Metabolomic markers of altered nucleotide metabolism in early stage adenocarcinoma

Abstract

Adenocarcinoma, a type of non-small cell lung cancer, is the most frequently diagnosed lung cancer and the leading cause of lung cancer mortality in the United States. It is well documented that biochemical changes occur early in the transition from normal to cancer cells, but the extent to which these alterations affect tumorigenesis in adenocarcinoma remains largely unknown. Herein, we describe the application of mass spectrometry and multivariate statistical analysis in one of the largest biomarker research studies to date aimed at distinguishing metabolic differences between malignant and nonmalignant lung tissue. Gas chromatography time-of-flight mass spectrometry was used to measure 462 metabolites in 39 malignant and nonmalignant lung tissue pairs from current or former smokers with early stage (stage IA-IB) adenocarcinoma. Statistical mixed effects models, orthogonal partial least squares discriminant analysis and network integration, were used to identify key cancer-associated metabolic perturbations in adenocarcinoma compared with nonmalignant tissue. Cancer-associated biochemical alterations were characterized by (i) decreased glucose levels, consistent with the Warburg effect, (ii) changes in cellular redox status highlighted by elevations in cysteine and antioxidants, alpha- and gamma-tocopherol, (iii) elevations in nucleotide metabolites 5,6-dihydrouracil and xanthine suggestive of increased dihydropyrimidine dehydrogenase and xanthine oxidoreductase activity, (iv) increased 5'-deoxy-5'-methylthioadenosine levels indicative of reduced purine salvage and increased de novo purine synthesis, and (v) coordinated elevations in glutamate and UDP-N-acetylglucosamine suggesting increased protein glycosylation. The present study revealed distinct metabolic perturbations associated with early stage lung adenocarcinoma, which may provide candidate molecular targets for personalizing therapeutic interventions and treatment efficacy monitoring.

Figure 1

Metabolomic network of biochemical differences between adenocarcinoma and non-malignant lung tissue. Edge color and width denote the type (enzymatic, purple; structural similarity, gray) and strength of relationships between metabolites. Node color displays significance (mixed effects model, pFDR ≤ 0.05) and direction of the change in tumor relative to non-malignant tissue (green, decrease; red, increase; gray, insignificant change) (Table 2 and Table S1). Node size displays O-PLS-DA loadings (empirical importance), and thick borders indicate O-PLS-DA selected discriminants for adenocarcinoma (Table 2). See metabolite O-PLS-DA model loading in Table S4 for quantitative differences in metabolites and corresponding node sizes. Node shape denotes the biochemical super class of each molecule.

Figure 2

Partial correlation network displaying conditionally independent relationships between O-PLS-DA selected discriminants for adenocarcinoma (Table 2). Edge color and width denote the direction and magnitude of partial correlations (pFDR≤0.05). Node color displays the direction of the change in tumor relative to non-malignant tissue (green, decrease; red, increase; pFDR ≤ 0.05). Node size displays the metabolite loading (empirical importance) in the O-PLS-DA model and shape denotes the biochemical super class of each molecule. See metabolite O-PLS-DA model loading in Table S4 for quantitative differences in metabolites and corresponding node sizes. Node inset boxplots summarize differences in z-scaled measurements between tumor and non-malignant tissue.