abstract
In this study, H-1 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 H-1 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 beta-hy-droxyisovalerate, alpha-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.
keywords
NUCLEAR-MAGNETIC-RESONANCE; H-1-NMR SPECTROSCOPY; MASS-SPECTROMETRY; BLADDER-CANCER; PROGRESSION; BIOMARKER; SARCOSINE; PROFILES; TUMORS
subject category
Biochemistry & Molecular Biology
authors
Carrola, J; Rocha, CM; Barros, AS; Gil, AM; Goodfellow, BJ; Carreira, IM; Bernardo, J; Gomes, A; Sousa, V; Carvalho, L; Duarte, IF
our authors
Ana Maria Pissarra Coelho Gil
Associate Professor with Aggregation
António de Sousa Barros
Post-doc Fellowship
Brian James Goodfellow
Assistant Professor
Iola Melissa Fernandes Duarte
Principal Researcher
Joana Carolina Quintela Carrola
PhD Studentacknowledgements
Funding is acknowledged from the Foundation for Science and Technology (FCT), Portugal, and FEDER (research project FCT/PTDC/QUI/68017/2006; FCOMP-01-0124-FEDER-007439). C.M.R. further acknowledges FCT for the doctoral grant SFRH/BD/63430/2009, and I.F.D. acknowledges L'Oreal Portugal, FCT and the National UNESCO Committee for funding this work through the L'Oreal Medals of Honor for Women in Science 2007. The authors are also grateful to all the volunteers who participated in this study, as well as to Dr. Jorge Ferreira and Eng. Nelson Ramos from INDASA S.A. for collaboration in the collection of control samples. Finally, Bruker BioSpin GmbH is acknowledged for access to BBIOREFCODE.