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Table of Contents
REVIEW ARTICLE
Year : 2019  |  Volume : 30  |  Issue : 4  |  Page : 144-150

Metabolic profiling based on nuclear magnetic resonance spectroscopy and mass spectrometry as a tool for clinical application


1 Associate Professor; UROGIV Research Group, Universidad del Valle, Cali, Colombia
2 Associate Professor; LABIOMOL Research Group, Universidad del Valle, Cali, Colombia

Date of Submission14-Jan-2019
Date of Decision20-Feb-2019
Date of Acceptance11-Mar-2019
Date of Web Publication29-Jul-2019

Correspondence Address:
Herney Andrés Garcia-Perdomo
Cll 4B # 36-00, Universidad del Valle, Cali
Colombia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/UROS.UROS_2_19

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  Abstract 


Metabolomics provides an abundance of information with the potential to accurately describe the physiological state of an organism. It aims to identify small molecules under physiological conditions that might serve as biomarkers and aid in the identification and treatment of health problems. Combining nuclear magnetic resonance (NMR) with mass spectrometry (MS) yields better identification and quantification of compounds, especially in mixtures, as well as the ability to cross-analyze data from both techniques and thereby increase the number of compounds identified. Metabolomic profiling using NMR and/or MS provides an important diagnostic tool for identifying metabolites under different conditions. This also requires a valid and reliable way to standardize the way we use it to identify biomarkers. Regarding the clinical application of metabolomics, for bladder cancer, threonine, phenylalanine, valine, isoleucine, lysine, methionine, leucine, glutamate, histidine, arginine, aspartic acid, tyrosine, glutamine, and serine were found discriminative in diagnosing this entity. On the other side, sarcosine, choline, phosphocholines, phosphorylcholines, carnitines, citrate, amino acids (lysine, glutamine, and ornithine), arachidonoyl amine, and lysophospholipids were found discriminative regarding the prostate cancer diagnosis.

Keywords: Cancer, mass spectrometry, metabolic profile, nuclear magnetic resonance spectroscopy, urologic oncology


How to cite this article:
Garcia-Perdomo HA, Vallejo FG, Sanchez A. Metabolic profiling based on nuclear magnetic resonance spectroscopy and mass spectrometry as a tool for clinical application. Urol Sci 2019;30:144-50

How to cite this URL:
Garcia-Perdomo HA, Vallejo FG, Sanchez A. Metabolic profiling based on nuclear magnetic resonance spectroscopy and mass spectrometry as a tool for clinical application. Urol Sci [serial online] 2019 [cited 2019 Aug 23];30:144-50. Available from: http://www.e-urol-sci.com/text.asp?2019/30/4/144/263649




  Introduction Top


The metabolic state of an organism depends on its genome, transcriptome, proteome, epigenome, microbiome, and exposome (environment) among other elements, including some that are still undiscovered. Metabolomics as a tool of study provides abundant information with the potential to accurately describe the physiological state of an organism.[1] One of the objectives of this tool is to identify and characterize small molecules under physiological conditions and identify those that may be important biomarkers for the identification and treatment of health problems.[2]

In this review, we will describe a few aspects of metabolomics, the methods used for quantification, and some clinical applications, mainly in cancer.


  General Concepts of Metabolomics Top


The term metabolomics is a rapidly growing field that was defined as the quantitative study of metabolites (molecules smaller than 1500 kDa) in a biological system and alterations to their concentrations due to environmental or genetic effects. The research has focused on areas such as toxicology, biomedical sciences, nutrition, genetics, innate errors of metabolism, diabetes, cancer, diagnostic tests, and neuronal diseases, among others. These applications are based on the theory that metabolites are the functional outputs of an organism. This might identify an alteration in the system's homeostasis that could occur before the appearance of symptoms of a particular disease, as a single metabolite may be the substrate for a number of different enzymes involved in complex metabolic pathways.[3],[4]

One of the main advantages of applying metabolomics is the ability to detect hundreds of metabolites in parallel,[5] thereby efficiently monitoring biochemical alterations. In addition, the metabolic profiles of biological specimens can be affected by factors such as diet, age, ethnicity, lifestyle, medications, or microbiota, and these factors should be controlled to obtain disease-specific information.[6]


  Types of Fluids Used in Metabolite Analysis Top


Analyses of metabolites can use various fluids including urine, blood plasma, serum, cerebrospinal fluid, saliva, and tissue. The former three are the most commonly used biofluids in metabolomic studies because they contain thousands of detectable metabolites and are obtained noninvasively or minimally invasively.[7] The use of each fluid type presents processing and analysis challenges and different possible associations with diseases and drug effects.

Urine

The analysis of urine samples offers a number of advantages over the use of other fluids such as blood (serum), plasma, saliva, cerebrospinal fluid, and homogenized tissues. Among these, advantages are the following: (1) urine can be collected in large quantities, (2) the sample collection process is not invasive, and samples can be taken repeatedly, and (3) urine requires less complex pretreatment steps for analysis because it has low protein levels and high concentrations of low-molecular-weight compounds.[5] Thus, urine is a sample with low complexity of analysis and few molecular interactions. For these reasons, urine is the most commonly used fluid in the analysis of metabolites for the early detection of diseases.[8],[9] However, given its high-salt content, mass spectrometry (MS) measurements of urine are slightly more challenging than measurements of other fluids and still require pretreatment of the sample.[1]

Blood plasma and serum

The use of these fluids for metabolite analysis allows the determination of the presence and stage of several diseases, but these fluids contain a wide range of macromolecules that, when analyzed, can overlap with the results from small molecule metabolites.[9]

Relationships can be found between serum and plasma metabolic profiles that can provide an overview of metabolic status at a point in time. In nuclear magnetic resonance (NMR) data, this relationship includes narrow signals from small molecule metabolites and broad signals from proteins and lipids.[5]

Homogenized tissues

The metabolic profiling of intact tissue has gained interest in recent years; its purpose is to obtain an approximate understanding of the molecular basis of disease and pathway analysis.[10] The global determination of metabolite concentrations in anatomical tissues might provide information on unique aspects of the tissue during pathological development, which cannot be derived from measurements taken in other fluids, such as the relationship between metabolism and changes in tissues. Nonetheless, the information derived from the analyzing tissue samples is difficult to obtain, requiring invasive methods and sample preparation for storage and analysis.[9]


  Metabolite Analysis Techniques Top


Two of the most important techniques currently used in metabolomics are NMR spectroscopy and MS. NMR requires little to no preparation, is rapid and noninvasive, does not destroy tissue, and has highly reproducible results (coefficient of variation: 1%–2%). Combining NMR with MS might increase the diagnostic yield, but the data obtained from NMR/MS experiments are quite complex, as they provide qualitative and quantitative information on several metabolites, and distinguishing statistically between disease and control markers can be difficult [Figure 1].[5]
Figure 1: Process for obtaining a metabolic profile

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We would like to go deeper describing the techniques used for metabolite analysis in the following paragraphs:

Nuclear magnetic resonance spectroscopy

At present, single-pulse (one-dimensional [1D]) and 1D nuclear Overhauser enhancement spectroscopy with water suppression is one of the most widely used methods in metabolomics. This technique is robust, provides a flat baseline under similar conditions, and allows pulses to suppress elevated signals from water, leaving metabolite signals intact.[11],[12] Two additional important methods used to eliminate large signals from large molecules (e.g., tissue and serum) are the Carr-Purcell-Meiboom-Gill sequence and the “edited diffusion” method that allows the observation of large molecules such as lipids.[1]

Other methods, such as 2D NMR, have been used in the detection of metabolites to reduce spectrum complexity, although these methods are expensive, time-consuming, and complex to analyze. These methods include 2D-J spectroscopy, correlation spectroscopy, total correlation spectroscopy, heteronuclear single-quantum coherence spectroscopy, and heteronuclear multiple bond correlation.[13],[14]

High-resolution magic-angle spinning (HR-MAS) NMR is another technique used with tissues. This technique obtains high-resolution spectra from heterogeneous samples that are neither solids nor pure liquids, such as solvated resins, allowing the in situ characterization of organic molecules and the quantification of compounds in the solid support phase [Table 1].[15]
Table 1: Nuclear magnetic resonance and mass spectrometry techniques

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Mass spectrometry

MS is used with various separation methods, such as gas chromatography (GC), liquid chromatography (LC), and capillary electrophoresis (CE), to provide chemical information for metabolomic studies [Table 1].[16],[17]

Liquid chromatography coupled to mass spectrometry (high-performance liquid chromatography-mass spectrometry)

LC-MS is one of the most important techniques used for metabolic analysis, with high sensitivity and the ability to provide a wide range of information on metabolite content.[5] This technique separates a sample by LC for subsequent analysis by MS. LC-MS has been widely used to analyze complexed substances (including nonvolatile) and can be used to separate macromolecules such as proteins.[18] LC-MS is considered a moderate to high-performance method, and ultra performance LC increases the chromatographic resolution of this technique 3–5 fold.[19] Urine can be input directly into an LC system, but serum and other liquids require preparation for protein precipitation.[5]

Gas chromatography coupled to mass spectrometry

GC-MS is usually used to analyze samples of fluids, such as urine, and volatile metabolites including fatty acids, steroids, and flavonoids. GC-MS has lower costs of analysis than high-performance LC (HPLC)-MS, but its use is limited for nonpolar thermally stable metabolites. Thus, it is commonly used to derivatize compounds that produce other volatile compounds. The advantages of GC-MS include its rapid ability to identify metabolites, supported by the commercial availability of extensive libraries.[20],[21]

The use of different agents in the derivatization of compounds has been studied in human urine samples treated with N, O-bis (trimethylsilyl) trifluoroacetamide (BSTFA), N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA), and methyl-bis-trifluoroacetamide (MBTFA) and analyzed in GC-quadruple MS. BSTFA and MSTFA exhibit similar efficiency with respect to the number of peaks detected, peak intensity, and reproducibility. MBTFA combined with BSTFA exhibits better secondary and tertiary amine derivation. Some amino acids were detected when derivation with BSTFA was followed by the use of MBTFA; however, these results were not reproducible, and BSTFA is recommended as an optimal derivation agent for GC-MS.[21]

Combining nuclear magnetic resonance and mass spectrometry

NMR has low sensitivity for the identification of metabolites; however, it offers the possibility for broad observation of the most abundant compounds in biological fluids and tissues without the need to prepare or fractionate samples. In addition, NMR allows the identification of compounds with identical masses and the identification of dynamics that can reveal metabolic pathways and their compartmentalization.[1] Combining NMR and MS yields increased identification and quantification of compounds, especially in complexed ones. In addition, data from both techniques can be cross-analyzed to increase the number of compounds identified using the principle of a relatively constant abundance/intensity ratio for the same metabolites across different samples.[22],[23] The combination of NMR and MS also allows the performance of isotope screening experiments and metabolic flow analysis (NMR provides position, and MS provides quantification).[24]


  Platforms to Establish Standards in Nuclear Magnetic Resonance-Based Metabolomics Top


Information on biological molecules associated with NMR spectra is available in databases including Human Metabolome Database, Biological Magnetic Resonance Data Bank, TOCSY Customized Carbon Trace Archive, and Complex Mixture Analysis by NMR, but information on many metabolites is still needed.[22],[23],[25],[26] Repositories of results from metabolomic studies have been generated by the National Institutes of Health's Common Fund Centers [27] and by initiatives to coordinate metabolomic standards, for example, Complex Systems Modelling and Simulation, which currently develops a robust infrastructure that allows the exchange of data or metadata between researchers and the development of applications (http://nmrml.org and http://metabolomexchange.org).[28] Many other important databases (pathway analysis and viewers) are now available for metabolomic analysis, and these include KEGG (http://www.genome.ad.jp/kegg/), MetaCyc (http://metacyc.org/), AraCyc (http://www. Arabidopsis.org/tools/aracyc/), MapMan (http://gabi.rzpd.de/projects/MapMan/), KaPPA-View (http://kpv.kazusa.or.jp/kappa-view/), the data model for plant metabolomic experiment ArMet (http://www.armet.org/), and functional genomic databases MetNet (http://metnet.vrac.iastate.edu/) and DOME (http://medicago.vbi.vt.edu). In addition, standards and best practices have been published for the metabolic phenotyping of biological fluids.[29],[30]


  Analysis of Metabolites in Clinical Conditions Top


The development of a pathology can be identified through different metabolic pathways, but we are only describing three of them: glycolisis, glutamine and fatty acids, in which adenosine triphosphate (ATP) production is increased, mediated by the increased activity of enzymes such as hexokinase and lactate dehydrogenase and the increased production of lactate induced by the overexpression of hypoxia-inducible factor-1. Under these conditions, the action of pyruvate dehydrogenase kinase 1 is induced, which in turn inhibits pyruvate dehydrogenase, thus reducing pyruvate input to the tricarboxylic acid (TCA) cycle and reducing levels of associated metabolites. The consumption of glutamine also increases through the action of the glutaminase enzyme. This effect presents a possible pathway for supplementing the deficit induced in TCA under anaerobic conditions. Some studies suggest that this pathway provides the main energy source used under aerobic conditions by proliferating cells such as lymphocytes, fibroblasts, and some cancer cells. This pathway is also a precursor of oxaloacetate, which is involved in the synthesis of fatty acids and cholesterol. The alteration of these pathways, in turn, alters the metabolism of fatty acids, which are produced in greater quantities in cancer cells due to the hyperactivity of enzymes such as ATP citrate lyase, acetyl-CoA carboxylase, and fatty acid synthase. The latter is expressed at low levels in normal cells and tissues but is highly expressed in cancerous tissues. Cancerous tissues also exhibit an increase in messenger lipids such as phosphatidylinositol-3, 4, 5-triphosphate which is formed by the action of phosphatidylinositol 3-kinase and active protein kinase B/Akt.[31] Alterations in these pathways can be used in the detection of metabolites and the preventive diagnosis, treatment, and identification of biomarkers for diseases. Therefore, the study of alterations to the levels of these compounds in tissue, plasma, or urine has been used to study various pathologies.

Urological cancer is a condition in which early detection based on biochemical methods is required to offer rapid treatment based on personalized medicine. Metabolomic tools based on NMR and MS have the potential to assist the provision of early diagnosis and targeted therapy. Various biomarkers for urological cancer based on different pathways of disease in human and animal models are currently being studied.

Bladder cancer

Metabolomics has been extensively used in the study of cancer, with studies on bladder cancer using LC/MS to compare the urine metabolites of healthy controls and those of patients with bladder cancer and identify differences.[32]

Recently, Cheng et al.[33] pooled 11 studies which described metabolites to detect bladder cancer in a systematic review, with different techniques and high heterogeneity: GC–time-of-flight MS (two studies), LC-MS/CE-MS (three studies), HR-MAS NMR spectroscopy (one study), proton NMR (three studies), reversed-phase LC-MS/hydrophilic interaction chromatography-MS (one study), and HPLC–quadrupole time-of-flight MS (one study). Regarding the altered glucose metabolites expression, they found no conclusion about glucose, fructose, and lactic acid since they were found up in some studies and down in others; nevertheless, pyruvic acid was found down in only one study (anaerobic oxidation). Similarly, citric acid and fumaric acid were also found down in different studies (aerobic oxidation).

Regarding the altered amino acid metabolites expression, they found that threonine, phenylalanine, valine, isoleucine, lysine, methionine, leucine (essential amino acids), glutamate, histidine, arginine, aspartic acid, tyrosine, glutamine, and serine (nonessential amino acids) were found up. They found some studies regarding the lipid and nucleotide metabolites expression; however, it was not enough to pool information and recommend for the diagnosis of bladder cancer.

Another study found that metabolite profiles in urine can discriminate between bladder cancers with and without muscular invasion and between healthy patients and those with bladder cancer and can trace relationships between different metabolic pathways and identify where pathology-associated alterations appear.[34]

There are few studies using different samples and analytical platforms with similar results for some metabolites; nonetheless, it is important to standardize these two fundamental variables to establish a way to diagnose bladder cancer nowadays.

Prostate cancer

Plasma and tissue samples from prostate cancer (PCa) patients have been evaluated using different methods such as HR-MAS and LC/MS; therefore, alterations in amino acid levels have been found, such as increased levels of lactate, phospholipids, and choline; low levels of citrate and polyamines; and regulation of spermine.[35],[36],[37],[38],[39] One of the advantages of this marker is that noninvasive methods can be used for the detection of PCa, as it can be measured in urine.[40]

Regarding the complete spectrum of metabolites, so far, the most promising biomarkers for PCa diagnosis are sarcosine (area under the curve [AUC]: 0.67),[40] choline, phosphocholines (AUC: 0.982),[39] phosphorylcholines, carnitines (AUC: 0.97),[41] citrate (AUC: 0.89),[42] amino acids (lysine, glutamine, and ornithine),[43],[44],[45],[46] arachidonoyl amine (AUC: 0.86),[41] and lysophospholipids (steroid hormone biosynthesis pathway and bile acids – sensitivity and specificity: 92%–94%).[45]

The following five constituents are also important when discriminating between PCa and hyperplasia: dihydroxybutanoic acid, xylonic acid, pyrimidine, xylopyranose, and ribofuranoside, with an AUC: 0.825.[47] In addition, citrate, glutamate, and taurine have important discriminatory roles, with a sensitivity of 100% and specificity of 96%.[48]

Regarding the metabolic pathways, many of them have been associated with PCa. The following are the most described in the literature:

  1. Energy metabolism, including TCA cycle intermediates,[48],[49],[50] lactate,[49],[50] citrate,[51] phosphoenolpyruvate, and adenosine diphosphate [50]
  2. Chronic stress is another important pathway involved in developing cancer, and cortisol [61] is thought to be related by this way [62]
  3. Cell proliferation pathway through de novo lipid biosynthesis has participated by different metabolites such as citrate, inositol, lactate,[63] and cortisol,[61],[62] along with phosphoethanolamine, glycerophosphoethanolamine,[64] and acetate.[63]


As readers can see, there are many metabolites that might be associated or altered in PCa; therefore, there is still place to study through this molecular method how to diagnose and establish prognosis in patients with this condition.

Renal cancer

Kidney cancer is one of the most important urological cancers since it has high mortality. The research has been focused on the clear cell type since it is the most frequent, and different studies have shown results from human and animal samples. Here, we present some of the most promising metabolite biomarkers for this kind of tumor according to critical revisions from Rodrigues et al. and Hakimi et al.:[65],[66] Regarding the amino acid metabolism, they found that creatine, glutamate, glutamine, and quinolinate were found upregulated mostly in renal tissue; however, 4-hydroxybenzoate, gentisate, and hippuric acid were found downregulated mainly in urine. Regarding the fatty acid metabolism, carnitine and acetylcarnitine were found upregulated in renal tissue, but choline/choline-containing compounds were found downregulated in the same kind of sample. The glutathione (reduced form) was found upregulated in renal tissue, and regarding the glycolysis, glucose was found to be inconclusive in different kinds of samples; on the other side, lactate and pyruvate were found upregulated in different samples. Citrate, fumarate, malate, and succinate had inconclusive findings, regarding the TCA metabolism. In addition, alpha-tocopherol (Vitamin E metabolism) was found upregulated in renal tissue. Most of the inconsistencies are because of differences in type, collection, handling, and manipulation of the samples; experimental designs; and the kind of analytical platforms.[65]

The identification of biomarkers has been used in renal cancer patients, allowing differentiation between healthy controls and cancer patients and suggesting the involvement of alterations in the quinolinate pathways (pathways of nicotinate and nicotinamide metabolism), gentisate (benzoate degradation pathway), and alpha-ketoglutarate (alanine, aspartate, and glutamine metabolism pathway) in the disease. However, these results have not been validated for use in clinical practice.[67],[68]


  Conclusions Top


Metabolomic profiling using NMR and/or MS provides an important diagnostic tool for identifying metabolites under different conditions. These tools have been tried for conditions that greatly affect the quality of life, such as cancer; in such cases, the fast, valid, and reliable identification of biomarkers is required. Different methods involving NMR and/or MS can result in variations in their results; however, the appropriate control of confounding variables and the use of statistical and bioinformatic analyses can offer a wide range of information, useful for clinical applications.

Acknowledgment

We wish to thank Fabián Hernández BSc MSc PhD (c), who supplied some of the information for this review.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Markley JL, Brüschweiler R, Edison AS, Eghbalnia HR, Powers R, Raftery D, et al. The future of NMR-based metabolomics. Curr Opin Biotechnol 2017;43:34-40.  Back to cited text no. 1
    
2.
Robinette SL, Brüschweiler R, Schroeder FC, Edison AS. NMR in metabolomics and natural products research: Two sides of the same coin. Acc Chem Res 2012;45:288-97.  Back to cited text no. 2
    
3.
Roux A, Lison D, Junot C, Heilier JF. Applications of liquid chromatography coupled to mass spectrometry-based metabolomics in clinical chemistry and toxicology: A review. Clin Biochem 2011;44:119-35.  Back to cited text no. 3
    
4.
Smolinska A, Blanchet L, Buydens LM, Wijmenga SS. NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review. Anal Chim Acta 2012;750:82-97.  Back to cited text no. 4
    
5.
Gowda GA, Zhang S, Gu H, Asiago V, Shanaiah N, Raftery D. Metabolomics-based methods for early disease diagnostics. Expert Rev Mol Diagn 2008;8:617-33.  Back to cited text no. 5
    
6.
Nicholson JK, Lindon JC, Holmes E. 'Metabonomics': Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999;29:1181-9.  Back to cited text no. 6
    
7.
Bollard ME, Stanley EG, Lindon JC, Nicholson JK, Holmes E. NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. NMR Biomed 2005;18:143-62.  Back to cited text no. 7
    
8.
Kemperman RF, Horvatovich PL, Hoekman B, Reijmers TH, Muskiet FA, Bischoff R. Comparative urine analysis by liquid chromatography-mass spectrometry and multivariate statistics: Method development, evaluation, and application to proteinuria. J Proteome Res 2007;6:194-206.  Back to cited text no. 8
    
9.
Zhang A, Sun H, Wang P, Han Y, Wang X. Recent and potential developments of biofluid analyses in metabolomics. J Proteomics 2012;75:1079-88.  Back to cited text no. 9
    
10.
Griffin JL, Kauppinen RA. Tumour metabolomics in animal models of human cancer. J Proteome Res 2007;6:498-505.  Back to cited text no. 10
    
11.
Saude E, Slupsky C, Sykes B. Optimization of NMR analysis of biological fluids for quantitative accuracy. Metabolomics 2006;2:113-23.  Back to cited text no. 11
    
12.
Mo H, Raftery D. Pre-SAT180, a simple and effective method for residual water suppression. J Magn Reson 2008;190:1-6.  Back to cited text no. 12
    
13.
Tang H, Wang Y, Nicholson JK, Lindon JC. Use of relaxation-edited one-dimensional and two dimensional nuclear magnetic resonance spectroscopy to improve detection of small metabolites in blood plasma. Anal Biochem 2004;325:260-72.  Back to cited text no. 13
    
14.
Xi Y, de Ropp JS, Viant MR, Woodruff DL, Yu P. Improved identification of metabolites in complex mixtures using HSQC NMR spectroscopy. Anal Chim Acta 2008;614:127-33.  Back to cited text no. 14
    
15.
Espinosa JF. High resolution magic angle spinning NMR applied to the analysis of organic compounds bound to solid supports. Curr Top Med Chem 2011;11:74-92.  Back to cited text no. 15
    
16.
Zhang AH, Wang P, Sun H, Yan GL, Han Y, Wang XJ. High-throughput ultra-performance liquid chromatography-mass spectrometry characterization of metabolites guided by a bioinformatics program. Mol Biosyst 2013;9:2259-65.  Back to cited text no. 16
    
17.
Granger J, Bake A, Plumb R, Perez J, Wilson I. Ultra performance liquid chromatography- MS (TOF): New separations technology for high throughput metabonomics. Drug Metab Rev 2004;36-252-2.  Back to cited text no. 17
    
18.
Wilson ID, Plumb R, Granger J, Major H, Williams R, Lenz EM. HPLC-MS-based methods for the study of metabonomics. J Chromatogr B Analyt Technol Biomed Life Sci 2005;817:67-76.  Back to cited text no. 18
    
19.
Wilson ID, Nicholson JK, Castro-Perez J, Granger JH, Johnson KA, Smith BW, et al. High resolution “ultra performance” liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. J Proteome Res 2005;4:591-8.  Back to cited text no. 19
    
20.
Ryan D, Robards K, Prenzler PD, Kendall M. Recent and potential developments in the analysis of urine: A review. Anal Chim Acta 2011;684:8-20.  Back to cited text no. 20
    
21.
Pasikanti KK, Ho PC, Chan EC. Development and validation of a gas chromatography/mass spectrometry metabonomic platform for the global profiling of urinary metabolites. Rapid Commun Mass Spectrom 2008;22:2984-92.  Back to cited text no. 21
    
22.
Bingol K, Bruschweiler-Li L, Yu C, Somogyi A, Zhang F, Brüschweiler R. Metabolomics beyond spectroscopic databases: A combined MS/NMR strategy for the rapid identification of new metabolites in complex mixtures. Anal Chem 2015;87:3864-70.  Back to cited text no. 22
    
23.
Bingol K, Brüschweiler R. NMR/MS translator for the enhanced simultaneous analysis of metabolomics mixtures by NMR spectroscopy and mass spectrometry: Application to human urine. J Proteome Res 2015;14:2642-8.  Back to cited text no. 23
    
24.
Gu H, Gowda GA, Neto FC, Opp MR, Raftery D. RAMSY: Ratio analysis of mass spectrometry to improve compound identification. Anal Chem 2013;85:10771-9.  Back to cited text no. 24
    
25.
Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, et al. HMDB: A knowledgebase for the human metabolome. Nucleic Acids Res 2009;37:D603-10.  Back to cited text no. 25
    
26.
Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J, et al. BioMagResBank. Nucleic Acids Res 2008;36:D402-8.  Back to cited text no. 26
    
27.
Sud M, Fahy E, Cotter D, Azam K, Vadivelu I, Burant C, et al. Metabolomics workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res 2016;44:D463-70.  Back to cited text no. 27
    
28.
Salek RM, Neumann S, Schober D, Hummel J, Billiau K, Kopka J, et al. Coordination of standards in metabolomics (COSMOS): Facilitating integrated metabolomics data access. Metabolomics 2015;11:1587-97.  Back to cited text no. 28
    
29.
Dona AC, Jiménez B, Schäfer H, Humpfer E, Spraul M, Lewis MR, et al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal Chem 2014;86:9887-94.  Back to cited text no. 29
    
30.
Emwas AH, Roy R, McKay RT, Ryan D, Brennan L, Tenori L, et al. Recommendations and standardization of biomarker quantification using NMR-based metabolomics with particular focus on urinary analysis. J Proteome Res 2016;15:360-73.  Back to cited text no. 30
    
31.
Chiaradonna F, Moresco RM, Airoldi C, Gaglio D, Palorini R, Nicotra F, et al. From cancer metabolism to new biomarkers and drug targets. Biotechnol Adv 2012;30:30-51.  Back to cited text no. 31
    
32.
Issaq HJ, Nativ O, Waybright T, Luke B, Veenstra TD, Issaq EJ, et al. Detection of bladder cancer in human urine by metabolomic profiling using high performance liquid chromatography/mass spectrometry. J Urol 2008;179:2422-6.  Back to cited text no. 32
    
33.
Cheng Y, Yang X, Deng X, Zhang X, Li P, Tao J, et al. Metabolomics in bladder cancer: A systematic review. Int J Clin Exp Med 2015;8:11052-63.  Back to cited text no. 33
    
34.
Putluri N, Shojaie A, Vasu VT, Vareed SK, Nalluri S, Putluri V, et al. Metabolomic profiling reveals potential markers and bioprocesses altered in bladder cancer progression. Cancer Res 2011;71:7376-86.  Back to cited text no. 34
    
35.
Giskeødegård GF, Hansen AF, Bertilsson H, Gonzalez SV, Kristiansen KA, Bruheim P, et al. Metabolic markers in blood can separate prostate cancer from benign prostatic hyperplasia. Br J Cancer 2015;113:1712-9.  Back to cited text no. 35
    
36.
Rantalainen M, Cloarec O, Beckonert O, Wilson ID, Jackson D, Tonge R, et al. Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice. J Proteome Res 2006;5:2642-55.  Back to cited text no. 36
    
37.
Swanson MG, Zektzer AS, Tabatabai ZL, Simko J, Jarso S, Keshari KR, et al. Quantitative analysis of prostate metabolites using 1H HR-MAS spectroscopy. Magn Reson Med 2006;55:1257-64.  Back to cited text no. 37
    
38.
Burns MA, He W, Wu CL, Cheng LL. Quantitative pathology in tissue MR spectroscopy based human prostate metabolomics. Technol Cancer Res Treat 2004;3:591-8.  Back to cited text no. 38
    
39.
Cheng LL, Burns MA, Taylor JL, He W, Halpern EF, McDougal WS, et al. Metabolic characterization of human prostate cancer with tissue magnetic resonance spectroscopy. Cancer Res 2005;65:3030-4.  Back to cited text no. 39
    
40.
Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 2009;457:910-4.  Back to cited text no. 40
    
41.
Lokhov PG, Dashtiev MI, Moshkovskii SA, Archakov AI. Metabolite profiling of blood plasma of patients with prostate cancer. Metabolomics 2010;6:156-63.  Back to cited text no. 41
    
42.
Serkova NJ, Gamito EJ, Jones RH, O'Donnell C, Brown JL, Green S, et al. The metabolites citrate, myo-inositol, and spermine are potential age-independent markers of prostate cancer in human expressed prostatic secretions. Prostate 2008;68:620-8.  Back to cited text no. 42
    
43.
Osl M, Dreiseitl S, Pfeifer B, Weinberger K, Klocker H, Bartsch G, et al. A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectrometry. Bioinformatics 2008;24:2908-14.  Back to cited text no. 43
    
44.
Miyagi Y, Higashiyama M, Gochi A, Akaike M, Ishikawa T, Miura T, et al. Plasma free amino acid profiling of five types of cancer patients and its application for early detection. PLoS One 2011;6:e24143.  Back to cited text no. 44
    
45.
Zang X, Jones CM, Long TQ, Monge ME, Zhou M, Walker LD, et al. Feasibility of detecting prostate cancer by ultraperformance liquid chromatography-mass spectrometry serum metabolomics. J Proteome Res 2014;13:3444-54.  Back to cited text no. 45
    
46.
Struck-Lewicka W, Kordalewska M, Bujak R, Yumba Mpanga A, Markuszewski M, Jacyna J, et al. Urine metabolic fingerprinting using LC-MS and GC-MS reveals metabolite changes in prostate cancer: A pilot study. J Pharm Biomed Anal 2015;111:351-61.  Back to cited text no. 46
    
47.
Wu H, Liu T, Ma C, Xue R, Deng C, Zeng H, et al. GC/MS-based metabolomic approach to validate the role of urinary sarcosine and target biomarkers for human prostate cancer by microwave-assisted derivatization. Anal Bioanal Chem 2011;401:635-46.  Back to cited text no. 47
    
48.
Hahn P, Smith IC, Leboldus L, Littman C, Somorjai RL, Bezabeh T. The classification of benign and malignant human prostate tissue by multivariate analysis of 1H magnetic resonance spectra. Cancer Res 1997;57:3398-401.  Back to cited text no. 48
    
49.
Halliday KR, Fenoglio-Preiser C, Sillerud LO. Differentiation of human tumors from nonmalignant tissue by natural-abundance 13C NMR spectroscopy. Magn Reson Med 1988;7:384-411.  Back to cited text no. 49
    
50.
Kami K, Fujimori T, Sato H, Sato M, Yamamoto H, Ohashi Y, et al. Metabolomic profiling of lung and prostate tumor tissues by capillary electrophoresis time-of-flight mass spectrometry. Metabolomics 2013;9:444-53.  Back to cited text no. 50
    
51.
Mondul AM, Moore SC, Weinstein SJ, Karoly ED, Sampson JN, Albanes D. Metabolomic analysis of prostate cancer risk in a prospective cohort: The alpha-tocolpherol, beta-carotene cancer prevention (ATBC) study. Int J Cancer 2015;137:2124-32.  Back to cited text no. 51
    
52.
Vander Heiden MG, Cantley LC, Thompson CB. Understanding the warburg effect: The metabolic requirements of cell proliferation. Science 2009;324:1029-33.  Back to cited text no. 52
    
53.
Shuster JR, Lance RS, Troyer DA. Molecular preservation by extraction and fixation, mPREF: A method for small molecule biomarker analysis and histology on exactly the same tissue. BMC Clin Pathol 2011;11:14.  Back to cited text no. 53
    
54.
Li T, Apte U. Bile acid metabolism and signaling in cholestasis, inflammation, and cancer. Adv Pharmacol 2015;74:263-302.  Back to cited text no. 54
    
55.
Swanson MG, Vigneron DB, Tabatabai ZL, Males RG, Schmitt L, Carroll PR, et al. Proton HR-MAS spectroscopy and quantitative pathologic analysis of MRI/3D-MRSI-targeted postsurgical prostate tissues. Magn Reson Med 2003;50:944-54.  Back to cited text no. 55
    
56.
Mondul AM, Moore SC, Weinstein SJ, Männistö S, Sampson JN, Albanes D. 1-Stearoylglycerol is associated with risk of prostate cancer: Results from serum metabolomic profiling. Metabolomics 2014;10:1036-41.  Back to cited text no. 56
    
57.
Huang G, Liu X, Jiao L, Xu C, Zhang Z, Wang L, et al. Metabolomic evaluation of the response to endocrine therapy in patients with prostate cancer. Eur J Pharmacol 2014;729:132-7.  Back to cited text no. 57
    
58.
Currie E, Schulze A, Zechner R, Walther TC, Farese RV Jr. Cellular fatty acid metabolism and cancer. Cell Metab 2013;18:153-61.  Back to cited text no. 58
    
59.
Griffin JL, Shockcor JP. Metabolic profiles of cancer cells. Nat Rev Cancer 2004;4:551-61.  Back to cited text no. 59
    
60.
Soronen P, Laiti M, Törn S, Härkönen P, Patrikainen L, Li Y, et al. Sex steroid hormone metabolism and prostate cancer. J Steroid Biochem Mol Biol 2004;92:281-6.  Back to cited text no. 60
    
61.
Cho HJ, Kim JD, Lee WY, Chung BC, Choi MH. Quantitative metabolic profiling of 21 endogenous corticosteroids in urine by liquid chromatography-triple quadrupole-mass spectrometry. Anal Chim Acta 2009;632:101-8.  Back to cited text no. 61
    
62.
Moreno-Smith M, Lutgendorf SK, Sood AK. Impact of stress on cancer metastasis. Future Oncol 2010;6:1863-81.  Back to cited text no. 62
    
63.
Schiebler ML, Miyamoto KK, White M, Maygarden SJ, Mohler JL.In vitro high resolution 1H-spectroscopy of the human prostate: Benign prostatic hyperplasia, normal peripheral zone and adenocarcinoma. Magn Reson Med 1993;29:285-91.  Back to cited text no. 63
    
64.
Komoroski RA, Holder JC, Pappas AA, Finkbeiner AE. 31P NMR of phospholipid metabolites in prostate cancer and benign prostatic hyperplasia. Magn Reson Med 2011;65:911-3.  Back to cited text no. 64
    
65.
Rodrigues D, Monteiro M, Jerónimo C, Henrique R, Belo L, Bastos ML, et al. Renal cell carcinoma: A critical analysis of metabolomic biomarkers emerging from current model systems. Transl Res 2017;180:1-11.  Back to cited text no. 65
    
66.
Hakimi AA, Reznik E, Lee CH, Creighton CJ, Brannon AR, Luna A, et al. An integrated metabolic atlas of clear cell renal cell carcinoma. Cancer Cell 2016;29:104-16.  Back to cited text no. 66
    
67.
Kim K, Aronov P, Zakharkin SO, Anderson D, Perroud B, Thompson IM, et al. Urine metabolomics analysis for kidney cancer detection and biomarker discovery. Mol Cell Proteomics 2009;8:558-70.  Back to cited text no. 67
    
68.
Kim K, Taylor SL, Ganti S, Guo L, Osier MV, Weiss RH. Urine metabolomic analysis identifies potential biomarkers and pathogenic pathways in kidney cancer. OMICS 2011;15:293-303.  Back to cited text no. 68
    


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