| Autor: |
Annerose, Seidel, Peter, Seidel, Olaf, Manuwald, Olf, Herbarth |
| Rok vydání: |
2013 |
| Předmět: |
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| Zdroj: |
Environmental toxicology. 30(8) |
| ISSN: |
1522-7278 |
| Popis: |
There is increasing worldwide interest in developing of markers for tumor diagnosis and identification of individuals who are at high cancer risk. Cancer, like other diseases accompanied by metabolic disorders, causes characteristic effects on cell turnover rate, activity of modifying enzymes, and RNA/DNA modifications. This results in an increased excretion of modified nucleosides in cancer patients. Therefore, for many years modified nucleosides have been suggested as tumor markers. The aim of the study was to elucidate further the usefulness of urinary nucleosides as possible markers at early detection of cancer in persons which are exposed against tumor promoting influences during their working life. Uranium miners are exposed to many kinds of pollutants that can cause health damage even lead to carcinogenesis. We analyzed modified nucleosides in urine samples from 92 miners who are at high risk for lung cancer to assess the levels of nucleosides by a multilayer perceptron (MLP) classifier - a neural network model. Eighteen nucleosides/metabolites were detected with reversed-phase high-pressure liquid chromatography (RP-HPLC). A valid set of urinary metabolites were selected and multivariate statistical technique of multilayer perceptron neural network were applied. In a previous study, MLP shows a sensitivity and specificity of 97 and 85%, respectively. MLP classification including the most relevant markers/nucleosides clearly demonstrates the elevation of RNA metabolism in miners, which is associated with possible malignant disease. We found that there were 30 subjects with early health disorders among 92 uranium workers based on MLP technique using modified nucleosides. The combination of RP-HPLC analysis of modified nucleosides and subsequent MLP analyses represents a promising tool for the development of a non-invasive prediction system and may assist in developing management and surveillance procedures. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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