Metabolic Signatures of Lung Cancer in Biofluids: NMR-Based Metabonomics of Urine

• Tipo: Artigo em Revista Científica Internacional
• Data: 2011

Autores

Carrola, J (Autor) Outra; Rocha, CM (Autor) Outra; Barros, AS (Autor) Outra; Gil, AM (Autor) Outra; Goodfellow, BJ (Autor) Outra; Carreira, IM (Autor) Outra; Bernardo, J (Autor) Outra; Gomes, A (Autor) Outra; Sousa, V (Autor) Outra; Carvalho, L (Autor) Outra; Duarte, IF (Autor) Outra

Revista

Título: Journal of Proteome Research

Vol. 10

Páginas: 221-230

ISSN: 1535-3893

Editora: American Chemical Society

Indexação

ISI Web of Knowledge - 196 Citações

Scopus - 203 Citações

Outras Informações

ID Authenticus: P-002-Z0J

DOI: 10.1021/pr100899x

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10