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Leichtle AB, Ceglarek U, Weinert P, et al. Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma. Metabolomics. 2012;9(3):677-687doi: 10.1007/s11306-012-0476-7.
Leichtle, A. B., Ceglarek, U., Weinert, P., Nakas, C. T., Nuoffer, J. M., Kase, J., Conrad, T., Witzigmann, H., Thiery, J., & Fiedler, G. M. (2013). Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma. Metabolomics : Official journal of the Metabolomic Society, 9(3), 677-687. https://doi.org/10.1007/s11306-012-0476-7
Leichtle, Alexander Benedikt, et al. "Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma." Metabolomics : Official journal of the Metabolomic Society vol. 9,3 (2013): 677-687. doi: https://doi.org/10.1007/s11306-012-0476-7
Leichtle AB, Ceglarek U, Weinert P, Nakas CT, Nuoffer JM, Kase J, Conrad T, Witzigmann H, Thiery J, Fiedler GM. Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma. Metabolomics. 2013 Jun;9(3):677-687. doi: 10.1007/s11306-012-0476-7. Epub 2012 Nov 06. PMID: 23678345; PMCID: PMC3651533.
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Metabolomics. 2013 Jun;9(3):677-687. doi: 10.1007/s11306-012-0476-7. Epub 2012 Nov 06.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma.

Metabolomics : Official journal of the Metabolomic Society

Alexander Benedikt Leichtle, Uta Ceglarek, Peter Weinert, Christos T Nakas, Jean-Marc Nuoffer, Julia Kase, Tim Conrad, Helmut Witzigmann, Joachim Thiery, Georg Martin Fiedler

Affiliations

  1. Center of Laboratory Medicine, University Institute of Clinical Chemistry, Inselspital-Bern University Hospital, Inselspital INO F 502/UKC, 3010 Bern, Switzerland.

PMID: 23678345 PMCID: PMC3651533 DOI: 10.1007/s11306-012-0476-7

Abstract

Metabolomics as one of the most rapidly growing technologies in the "-omics" field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Formula: see text] We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and-despite all its current limitations-can deliver marker panels with high selectivity even in multi-class settings.

Keywords: Amino acids; Marker panels; Metabolomics; Modeling; Pancreatic cancer

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