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NMR metabolomics of human lung tumours reveals distinct metabolic signatures for adenocarcinoma and squamous cell carcinoma.

 Significance Statement

This paper describes the application of NMR metabolomics to the metabolic characterization of human lung tumours, focusing on the two most prevalent subtypes, adenocarcinoma (AdC) and squamous cell carcinoma (SqCC). Besides affording direct insight into cancer altered metabolism, this work has provided new, clear evidence of distinct metabolic behaviours for AdC and SqCC: main alterations in AdC were related to phospholipid and protein metabolisms, whereas SqCC was found to have stronger glycolytic and glutaminolytic profiles. These findings may potentially impact on diagnosis and offer important leads for future research on novel therapeutic targets or imaging tracers.

Figure legend

Correlation heat map colour-coded by the strength of Spearman correlation coefficients (r) between metabolites found to be important in tumour vs. control discrimination for adenocarcinoma and squamous cell carcinoma. Cut-off values of |r| > 0.7 and p < 0.004 (Bonferroni-corrected) have been used.

NMR metabolomics of human lung tumours reveals distinct metabolic signatures for adenocarcinoma and squamous cell carcinoma. Global Medical Discovery

 

 

 

 

 

 

 

Journal Reference

Rocha CM1, Barros AS2, Goodfellow BJ1, Carreira IM3, Gomes A4, Sousa V5, Bernardo J6, Carvalho L5, Gil AM1, Duarte IF7. Carcinogenesis. 2015 ;36(1):68-75.

Show Affiliations

1 QOPNA Research Unit, Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal,

2 Laboratory of Cytogenetics and Genomics, Faculty of Medicine, University of Coimbra, Azinhaga de Santa Comba, 3000–548 Coimbra, Portugal,

3 Centre for Investigation in Environment, Genetics and Oncobiology (CIMAGO), Faculty of Medicine, University of Coimbra,3004–504 Coimbra, Portugal,

4 Department of Pathological Anatomy, University Hospitals of Coimbra, 3000–075 Coimbra, Portugal,

5 Institute of Pathological Anatomy, Faculty of Medicine, University of Coimbra, Rua Larga, 3004–504 Coimbra, Portugal and

6 Cardiothoracic Surgery, University Hospitals of Coimbra, 3000-075 Coimbra, Portugal

Abstract

Lung tumour subtyping, particularly the distinction between adenocarcinoma (AdC) and  squamous  cell carcinoma (SqCC), is a critical diagnostic requirement. In this work, the metabolic signatures of  lung carcinomas were investigated through (1)H NMR metabolomics, with a view to provide additional criteria for improved diagnosis and treatment planning. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (NMR) spectroscopy was used to analyse matched tumour and adjacent control tissues from 56 patients undergoing surgical excision of primary lungcarcinomas. Multivariate modeling allowed tumour and control tissues to be discriminated with high accuracy (97% classification rate), mainly due to significant differences in the levels of 13 metabolites. Notably, the magnitude of those differences were clearly distinct for AdC and SqCC: major alterations in AdC were related to phospholipid metabolism (increased phosphocholine, glycerophosphocholine and phospho ethanolamine,  together with decreased acetate) and protein catabolism (increased peptide moieties), whereas SqCC had stronger glycolytic and glutaminolytic profiles (negatively correlated variations in glucose and lactate and positively correlated increases in glutamate and alanine). Other tumour metabolic features were increased creatine, glutathione, taurine and uridine nucleotides, the first two being especially prominent in SqCC and the latter in AdC. Furthermore, multivariate analysis of AdC and SqCC profiles allowed their discrimination with a 94% classification rate, thus showing great potential for aiding lung tumours subtyping. Overall, this study has provided new, clear evidence of distinct metabolic signatures for lung AdC and SqCC, which can potentially impact on diagnosis and provide important leads for future research on novel therapeutic targets or imaging tracers.

© The Author 2014. Published by Oxford University Press. All rights reserved.

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