| Title |
Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma
|
|---|---|
| Published in |
Metabolomics, November 2012
|
| DOI | 10.1007/s11306-012-0476-7 |
| Pubmed ID | |
| Authors |
Alexander Benedikt Leichtle, Uta Ceglarek, Peter Weinert, Christos T. Nakas, Jean-Marc Nuoffer, Julia Kase, Tim Conrad, Helmut Witzigmann, Joachim Thiery, Georg Martin Fiedler |
| 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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Peru | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 1 | 2% |
| South Africa | 1 | 2% |
| Unknown | 53 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 35% |
| Student > Ph. D. Student | 10 | 18% |
| Student > Master | 6 | 11% |
| Student > Bachelor | 4 | 7% |
| Student > Doctoral Student | 3 | 5% |
| Other | 8 | 15% |
| Unknown | 5 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 24% |
| Medicine and Dentistry | 10 | 18% |
| Chemistry | 7 | 13% |
| Biochemistry, Genetics and Molecular Biology | 4 | 7% |
| Engineering | 2 | 4% |
| Other | 10 | 18% |
| Unknown | 9 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 November 2012.
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#18,320,524
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#1,070
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Outputs of similar age from Metabolomics
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