Can nuclear magnetic resonance (NMR) spectroscopy reveal different metabolic signatures for lung tumours?

Duarte, Iola F.; Rocha, C.; Barros, António S.; Gil, Ana M.; Goodfellow, Brian J.; Carreira, Isabel M.; Bernardo, João; Gomes, Ana; Sousa, Vítor; Carvalho, Lina

doi:10.1007/s00428-010-0993-6

Cited by 35 publications

(40 citation statements)

References 33 publications

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“…With 1 H high-resolution magic angle spinning (HRMAS) NMR spectroscopy, Duarte et al observed the decreased level of glycine in tumor samples from lung cancer patients, suggesting that glycine might be involved in altered metabolisms in tumor tissues. 23 In our study, we observed specifically altered glycine level in serum samples from BC patients, which is consistent with the results of the previous study.…”

Section: Amino Acids Metabolismsupporting

confidence: 93%

NMR-based Metabolomic Analysis of Human Bladder Cancer

et al. 2012

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Section: Amino Acids Metabolismsupporting

confidence: 93%

NMR-based Metabolomic Analysis of Human Bladder Cancer

et al. 2012

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“…In fact, we have observed a trend for lipids to be increased in lung tumoral tissue (relative to noninvolved pulmonary parenchyma), 32,33 and although this increase could have multiple sources, including tissue necrosis, its possible relation with the observed changes in the plasma lipoprotein profile should not be ruled out. The poor nutritional status generally associated with cancer has also been reported to account for the reduction in HDL and LDL levels.…”

Section: Articlementioning

confidence: 94%

“…Lactate was found to be increased and glucose was found to be decreased in the plasma of cancer patients, most likely reflecting the well-known increased glycolytic activity of tumor cells (the Warburg effect), as confirmed by metabolic profiling of lung tissues. 32,33,46 Indeed, the standard measurement of glucose in serum samples from 300 fasted patients with a variety of cancer types also showed a significant reduction of glucose levels relative to the same number of gender-and age-matched controls. 44 Consistently with this enhanced glycolysis and as previously reported for leukemia, 47 kidney, 8 liver, 12 and colorectal 18 cancers, the glycolytic product pyruvate was found to be increased.…”

Section: Articlementioning

confidence: 99%

“…Following our previous studies on tissues collected from surgical specimens 32,33 and on urine from lung cancer patients and healthy subjects, 34 the present work aims to determine if the plasma metabolic composition reflects the presence of the disease and, thus, if nontargeted NMR-based profiling may be useful in lung cancer screening. Hence, preoperative plasma samples from patients with primary lung cancer (n = 85), not submitted to radiation or chemotherapy treatment, were analyzed by 1 H NMR, together with samples from healthy controls (n = 78), and their metabolic variations were assessed with the aid of multivariate statistical analysis.…”

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confidence: 99%

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

Rocha

Carrola²,

Barros³

et al. 2011

J. Proteome Res.

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151

108

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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

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“…Model derived features were selected as those with a VIP score >= 1.0. PLSDA is commonly used in metabolomics investigations including multiple forms of cancer, cardiac ischemia, parkinson's disease and asthma (Nishiumi et al, 2010;Bodi et al, 2012;Bogdanov et al, 2008;Carraro et al, 2007;Chen et al, 2011;Duarte et al, 2010;Qiu et al, 2010). This technique has also been previously implemented in our lab investigating hemorrhagic shock (Lexcen, Lusczek, Witowski, Mulier, & Beilman, 2012).…”

Section: Partial Least Squares Discriminant Analysis (Plsda)mentioning

confidence: 99%

Optimal Algorithm for Metabolomics Classification and Feature Selection varies by Dataset

Determan¹

2014

IJB

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Metabolomics, the systematic identification and quantification of all metabolites in a biological system, is increasingly applied towards identification of biomarkers for disease diagnosis, prognosis and risk prediction. Applications of metabolomics extend across the health spectrum including Alzheimer's, cancer, diabetes, and trauma. Despite the continued interest in metabolomics there are numerous techniques for analyzing metabolomics datasets with the intent to classify group membership (e.g. Control or Treated). These include Partial Least Squares Discriminant Analysis, Support Vector Machines, Random Forest, Regularized Generalized Linear Models, and Prediction Analysis for Microarrays. Each classification algorithm is dependent upon different assumptions and can potentially lead to alternate conclusions. This project seeks to conduct an in depth comparison of algorithm performance on both simulated and real datasets to determine which algorithms perform best given alternate dataset structures. Three simulated datasets were generated to validate algorithm performance and mimic 'real' metabolomics data: (Han et al., 2011) independent null dataset (no correlation, no discriminatory variables), (Davis, Schiller, Eurich, & Sawyer, 2012) correlated null (no discriminating variables), (Guan et al., 2009) correlated discriminatory. This comparison is also applied to 3 open-access datasets including two Nuclear Magnetic Resonance (NMR) and one Mass Spectrometry (MS) dataset. Performance was evaluated based on the Robustness-Performance-Trade-off (RPT) incorporating a balance between model classification accuracy and feature selection stability. We also provide a free, open-source R Bioconductor package (OmicsMarkeR) that conducts the analyses herein. The proposed work provides an important advancement in metabolomics analysis and helps alleviate the confusion of potentially paradoxical analyses thereby leading to improved exploration of disease states and identification of clinically important biomarkers.

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Can nuclear magnetic resonance (NMR) spectroscopy reveal different metabolic signatures for lung tumours?

Cited by 35 publications

References 33 publications

NMR-based Metabolomic Analysis of Human Bladder Cancer

NMR-based Metabolomic Analysis of Human Bladder Cancer

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

Optimal Algorithm for Metabolomics Classification and Feature Selection varies by Dataset

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