المؤلفون: Carrola J; CICECO, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal., Rocha CM, Barros AS, Gil AM, Goodfellow BJ, Carreira IM, Bernardo J, Gomes A, Sousa V, Carvalho L, Duarte IF

المصدر: Journal of proteome research [J Proteome Res] 2011 Jan 07; Vol. 10 (1), pp. 221-30. Date of Electronic Publication: 2010 Nov 23.

نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't

بيانات الدورية: Publisher: American Chemical Society Country of Publication: United States NLM ID: 101128775 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1535-3907 (Electronic) Linking ISSN: 15353893 NLM ISO Abbreviation: J Proteome Res Subsets: MEDLINE

مواضيع طبية MeSH: Lung Neoplasms/*metabolism , Metabolomics/*methods , Nuclear Magnetic Resonance, Biomolecular/*methods , Urine/*chemistry, Adult ; Aged ; Aged, 80 and over ; Case-Control Studies ; Discriminant Analysis ; Female ; Humans ; Least-Squares Analysis ; Male ; Middle Aged ; Monte Carlo Method ; Multivariate Analysis ; Principal Component Analysis ; ROC Curve ; Reproducibility of Results

مستخلص: In this study, ¹H NMR-based metabonomics has been applied, for the first time to our knowledge, to investigate lung cancer metabolic signatures in urine, aiming at assessing the diagnostic potential of this approach and gaining novel insights into lung cancer metabolism and systemic effects. Urine samples from lung cancer patients (n = 71) and a control healthy group (n = 54) were analyzed by high resolution ¹H NMR (500 MHz), and their spectral profiles subjected to multivariate statistics, namely, Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Projections to Latent Structures (OPLS)-DA. Very good discrimination between cancer and control groups was achieved by multivariate modeling of urinary profiles. By Monte Carlo Cross Validation, the classification model showed 93% sensitivity, 94% specificity and an overall classification rate of 93.5%. The possible confounding influence of other factors, namely, gender and age, have also been modeled and found to have much lower predictive power than the presence of the disease. Moreover, smoking habits were found not to have a dominating influence over class discrimination. The main metabolites contributing to this discrimination, as highlighted by multivariate analysis and confirmed by spectral integration, were hippurate and trigonelline (reduced in patients), and β-hydroxyisovalerate, α-hydroxyisobutyrate, N-acetylglutamine, and creatinine (elevated in patients relatively to controls). These results show the valuable potential of NMR-based metabonomics for finding putative biomarkers of lung cancer in urine, collected in a minimally invasive way, which may have important diagnostic impact, provided that these metabolites are found to be specifically disease-related.