Atormac
brintellex
Neurology India
menu-bar5 Open access journal indexed with Index Medicus
  Users online: 1714  
 Home | Login 
About Editorial board Articlesmenu-bullet NSI Publicationsmenu-bullet Search Instructions Online Submission Subscribe Videos Etcetera Contact
  Navigate Here 
 Search
 
  
 Resource Links
  »  Similar in PUBMED
 »  Search Pubmed for
 »  Search in Google Scholar for
 »Related articles
  »  Article in PDF (827 KB)
  »  Citation Manager
  »  Access Statistics
  »  Reader Comments
  »  Email Alert *
  »  Add to My List *
* Registration required (free)  

 
  In this Article
 »  Abstract
 » Introduction
 » Methods
 » Results
 » Discussion
 » Conclusions
 »  References
 »  Article Figures
 »  Article Tables

 Article Access Statistics
    Viewed2624    
    Printed75    
    Emailed0    
    PDF Downloaded59    
    Comments [Add]    
    Cited by others 1    

Recommend this journal

 


 
Table of Contents    
ORIGINAL ARTICLE
Year : 2016  |  Volume : 64  |  Issue : 2  |  Page : 246-251

Metabonomic signature analysis in plasma samples of glioma patients based on 1H-nuclear magnetic resonance spectroscopy


1 Department of Thoracic Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
2 Department of Thoracic Surgery, Huashan Hospital Affiliated to Fudan University, Shanghai, China
3 Department of Neurosurgery, Shanghai East Hospital Affiliated to Tongji University, Shanghai, China
4 Central Laboratory, Xinjiang Medical University, Urumqi, China

Date of Web Publication3-Mar-2016

Correspondence Address:
Ilyar Sheyhidin
Department of Thoracic Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0028-3886.177606

Rights and Permissions

 » Abstract 

Objective: The presence of a glioma is associated with increasing mortality. In this study, nuclear magnetic resonance (NMR) based metabonomics has been applied to investigate the metabolic signatures of a glioma in plasma. The purpose of this study was to assess the diagnostic potential of this approach and gain novel insights into the metabolism of glioma and its systemic effects.
Methods: Plasma samples were collected prospectively by centrifugation of blood samples from patients with a glioma (n = 70) or a control group (n = 70). NMR spectra of these plasma samples were analyzed using orthogonal partial least square discriminant analysis (OPLS-DA) to identify the potential biomarkers.
Results: The OPLS-DA model showed a good differentiation between the glioma and the control groups. A total of 20 metabolites were identified, which are closely correlating with the presence of a glioma. Compared to the control group, patients with a glioma were associated with lower concentrations of isoleucine, leucine, valine, lactate, alanine, glycoprotein, glutamate, citrate, creatine, myo-inositol, choline, tyrosine, phenylalanine, 1-methylhistidine, α-glucose, β-glucose, and higher concentrations of very low density lipoprotein, low density lipoprotein (LDL), unsaturated lipid, and pyruvate. These 20 metabolites, which are involved in energy, fatty acid, and amino acid metabolism, may be associated with a human glioma.
Conclusion: Our study is the first one to identify the plasma metabolites that have the potential to distinguish between patients with a glioma and healthy subjects. NMR-based metabonomics provides a good sensitivity and selectivity in differentiating the healthy control group from patients suffering form the disease. Plasma metabolic profiling may have a potential in diagnosing a glioma in the early phase and may help in enhancing our understanding of its underlying mechanisms.


Keywords: Glioma; metabonomics; nuclear magnetic resonance spectra


How to cite this article:
Kelimu A, Xie R, Zhang K, Zhuang Z, Mamtimin B, Sheyhidin I. Metabonomic signature analysis in plasma samples of glioma patients based on 1H-nuclear magnetic resonance spectroscopy. Neurol India 2016;64:246-51

How to cite this URL:
Kelimu A, Xie R, Zhang K, Zhuang Z, Mamtimin B, Sheyhidin I. Metabonomic signature analysis in plasma samples of glioma patients based on 1H-nuclear magnetic resonance spectroscopy. Neurol India [serial online] 2016 [cited 2020 Nov 25];64:246-51. Available from: https://www.neurologyindia.com/text.asp?2016/64/2/246/177606



 » Introduction Top


A glioma is the most common tumor from amongst primary central nervous system tumors, accounting for more than 70% of all primary brain tumors.[1],[2],[3] The prognosis of a patient detected to be having a malignant glioma is very poor and the recurrence rate is still very high. The survival period in malignant lesions is generally not more than 2 years. Thus, an early diagnosis of these lesions is the key to success in their management.[4],[5]

Metabonomics is a quantitative measurement of the dynamic and multivariate metabolic response of complex multicellular organisms of living systems to pathophysiologic stimuli or genetic modification and the consequent disruption of system regulation.[6],[7] It is a powerful tool for analyzing the chemical composition and in providing important information regarding the disease processes. It has, therefore, been widely used in the diagnosis of diseases [8],[9] and in biomarker screening.[10],[11] Nuclear magnetic resonance (NMR) is a noninvasive technique that is widely used in metabonomic studies for acquiring the metabolic profiles of biological specimens without extensive sample preparation.[12] NMR-based metabonomics is also used to find possible biomarkers of different cancers such as esophageal cancer,[13],[14] colorectal cancer,[15] prostate cancer,[16] breast cancer,[17] cervical cancer,[18] and brain cancer.[19] NMR based metabonomics coupled with data-reduction techniques offer a powerful approach to the metabolic data analysis of biofluids and tissues, thus, providing a “metabolic fingerprint” of the pathophysiological changes in the body.

In this study, the NMR profiling of human plasma was explored as a method to identify the glioma-related metabolic signature. This has the potential to provide valuable insights into the metabolism of gliomas and the systemic effects associated with its presence.


 » Methods Top


Collection of plasma samples

The Ethics Committee of Xinjiang Medical University approved the study protocol, and all subjects gave a written, informed consent. A total of 70 healthy volunteers were recruited following their extensive medical examination; and, 70 patients having a glioma (World Health Organisation grade III and grade IV) from the First Affiliated Hospital of Xinjiang Medical University were enrolled during the period from June 2012 to March 2013. All patients showed a tumor of greater than or equal to 3cm in size. The patient groups were not evenly matched based on their gender, age or stage of the disease, to maximize the patient diversity in the study. Patients with cardiovascular, hepatic, renal or inflammatory disease, and pregnant women were excluded from the study. The average age of participants was 64.2 years, ranging from 46 to 77 years.

Plasma samples were obtained by centrifugation of blood samples from every individual in the morning before breakfast and were immediately stored at −80°C until they were subjected to NMR spectroscopy.

1 H-nuclear magnetic resonance spectroscopy of blood plasma

The plasma samples were prepared for NMR analysis by mixing 200 μL of plasma with 400 μL of heavy water buffer solution (1.5 M NaH2 PO4 + 1.5 M K2 HPO4 in 20% v/v D2O and 80% v/v H2O, pH 7.4), and were kept at room temperature for 10 min. The mixture was subjected to centrifugation at 10,000 rpm for a further 10 min at 4°C. The clear supernatant (550 μL) was placed in a 5-mm NMR tube for spectroscopic analysis. The samples were analyzed by 1 H-NMR spectroscopy at 599.95 MHz using a Varian Unity Inova 600 (California, USA) spectrometer at 298 K. A combination of presaturation and the Carr–Purcell–Meiboom–Gill pulse sequence suppressed the water signals and broad protein resonance signals. For each sample, 128 scans were converted into 32,768 data points with a spectral width of 10,000 Hz, which resulted in an acquisition time of 1.64 s and a relaxation delay of 2 s. For assignment purposes, several two-dimensional (2D) NMR experiments including 1 H-1 H homonuclear correlation spectroscopy (COSY), total COSY (TOCSY), and J-resolved spectroscopy (J-Res) were also performed for the selected samples.[20],[21],[22]

Data analysis

Before Fourier transformation, free induction decays were multiplied by an exponential function equivalent to a 0.3 Hz line broadening factor. Fourier-transformed 1 H-NMR spectra were manually phased and baseline corrected, and chemical shifts were referenced to the anomeric proton signal of α-glucose at a chemical shift (δ) of 5.233 ppm. NMR spectra over the range of δ9.0–0.5 ppm were segmented into integral regions of 0.003 ppm, for each spectrum. All regions were normalized by the total integrated area of each spectrum. Due to the high variability in the intensity of water, the regions with δH = 5.22–4.68 ppm were excluded from the analysis. The pattern recognition analysis was carried out on the normalized NMR data sets using the SIMCA-P + software (Version 11.0, Umetrics Inc., Umeå, Sweden). We used the orthogonal partial least square discriminant analysis (OPLS-DA) method with unit variance scaling.[23],[24],[25] OPLS-DA, a new method of data analysis, is a combination of orthogonal signal correction (OSC) with partial least squares discriminant analysis (PLS-DA). OPLS-DA, including an OSC in the PLS-DA, was used for the extraction of cancer-related biomarkers by removing the influence of systematic variations unrelated to the cancer pathology. OSC was capable of eliminating the influence of diet, age, sex, and environmental factors, and of decreasing sample heterogeneity (which is the common source of error in clinical investigations).[26],[27],[28]

In this study, OPLS-DA comparisons between data of NMR spectra were obtained from the healthy controls and the patients with a glioma. The OPLS-DA model was constructed using the NMR data as the X matrix and the class information identifier for the different groups as the Y variable, using one PLS and one orthogonal component. The quality of the OPLS-DA model was described by the parameters R 2 X and Q 2; R 2 X represented the total explained variation for the X matrix, and Q 2 indicated the predictability of the model related to its statistical validity.[29] The discriminative significance of metabolites between the different groups was determined by the Pearson's product–moment correlation coefficient.


 » Results Top


Typical examples of the plasma samples obtained from the 1 H-NMR spectra of a healthy individual and a patient suffering from a glioma are shown in [Figure 1]. Resonance assignments were made by http://www.metabolomics.ca and confirmed by two dimensional (2D) NMR methods such as COSY, TOCSY, and J-Res spectra.
Figure 1: Representative 599.95 MHz 1H-nuclear magnetic resonance spectra of serum samples from patients with a glioma (top) and the healthy group (bottom)

Click here to view


To optimize the separation of the two groups, we then utilized OPLS-DA to visualize the metabolic differences between the plasma samples of patients having a glioma and healthy controls. [Figure 2] shows that the two groups achieved a distinct separation in the score plot of the principal component (PC) 1 and PC2 of the OPLS-DA. In this study, R 2 X = 0.40 and Q 2 = 0.88, were significantly high, indicating that it is an excellent model suitable for data analysis. Based on the number of samples in each group, a correlation coefficient (Pearson's product–moment correlation coefficient) of 0.232 was used as the cutoff value for the statistical significance based on the discrimination significance at the level of P = 0.05. A positive value indicated a relatively lower concentration, and a negative value indicated a relatively greater concentration of metabolites in the plasma of patients with a glioma. The values of the correlation coefficients indicating the significance of the metabolites contributing to the separation between the healthy patients and the patient suffering from a glioma are summarized in [Table 1].
Figure 2: Orthogonal partial least square discriminant analysis (OPLS-DA) score plot of samples from patients with a glioma and the healthy group. Red triangle: Glioma samples; black box: Healthy group samples. The model parameter R2X = 0.40 and Q2 = 0.88

Click here to view
Table 1: Comparison of plasma metabolites and correlation coefficients in the glioma patients and healthy controls

Click here to view


We applied 1 H-NMR to study the metabonomic profiling of human glioma and identified a total of 20 distinguishing metabolites. These metabolites are involved in the key metabolic pathways including glycolysis, amino acids metabolism, tricarboxylic acid (TCA) cycle, and fatty acid metabolism.

These correlation coefficients show that compared with the control group, the patients with a glioma had lower concentrations of isoleucine (δ0.93, δ1.00, δ1.96), leucine (δ0.95, δ0.97, δ1.72), valine (δ0.98, δ1.03, δ3.60), alanine (δ1.47, δ3.76), tyrosine (δ3.94, δ6.88, δ7.18), phenylalanine (δ7.32, δ7.37, δ7.42), 1-methylhistidine (δ7.06, δ7.79), glycoprotein (δ2.03), glutamate (δ2.13, δ2.36, δ3.75), myo-inositol (δ3.27, δ3.65), citrate (δ2.52, δ2.67), creatinine (δ3.06, δ4.05), choline (δ3.66, δ4.30), α-glucose (δ3.53, δ3.72, δ3.76, δ5.23), β-glucose (δ3.24, δ3.40, δ3.49, δ3.90, δ4.64), and lactate (δ1.33, δ4.11) (r > 0.232, P < 0.05), but significantly higher concentrations of very low density lipoprotein [VLDL] (δ0.85, δ0.88, δ1.26, δ2.22), low density lipoprotein [LDL] (δ1.26, δ4.24), unsaturated lipid (δ5.28, δ5.30) and pyruvate (δ2.37) [r>−0.232, P < 0.05].


 » Discussion Top


The prognosis for patients with a glioma remains unclear despite significant progress in clinical therapies and related technologies. This is largely due to the inability of the current treatment strategies to address the highly invasive nature of this type of tumor. Malignant glial cells often spread throughout the brain, making it exceedingly difficult to target and treat all intracranial neoplastic foci. As a result, tumor recurrence is inevitable despite aggressive surgery as well as adjuvant radiotherapy, and/or chemotherapy.[30]

In the recent years, evidence has accumulated in the reported literature that blood metabolic profiles may also exquisitely reflect different types of central nervous system cancer pathologies in humans, which also includes the presence of a glioma.[31] In this work, the NMR profile of plasma has shown, for the first time to the best of our knowledge, that subsequent analysis of metabolite profiles of plasma samples can distinguish between patients with a glioma from healthy normal controls.

In this study, the tricarboxylic acid cycle and energy metabolism are dominantly altered in patients having a glioma. There were some metabolites that were shown to increase in the patients having a glioma (such as VLDL, LDL, unsaturated lipid, and pyruvate). The altered values of these metabolites may probably reflect an altered energy metabolism or a disregulated metabolism of the corresponding metabolites to compensate for the excessive energy consumption by the cancer cells. Several metabolites such as isoleucine, leucine, valine, lactate, alanine, glycoprotein, glutamate, citrate, creatinine myo-inositol, choline, tyrosine, phenylalanine, 1-methylhistidine, α-glucose, and β-glucose were less concentrated in patients with a glioma. This represents a typical signature in cancer patients. It has been previously confirmed that tumors rely on glycolysis as the main source of energy even in the presence of oxygen.[32],[33] Furthermore, the precursors of glucose in gluconeogenesis, such as lactate and alanine were found in lower concentrations in patients with a glioma, which clearly points towards an altered energy metabolism. The decrease of glucose, which is common in many cancers, indicates the increasing demand for higher energy in malignant tumors.[13] Many blood amino acids were down-regulated in glioma patients compared with healthy controls, which indicate an increased demand for and overutilization of amino acids in the tumor tissue, as has been observed in other reports on varied malignant tumors.[34],[35] Fatty acid metabolism is also altered in the plasma of cancer patients, as evidenced by the increased levels of a number of unsaturated lipids, VLDL and LDL. This finding is also in accordance with the findings observed in the blood of patients suffering from other cancers.[18]

Concentrations of the metabolites VLDL, LDL, unsaturated lipid, and pyruvate increased in the plasma of patients with a glioma as compared with healthy persons. Ketone bodies are intermediate products of fatty acid metabolism and enter the blood stream after their production in the liver.[36] Usually, they are present in a low concentration in the blood, but this level increases with fat metabolism.[37] Organs outside the liver produce a large quantity of acetyl coenzyme A, which uses the ketone bodies and inhibits pyruvate dehydrogenase so that pyruvate cannot enter the Kreb's cycle and its plasma concentration increases.[38] Lower levels of creatine and myo-inositol are also related to fat mobilization. Carnitine is the main carrier of acetyl coenzyme A during fat metabolism and is involved in the biosynthesis and oxidation of fatty acids. A decrease in carnitine concentration also indicates an increased fat metabolism. α-Glucose, β-glucose, and their non-oxidative product, lactate, were decreased significantly in patients with a glioma compared with healthy controls. It is assumed that patients with a glioma rely on fat metabolism as their main source of energy.[39],[40]

We observed that the plasma concentration of various amino acids, including leucine, alanine, citrulline, tyrosine, histidine, isoleucine and valine decreased significantly in patients with a glioma. The metabolites related to cell membrane protection and immune function [41] such as glutamine, myo-inositol, scyllo-inositol, and creatine also decreased in the plasma. The immune system is the main antitumor defense system in the body. Glutamine maintains the function of the immune system and increases the total lymphocyte count in combination with glycoprotein and alanine.[42] Glutamine also protects cells, tissues, and organs from damage by free radicals. A decreased plasma glutamine level indicates a decrease in cell antioxidant activity, cell membrane damage, immune dysfunction, free radical injury, and an intensified oxidative damage.[43] Some studies have demonstrated that a glioma produces many oxygen free radicals,[44] which is the main cause of oxidant–antioxidant imbalance. Therefore, it is thought that a series of abnormalities such as immune dysfunction and oxidant-antioxidant imbalance occur in patients suffering from a glioma.


 » Conclusions Top


We have shown that the metabolic profiling of plasma, using a combination 1 H-NMR with multivariate statistical methods, revealed a detailed picture of metabolic changes in patients with a glioma compared with healthy controls. We discovered that a series of metabolites in glucose metabolism, fatty acid metabolism, and TCA cycle showed an altered expression in the plasma of patients with a glioma. These findings will not only enable an early diagnosis of this cancer at the molecular level but also improve our understanding of the initiation and development of a glioma. The plasma metabolite measurements have the capacity to revolutionize an early cancer detection and treatment.

Financial support and sponsorship

Nil.

Conflicts of interest

The authors have declared no conflicts of interest for this article.

 
 » References Top

1.
Hunter SB, Brat DJ, Olson JJ, Von Deimling A, Zhou W, Van Meir EG. Alterations in molecular pathways of diffusely infiltrating glial neoplasms: Application to tumor classification and anti-tumor therapy (Review). Int J Oncol 2003;23:857-69.  Back to cited text no. 1
    
2.
Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 2007;114:97-109.  Back to cited text no. 2
    
3.
Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, Janzer RC, et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 2009;10:459-66.  Back to cited text no. 3
    
4.
Van Meir EG, Hadjipanayis CG, Norden AD, Shu HK, Wen PY, Olson JJ. Exciting new advances in neuro-oncology: The avenue to a cure for malignant glioma. CA Cancer J Clin 2010;60:166-93.  Back to cited text no. 4
    
5.
Palmnas MS, Vogel HJ. The future of NMR metabolomics in cancer therapy: Towards personalizing treatment and developing targeted drugs? Metabolites 2013;3:373-96.  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.
Carrola J, Rocha CM, Barros AS, Gil AM, Goodfellow BJ, Carreira IM, et al. Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of urine. J Proteome Res 2011;10:221-30.  Back to cited text no. 7
    
8.
Yang J, Xu G, Hong Q, Liebich HM, Lutz K, Schmülling RM, et al. Discrimination of type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles. J Chromatogr B Analyt Technol Biomed Life Sci 2004;813:53-8.  Back to cited text no. 8
    
9.
Claudino WM, Quattrone A, Biganzoli L, Pestrin M, Bertini I, Di Leo A. Metabolomics: Available results, current research projects in breast cancer, and future applications. J Clin Oncol 2007;25:2840-6.  Back to cited text no. 9
    
10.
Xue R, Lin Z, Deng C, Dong L, Liu T, Wang J, et al. A serum metabolomic investigation on hepatocellular carcinoma patients by chemical derivatization followed by gas chromatography/mass spectrometry. Rapid Commun Mass Spectrom 2008;22:3061-8.  Back to cited text no. 10
    
11.
Bogdanov M, Matson WR, Wang L, Matson T, Saunders-Pullman R, Bressman SS, et al. Metabolomic profiling to develop blood biomarkers for Parkinson's disease. Brain 2008;131:389-96.  Back to cited text no. 11
    
12.
Cao Z, Wu LP, Li YX, Guo YB, Chen YW, Wu RH. Change of choline compounds in sodium selenite-induced apoptosis of rats used as quantitative analysis by in vitro 9.4T MR spectroscopy. World J Gastroenterol 2008;14:3891-6.  Back to cited text no. 12
    
13.
Zhang J, Bowers J, Liu L, Wei S, Gowda GA, Hammoud Z, et al. Esophageal cancer metabolite biomarkers detected by LC-MS and NMR methods. PLoS One 2012;7:e30181.  Back to cited text no. 13
    
14.
Wang L, Chen J, Chen L, Deng P, Bu Q, Xiang P, et al. 1 H-NMR based metabonomic profiling of human esophageal cancer tissue. Mol Cancer 2013;12:25.  Back to cited text no. 14
    
15.
Chan EC, Koh PK, Mal M, Cheah PY, Eu KW, Backshall A, et al. Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J Proteome Res 2009;8:352-61.  Back to cited text no. 15
    
16.
van Asten JJ, Cuijpers V, Hulsbergen-van de Kaa C, Soede-Huijbregts C, Witjes JA, Verhofstad A, et al. High-resolution magic angle spinning NMR spectroscopy for metabolic assessment of cancer presence and Gleason score in human prostate needle biopsies. MAGMA 2008;21:435-42.  Back to cited text no. 16
    
17.
Whitehead TL, Kieber-Emmons T. Applying in vitro NMR spectroscopy and 1 H NMR metabonomics to breast cancer characterization and detection. Prog Nucl Magn Reson Spectrosc 2005;47:165-74.  Back to cited text no. 17
    
18.
Hasim A, Ali M, Mamtimin B, Ma JQ, Li QZ, Abudula A. Metabonomic signature analysis of cervical carcinoma and precancerous lesions in women by 1 H NMR spectroscopy. Exp Ther Med 2012;3:945-51.  Back to cited text no. 18
    
19.
Astrakas LG, Zurakowski D, Tzika AA, Zarifi MK, Anthony DC, De Girolami U, et al. Noninvasive magnetic resonance spectroscopic imaging biomarkers to predict the clinical grade of pediatric brain tumors. Clin Cancer Res 2004;10:8220-8.  Back to cited text no. 19
    
20.
Waters NJ, Waterfield CJ, Farrant RD, Holmes E, Nicholson JK. Metabonomic deconvolution of embedded toxicity: Application to thioacetamide hepato- and nephrotoxicity. Chem Res Toxicol 2005;18:639-54.  Back to cited text no. 20
    
21.
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. 21
    
22.
Wang Y, Holmes E, Comelli EM, Fotopoulos G, Dorta G, Tang H, et al. Topographical variation in metabolic signatures of human gastrointestinal biopsies revealed by high-resolution magic-angle spinning 1 H NMR spectroscopy. J Proteome Res 2007;6:3944-51.  Back to cited text no. 22
    
23.
Wang Y, Carolan JC, Hao F, Nicholson JK, Wilkinson TL, Douglas AE. Integrated metabonomic-proteomic analysis of an insect-bacterial symbiotic system. J Proteome Res 2010;9:1257-67.  Back to cited text no. 23
    
24.
Trygg J, Wold S. Orthogonal projections to latent structure (O-PLS). J Chemom 2002;16:119-28.  Back to cited text no. 24
    
25.
Bylesjo M, Rantalainen M, Cloarec O, Nicholson JK, Holmes E, Trygg J. OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classification. J Chemom 2006;20:241-51.  Back to cited text no. 25
    
26.
Kochhar S, Jacobs DM, Ramadan Z, Berruex F, Fuerholz A, Fay LB. Probing gender-specific metabolism differences in humans by nuclear magnetic resonance-based metabonomics. Anal Biochem 2006;352:274-81.  Back to cited text no. 26
    
27.
Stella C, Beckwith-Hall B, Cloarec O, Holmes E, Lindon JC, Powell J, et al. Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res 2006;5:2780-8.  Back to cited text no. 27
    
28.
Wang Y, Tang H, Nicholson JK, Hylands PJ, Sampson J, Holmes E. A metabonomic strategy for the detection of the metabolic effects of chamomile (Matricaria recutita L.) ingestion. J Agric Food Chem 2005;53:191-6.  Back to cited text no. 28
    
29.
Cloarec O, Dumas ME, Trygg J, Craig A, Barton RH, Lindon JC, et al. Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1 H NMR spectroscopic metabonomic studies. Anal Chem 2005;77:517-26.  Back to cited text no. 29
    
30.
Watkins S, Sontheimer H. Unique biology of gliomas: Challenges and opportunities. Trends Neurosci 2012;35:546-56.  Back to cited text no. 30
    
31.
Chinnaiyan P, Kensicki E, Bloom G, Prabhu A, Sarcar B, Kahali S, et al. The metabolomic signature of malignant glioma reflects accelerated anabolic metabolism. Cancer Res 2012;72:5878-88.  Back to cited text no. 31
    
32.
Garber K. Energy boost: The Warburg effect returns in a new theory of cancer. J Natl Cancer Inst 2004;96:1805-6.  Back to cited text no. 32
    
33.
Qiu Y, Cai G, Su M, Chen T, Liu Y, Xu Y, et al. Urinary metabonomic study on colorectal cancer. J Proteome Res 2010;9:1627-34.  Back to cited text no. 33
    
34.
Lai HS, Lee JC, Lee PH, Wang ST, Chen WJ. Plasma free amino acid profile in cancer patients. Semin Cancer Biol 2005;15:267-76.  Back to cited text no. 34
    
35.
Denkert C, Budczies J, Weichert W, Wohlgemuth G, Scholz M, Kind T, et al. Metabolite profiling of human colon carcinoma – Deregulation of TCA cycle and amino acid turnover. Mol Cancer 2008;7:72.  Back to cited text no. 35
    
36.
Nomura DK, Long JZ, Niessen S, Hoover HS, Ng SW, Cravatt BF. Monoacylglycerol lipase regulates a fatty acid network that promotes cancer pathogenesis. Cell 2010;140:49-61.  Back to cited text no. 36
    
37.
Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL. Systems level studies of mammalian metabolomes: The roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 2011;40:387-426.  Back to cited text no. 37
    
38.
Connor SC, Hansen MK, Corner A, Smith RF, Ryan TE. Integration of metabolomics and transcriptomics data to aid biomarker discovery in type 2 diabetes. Mol Biosyst 2010;6:909-21.  Back to cited text no. 38
    
39.
Walenta S, Schroeder T, Mueller-Klieser W. Lactate in solid malignant tumors: Potential basis of a metabolic classification in clinical oncology. Curr Med Chem 2004;11:2195-204.  Back to cited text no. 39
    
40.
Glunde K, Serkova NJ. Therapeutic targets and biomarkers identified in cancer choline phospholipid metabolism. Pharmacogenomics 2006;7:1109-23.  Back to cited text no. 40
    
41.
Nomura DK, Dix MM, Cravatt BF. Activity-based protein profiling for biochemical pathway discovery in cancer. Nat Rev Cancer 2010;10:630-8.  Back to cited text no. 41
    
42.
Turcan S, Rohle D, Goenka A, Walsh LA, Fang F, Yilmaz E, et al. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 2012;483:479-83.  Back to cited text no. 42
    
43.
Ma Y, Zhang P, Yang Y, Wang F, Qin H. Metabolomics in the fields of oncology: A review of recent research. Mol Biol Rep 2012;39:7505-11.  Back to cited text no. 43
    
44.
Pan Z, Raftery D. Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Anal Bioanal Chem 2007;387:525-7.  Back to cited text no. 44
    


    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1]

This article has been cited by
1 Metabolomic signature of brain cancer
Renu Pandey,Laura Caflisch,Alessia Lodi,Andrew J. Brenner,Stefano Tiziani
Molecular Carcinogenesis. 2017; 56(11): 2355
[Pubmed] | [DOI]



 

Top
Print this article  Email this article
   
Online since 20th March '04
Published by Wolters Kluwer - Medknow