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.

This work aims at characterizing the metabolic profile of human lung cancer, to gain new insights into tumor metabolism and to identify possible biomarkers with potential diagnostic value in the future. Paired samples of tumor and noninvolved adjacent tissues from 12 lung tumors have been directly analyzed by (1)H HRMAS NMR (500/600 MHz) enabling, for the first time to our knowledge, the identification of over 50 compounds. The effect of temperature on tissue stability during acquisition time has also been investigated, demonstrating that analysis should be performed within less than two hours at low temperature (277 K), to minimize glycerophosphocholine (GPC) and phosphocholine (PC) conversion to choline and reduce variations in some amino acids. The application of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to the standard 1D (1)H spectra resulted in good separation between tumor and control samples, showing that inherently different metabolic signatures characterize the two tissue types. On the basis of spectral integration measurements, lactate, PC, and GPC were found to be elevated in tumors, while glucose, myo-inositol, inosine/adenosine, and acetate were reduced. These results show the valuable potential of HRMAS NMR-metabonomics for investigating the metabolic phenotype of lung cancer.

Lung cancer is one of the most prevalent and fatal types of cancer, with average 5-year survival rates lower than 15%, 1,2 which is mainly due to the advanced stage at which lung tumors are usually diagnosed. Indeed, when lung cancer is detected before metastasizing to lymph nodes or distant sites, the 5-year survival rates increase drastically to 60À80%, thus stressing the importance of early diagnosis. However, the majority of patients show no signs or symptoms during the initial phases of neoplastic growth, hindering early detection and the possibility of curative surgical treatment. Moreover, radiological tests, such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), which would allow the detection of initial cancer lesions, are not suitable for general screening of the population, mainly due to their high costs. Therefore, new methods that can aid in the early detection of lung cancer and contribute to improved prognosis are greatly needed.The search for metabolic markers of cancer in human tissues and biofluids has been the focus of a number of metabonomic studies in recent years. 3,4 In particular, metabolic profiling of blood plasma or serum has been increasingly used to unveil metabolic alterations associated with different cancer types, such as breast, 5À7 kidney, 8À10 liver, 11À13 prostate, 14À16 colorectal, 17À20 oral, 21,22 pancreatic, 23,24 esophageal, 25 and bone 26 cancers. In the case of lung cancer, only a few studies focusing on plasma or serum metabolic profiling have been recently reported. Maeda and co-workers proposed that the differences in the plasma amino acid profiles between healthy controls and non-small-cell lung cancer (NSCLC) patients, as assessed by targeted liquid chromatography coupled to mass spectrometry (LCÀMS), could potentially be useful for screening NSCLC. 27 In another MS study of specific compounds, namely, lysophosphatidylcholines (lysoPC), abnormal levels of lysoPC isomers with different fatty acyl positions were found in the plasma of lung cancer patients compared to controls. 28 Using a more global profiling approach, Jordan and colleagues reported the NMR analysis of paired tissue and serum samples from 14 subjects with two different lung cancer histological types (adenocarcinoma and squamous cell

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 lung carcinomas. 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 phosphoethanolamine, 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 aim of this work was to investigate the effects of cell handling and storage on cell integrity and 1 H high resolution magic angle spinning (HRMAS) NMR spectra. Three different cell types have been considered (lung tumoral, amniocytes, and MG-63 osteosarcoma cells) in order for sample-dependent effects to be identified. Cell integrity of fresh cells and cells frozen in cryopreservative solution was ∼70-80%, with the former showing higher membrane degradation, probably enzymatic, as indicated by increased phosphocholine (PC) and/or glycerophosphocholine (GPC). Unprotected freezing (either gradual or snap-freezing) was found to lyse cells completely, similar to mechanical cell lysis. Besides enhanced metabolites visibility, lysed cells showed a different lipid profile compared to intact cells, with increased choline, PC, and GPC and decreased phosphatidylcholine (PTC). Cell lysis has, therefore, a significant effect on cell lipid composition, making handling reproducibility an important issue in lipid analysis. Sample spinning was found to disrupt 5-25% of cells, depending on cell type, and HRMAS was shown to be preferable to solution-state NMR of suspensions or supernatant, giving enhanced information on lipids and comparable resolution for smaller metabolites. Relaxation-and diffusion-edited NMR experiments gave limited information on intact cells, compared to lysed cells. The 1 H HRMAS spectra of the three cell types are compared and discussed.Nuclear magnetic resonance (NMR) spectroscopy has been, in recent years, increasingly employed for the analysis of metabolic processes in biological systems because of its ability to provide rapid detection of many different metabolites present in complex systems such as biofluids, biological tissues, or cells. The analysis of the metabolome of biological systems provides important information on their biochemical phenotypes and on the metabolic changes occurring in response to external stimuli, e.g., drug exposure, disease onset, medication. 1,2The study of cellular metabolism using NMR has been successfully carried out with strong emphasis on cell extracts, either hydrophilic or lipophilic. For instance, acidic extracts, in the presence of ice-cold perchloric acid (PCA) or trichloroacetic acid (TCA), allow polar metabolites to be identified 3,4 as shown for PCA extracts of human colon adenocarcinoma cells 5 and human osteosarcoma cells 6,7 and TCA extracts of human rhabdomyosarcoma cells 8 and human lung cancer cells. 9 Other extraction methods have been used to identify aqueous and lipophilic metabolites, for instance in human colon carcinoma cells, 10 rat astrocyte cells, 11 human prostate cancer cells, 12 and human lung carcinoma cell lines. 13 In addition to the unavoidable selectivity of extraction methods, rendered useful only when the nature of the compounds of interest is known a priori, sample extraction may involve significant loss of particular cellular components, retained in the residual insoluble precipitate and not amenable to study by solution-st...

This study aims to evaluate the potential of (1)H NMR spectroscopy, combined with multivariate statistics, for discriminating between tumour and non-involved (control) pulmonary parenchyma and for providing biochemical information on different histological types. Paired tissue samples from 24 primary lung tumours were directly analysed by high-resolution magic angle spinning (HRMAS) (1)H NMR spectroscopy (500 MHz), and their spectral profiles subjected to principal component analysis (PCA) and partial least squares regression discriminant analysis (PLS-DA). Tumour and adjacent control parenchyma were clearly discriminated in the PLS-DA model with a high level of sensitivity (95% of tumour samples correctly classified) and 100% specificity (no false positives). The metabolites giving rise to this separation were mainly lactate, glycerophosphocholine, phosphocholine, taurine, reduced glutathione and uridine di-phosphate (elevated in tumours) and glucose, phosphoethanolamine, acetate, lysine, methionine, glycine, myo- and scyllo-inositol (reduced in tumours compared to control tissues). Furthermore, PLS-DA of a sub-set of tumour samples allowed adenocarcinomas to be discriminated from carcinoid tumours and epidermoid carcinomas, highlighting differences in metabolite levels between these histological types, and therefore revealing valuable knowledge on the biochemistry of different types of bronchial-pulmonary carcinomas.

The knowledge that the organism's metabolome is a potentially informative mirror of the impact of disease and its dynamics has led to promising developments in cancer research, strongly geared toward the discovery of new biomarkers of disease onset and progression. The present text reviews the advances made in the last 10 years in lung cancer research making use of the metabolomics strategies, particularly concerning metabolite profiling of human biofluids (blood serum and plasma, urine and others), expected to reflect the deviant metabolic behavior of lung tumors. The main goal of this article is to provide the reader with an up-to-date summary of the main metabolic variations taking place in biofluids, in relation to lung cancer, as well as of the analytical strategies employed to unveil them. Furthermore, particular needs and challenges are identified and possible developments envisaged.

This paper is aimed at understanding epileptic patient disorders through the analysis of surface electroencephalograms (EEG). It deals with the detection of spikes or spike-waves based on a nonorthogonal wavelet transform. A multilevel structure is described that locates the temporal segments where abnormal events occur. These events are then visually interpreted by means of a 3D mapping technique. This 3D display makes use of a ray tracing scheme and combines both the functional (the EEG but also its wavelet representation) and the morphological data (acquired from computed tomography [CT] or magnetic resonance imaging [MRI] devices). The results show that a significant reduction of the clinical workload is obtained while the most important episodes are better reviewed and analyzed.