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

Zhou, Jixiang; Xu, Bin; Huang, Jing; Jia, Xiaoyue; Xue, Jing; Shi, Xin-Chang; Xiao, Liying; Li, Wei

doi:10.1016/j.cca.2008.10.030

Cited by 64 publications

(44 citation statements)

References 39 publications

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“…23,24 Thus, metabolomics is a powerful approach for examining disease-related metabolic changes and accordingly is effective for the identification of new biomarkers, [25][26][27] and as a potential prognostic tool in its own right. [28][29][30] Some recent studies have highlighted the potential for metabolomics analysis of blood samples for the detection of cancers, including epithelial ovarian 31 and oral 32 (using a 1 H-NMR-based platform), as well as prostate cancers 33 (using a mass spectrometry-based platform).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2010

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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 ¼ 29) exhibited higher concentrations of pyruvate and glutamate and decreased concentrations of isoleucine compared with controls (n ¼ 9). Differences in metabolic profiles between unmutated (UM-IGHV; n ¼ 10) and mutated IGHV (M-IGHV; n ¼ 19) patients were determined using partial least square discriminatory analysis (PLS-DA; R 2 ¼ 0.74, Q 2 ¼ 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 diseaserelated 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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2010

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“…Furthermore, an ideal cancer screening method should be accurate, non-invasive, and low-cost. Metabolomics or metabonomics (Sugimoto et al 2010;Tiziani et al 2009;Yan et al 2008;Zhou et al 2009) is a complementary approach for early detection of oral cancer (OSCC) which utilizes a novel and unique strategy that provides a coherent perspective of the complete metabolic response of organisms to pathophysiologic stimuli or genetic modification. Recent urinary metabonomic analysis demonstrates its applicability for the diagnosis and prognosis of disease (Kind et al 2007;Qiu et al 2010;Wu et al 2009a).…”

Section: Introductionmentioning

confidence: 99%

Urine metabolite profiling offers potential early diagnosis of oral cancer

et al. 2011

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Abstract:Oral cancer is the sixth most common human cancer, with a high morbidity rate and an overall 5-year survival rate of less than 50%. It is often not diagnosed until it has reached an advanced stage. Therefore, an early diagnostic and stratification strategy is of great importance for oral cancer. In the current study, urine samples of patients with oral squamous cell carcinoma (OSCC, n = 37), oral leukoplakia (OLK, n = 32) and healthy subjects (n = 34) were analyzed by gas chromatography-mass spectrometry (GC-MS). Using multivariate statistical analysis, the urinary metabolite profiles of OSCC, OLK and healthy control samples can be clearly discriminated and a panel of differentially expressed metabolites was obtained. Metabolites, valine and 6-hydroxynicotic acid, in combination yielded an accuracy of 98.9%, sensitivity of 94.4%, specificity of 91.4%, and positive predictive value of 91.9% in distinguishing OSCC from the controls. The combination of three differential metabolites, 6-hydroxynicotic acid, cysteine, and tyrosine, was able to discriminate between OSCC and OLK with an accuracy of 92.7%, sensitivity of 85.0%, specificity of 89.7%, and positive predictive value of 91.9%. This study demonstrated that the metabolite markers derived from this urinary metabolite profiling approach may hold promise as a diagnostic tool for early stage OSCC and its differentiation from other oral conditions.

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“…PLS-DA indicated the presence of group separation, as well as helped establish whether the separation between the clusters was significant through the plots of PLS-DA coefficient and the variable influence on projection (VIP). This method is more advantageous compared with PCA, as it can reduce the noise of two blocks of variables, identify the missing data and handle the colinearity among the variables (30,31).…”

Section: Pls-damentioning

confidence: 99%

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

et al. 2015

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Abstract.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, 1 H 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, 1 H NMR-based metabolomic analysis has a high potential for monitoring the formation of MDR during clinical tumor chemotherapy in the future.

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

Cited by 64 publications

References 39 publications

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

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

Urine metabolite profiling offers potential early diagnosis of oral cancer

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

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