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

Abstract

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.

Urinary metabolomic changes as a predictive biomarker of asthma exacerbation

“…All samples were analyzed by using solid-phase microextraction (SPME)/GC 3 GC-ToFMS, 4 and samples from 5 of 10 subjects were also analyzed by using 1 H-NMR spectroscopy (see the Methods section in this article's Online Repository). 5 Exploratory principal component analysis (PCA) was applied to GC 3 GC peak areas, as well as to 1 H-NMR spectra, to extract the metabolic features of the exacerbation (see the Methods section in this article's Online Repository).…”

“…1 We 2 and others [3][4][5][6][7] have also reported chronic loss of lung elastic recoil in clinically stable, nonsmoking, adult patients with moderate-to-severe asthma with persistent expiratory airflow limitation that may be partially reversible with treatment. The mechanism(s) responsible for the loss of lung elastic recoil in acute 1 and chronic asthma 2-7 remains unknown, especially with normal diffusing capacity and normal or only mild parenchymal attenuation of lung density on highresolution thin-section lung computed tomography (CT) at full inspiration.…”

Cordyceps sinensis protects against liver and heart injuries in a rat model of chronic kidney disease: a metabolomic analysis

“…Quantitative comparison of metabolites in 1 H NMR spectra of liver and heart aqueous extract samples The average changes of metabolites between two groups were calculated [35] . Box charts were used to depict the variation in the integrals of the most significant metabolites in the CS, OP, and SO groups, which are shown in Figure 4.…”

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

“…NMR analytics have been used for biomarker detection in humans in several tumor entities other than PCa (Table 2): non-small-cell lung cancer (Doskocz et al, 2015), oral squamous cell carcinoma (OSCC) , gastric cancer (Jung et al, 2014), myeloma (Lodi et al, 2013), pancreatic ductal adenocarcinoma (Davis et al, 2013), lung cancer (Carrola et al, 2011), and BCa (Bansal et al, 2013).…”

“…PCA: principal component analysis; PLS-DA: partial least square-discriminant analysis; OPLS-DA: orthogonal projection to latent structure discriminant analysis cancer, illustrating the major problem of current metabolomic biomarker studies, patient pre-selection (Chan et al, 2016). Carrola et al (2011) reported on urine analysis for lung cancer patients using 1 H-NMR combined with PCA, PLS-DA, and orthogonal partial minimum variance discriminant analysis. The results showed that the development process of lung cancer was closely related to potential biological markers, including hippurate, gourd trigonelline, β-hydroxyisovaleric acid, α-hydroxyisobutyric acid, N-aceglutamide, and creatine anhydride.…”