setting. Instead, alternative markers of IGHV status, such as zeta-associated protein (ZAP70) or messenger RNA levels are often used. We report a (1)H-NMR-based metabolomics approach to examine serum metabolic profiles of early stage, untreated CLL patients (Binet stage A) classified on the basis of IGHV mutational status or ZAP70. Metabolic profiles of CLL patients (n=29) exhibited higher concentrations of pyruvate and glutamate and decreased concentrations of isoleucine compared with controls (n=9). Differences in metabolic profiles between unmutated (UM-IGHV; n=10) and mutated IGHV (M-IGHV; n=19) patients were determined using partial least square discriminatory analysis (PLS-DA; R(2)=0.74, Q(2)=0.36). The UM-IGHV patients had elevated levels of cholesterol, lactate, uridine and fumarate, and decreased levels of pyridoxine, glycerol, 3-hydroxybutyrate and methionine concentrations. The PLS-DA models derived from ZAP70 classifications showed comparatively poor goodness-of-fit values, suggesting that IGHV mutational status correlates better with disease-related metabolic profiles. Our results highlight the usefulness of (1)H-NMR-based metabolomics as a potential non-invasive prognostic tool for identifying CLL disease-state biomarkers.

Toxicometabolomics of urinary biomarkers for human gastric cancer in a mouse model

Toxicometabolomics of urinary biomarkers for human gastric cancer in a mouse model was investigated using (1)H-nuclear magnetic resonance (NMR) spectroscopy. A human gastric adenocarcinoma cell line (1 × 10(7) cells/ml) was grafted onto the skin of the back of intact male BALB/c-nu/nu mice. After the xenografted tumors developed, urine was collected and analyzed for endogenous metabolites. Global profiling combined with principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal projections to latent squares-discriminant analysis (OPLS-DA) showed distinct separation of clusters between control and tumor-bearing mice. Targeted profiling revealed significant changes in trimethylamine oxide (TMAO), 3-indoxylsulfate, hippurate, and citrate levels in mice carrying human gastric cancer cells compared to normal mice. The levels of TMAO (0.41-fold) and hippurate (0.26-fold) in tumor-bearing mice were significantly decreased, whereas the levels of 3-indoxylsulfate (3.39-fold), 2-oxoglutarate (2.32-fold), and citrate (1.9-fold) were significantly increased in urine samples of tumor-bearing mice. Data suggest that TMAO, hippurate, 3-indoxylsulfate, 2-oxoglutarate, and citrate may serve as useful urinary biomarkers for gastric tumorigenesis in a mouse model.

Chemometrics in metabolomics--a review in human disease diagnosis

Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.