Abstract
BackgroundBreast cancer belongs to the most frequent and severe cancer types in human. Since excretion of modified nucleosides from increased RNA metabolism has been proposed as a potential target in pathogenesis of breast cancer, the aim of the present study was to elucidate the predictability of breast cancer by means of urinary excreted nucleosides.MethodsWe analyzed urine samples from 85 breast cancer women and respective healthy controls to assess the metabolic profiles of nucleosides by a comprehensive bioinformatic approach. All included nucleosides/ribosylated metabolites were isolated by cis-diol specific affinity chromatography and measured with liquid chromatography ion trap mass spectrometry (LC-ITMS). A valid set of urinary metabolites was selected by exclusion of all candidates with poor linearity and/or reproducibility in the analytical setting. The bioinformatic tool of Oscillating Search Algorithm for Feature Selection (OSAF) was applied to iteratively improve features for training of Support Vector Machines (SVM) to better predict breast cancer.ResultsAfter identification of 51 nucleosides/ribosylated metabolites in the urine of breast cancer women and/or controls by LC- ITMS coupling, a valid set of 35 candidates was selected for subsequent computational analyses. OSAF resulted in 44 pairwise ratios of metabolite features by iterative optimization. Based on this approach ultimately estimates for sensitivity and specificity of 83.5% and 90.6% were obtained for best prediction of breast cancer. The classification performance was dominated by metabolite pairs with SAH which highlights its importance for RNA methylation in cancer pathogenesis.ConclusionExtensive RNA-pathway analysis based on mass spectrometric analysis of metabolites and subsequent bioinformatic feature selection allowed for the identification of significant metabolic features related to breast cancer pathogenesis. The combination of mass spectrometric analysis and subsequent SVM-based feature selection represents a promising tool for the development of a non-invasive prediction system.
Highlights
Breast cancer belongs to the most frequent and severe cancer types in human
Generating a valid metabolic profile Based on a set of 51 detectable urinary cis-diol metabolites in the applied sample volume of 1 mL urine, we first attempted to define a valid metabolic profile
In conclusion, we found a reasonable set of 44 tumorrelated metabolite pairs measured by LC-IT MS with a Support Vector Machines (SVM) prediction performance of 83.5% sensitivity and 90.6% specifity (p-value
Summary
Breast cancer belongs to the most frequent and severe cancer types in human. Since excretion of modified nucleosides from increased RNA metabolism has been proposed as a potential target in pathogenesis of breast cancer, the aim of the present study was to elucidate the predictability of breast cancer by means of urinary excreted nucleosides. The established markers of breast cancer (e.g. CA-15-3 and CEA) offer only unsatisfactory prediction accuracy and are not recommended for early diagnosis and therapy surveillance [2]. The coupling of liquid-, gas- or capillary liquid chromatography with mass spectrometric techniques like ESI-IT MS [9] and ESI tandem MS [10] has been established as method of choice. Systems such as ESI-TOF MS [11], MALDI-TOF MS [12] and especially FTICR MS [13] are valuable tools for the elucidation of chemical structures
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