Prediction of breast cancer by profiling of urinary RNA metabolites using Support Vector Machine-based feature selection.

BACKGROUND: 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. METHODS: We 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. RESULTS: After 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. CONCLUSION: Extensive 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.

Identification of urinary modified nucleosides and ribosylated metabolites in humans via combined ESI-FTICR MS and ESI-IT MS analysis.

The physiological response of the human body to several diseases can be reflected by the metabolite pattern in biological fluids. 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 altered excretion of modified nucleosides and biochemically related compounds. In the course of our metabolic profiling project, we screened 24-h urine of patients suffering from lung, rectal, or head and neck cancer for previously unknown ribosylated metabolites. Therefore, we developed a sample preparation procedure based on boronate affinity chromatography followed by additional prepurification with preparative TLC. The isolated metabolites were analyzed by ion trap mass spectrometry (IT MS) and Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS). IT MS was applied for LC-auto MS(3) screening runs and MS(n(n=4-6)) syringe pump infusion experiments, yielding characteristic fragmentation patterns. FTICR MS measurements enabled the calculation of corresponding molecular formulae based on accurate mass determination (mass accuracy: 1-5 ppm for external and sub-ppm values for internal calibration). We were able to identify 22 metabolites deriving from cellular RNA metabolism and related metabolic pathways like histidine metabolism, purine biosynthesis, methionine/polyamine cycle, and nicotinate/nicotinamide metabolism. The compounds 1-ribosyl-3-hydroxypyridinium, 1-ribosyl-pyridinium, and 3-ribosyl-1-methyl-l-histidinium as well as a series of ribosylated histamines, conjugated to carboxylic acids at the N(omega)-position were found as novel urinary constituents. The occurrence of the modified nucleosides 2-methylthio-N(6)-(cis-hydroxyisopentenyl)-adenosine, 5-methoxycarbonylmethyl-2-thiouridine, N(6)-methyl-N(6)-threonylcarbamoyladenosine, and 2-methylthio-N(6)-threonylcarbamoyladenosine in human urine is verified for the first time.

Bioinformatical evaluation of modified nucleosides as biomedical markers in diagnosis of breast cancer.

It is known that patients suffering from cancer diseases excrete increased amounts of modified nucleosides with their urine. Especially methylated nucleosides have been proposed to be potential tumor markers for early diagnosis of cancer. For determination of nucleosides in randomly collected urine samples, the nucleosides were extracted using affinity chromatography and then analyzed via reversed phase high-performance liquid chromatography (HPLC) with UV-detection. Eleven nucleosides were quantified in urine samples from 51 breast cancer patients and 65 healthy women. The measured concentrations were used to train a Support Vector Machine (SVM) and a k-nearest-neighbor classifier (k-NN) to discriminate between healthy control subjects and patients suffering from breast cancer. Evaluations of the learned models by computing the leave-one-out error and the prediction error on an independent test set of 29 subjects (15 healthy, 14 breast cancer patients) showed that by using the eleven nucleosides, the occurrence of breast cancer could be forecasted with 86% specificity and 94% sensitivity when using an SVM and 86% for both specificity and sensitivity with the k-NN model.

Metabonomics in cancer diagnosis: mass spectrometry-based profiling of urinary nucleosides from breast cancer patients.

Modified nucleosides are formed post-transcriptionally in RNA. In cancer disease, the cell turnover and thus RNA metabolism is increased, yielding higher concentrations of excreted modified nucleosides. In the presented study, urinary ribonucleosides were used to differentiate between breast cancer patients and healthy volunteers. The nucleosides were extracted from urine samples using affinity chromatography and subsequently analyzed via liquid chromatography ion trap mass spectrometry (LC-IT-MS). The peak areas were related to the internal standard isoguanosine and to the urinary creatinine level. For bioinformatic pattern recognition we used the support vector machine. We examined 113 urine samples from breast cancer patients (stage Tis-T4) and 99 control samples from healthy volunteers. We achieved a sensitivity of 87.67% and a specificity of 89.90% when including 31 nucleosides. The medical metabonomics concept based on the urinary nucleoside profile reveals a significantly improved classification compared with currently applied breast cancer biomarkers such as CA15-3.

Metabolic signature of breast cancer cell line MCF-7: profiling of modified nucleosides via LC-IT MS coupling.

BACKGROUND: Cancer, like other diseases accompanied by strong metabolic disorders, shows characteristic effects on cell turnover rate, activity of modifying enzymes and DNA/RNA modifications, resulting also in elevated amounts of excreted modified nucleosides. For a better understanding of the impaired RNA metabolism in breast cancer cells, we screened these metabolites in the cell culture supernatants of the breast cancer cell line MCF-7 and compared it to the human mammary epithelial cells MCF-10A. The nucleosides were isolated and analyzed via 2D-chromatographic techniques: In the first dimension by cis-diol specific boronate affinity extraction and subsequently by reversed phase chromatography coupled to an ion trap mass spectrometer. RESULTS: Besides the determination of ribonucleosides, additional compounds with cis-diol structure, deriving from cross-linked biochemical pathways, like purine-, histidine- and polyamine metabolism were detected. In total, 36 metabolites were identified by comparison of fragmentation patterns and retention time. Relation to the internal standard isoguanosine yielded normalized area ratios for each identified compound and enabled a semi-quantitative metabolic signature of both analyzed cell lines.13 of the identified 26 modified ribonucleosides were elevated in the cell culture supernatants of MCF-7 cells, with 5-methyluridine, N2,N2,7-trimethylguanosine, N6-methyl-N6-threonylcarbamoyladenosine and 3-(3-aminocarboxypropyl)-uridine showing the most significant differences. 1-ribosylimidazole-4-acetic acid, a histamine metabolite, was solely found in the supernatants of MCF-10A cells, whereas 1-ribosyl-4-carboxamido-5-aminoimidazole and S-adenosylmethionine occurred only in supernatants of MCF-7 cells. CONCLUSION: The obtained results are discussed against the background of pathological changes in cell metabolism, resulting in new perspectives for modified nucleosides and related metabolites as possible biomedical markers for breast carcinoma in vivo.

Comprehensive authentication of (E)-alpha(beta)-ionone from raspberries, using constant flow MDGC-C/P-IRMS and enantio-MDGC-MS.

A new coupling system of GC-GC, connected via a Multi Column Switching Device MCS2 for measuring isotope ratios, is introduced. By means of several standard substances the precise and accurate measurement of isotopic values is proved. First applications concerning the authentication of raspberry aroma compounds are established. Consequently, the combination of constant flow multidimensional gas chromatography-combustion/pyrolysis-isotope ratio mass spectrometry (MDGC-C/P-IRMS) is applied to the authenticity assessment of (E)-alpha(beta)-ionone from six different raspberry cultivars. Furthermore, 12 commercially available raspberry products and samples of (E)-alpha(beta)-ionone, some declared to be natural, are investigated. delta(2)Eta(V)(-)(SMOW) and delta(13)C(V)(-)(PDB) values of (E)-alpha(beta)-ionone are determined, and characteristic authenticity ranges were concluded from raspberries by correlation of both delta(2)Eta(V)(-)(SMOW) and delta(13)C( V)(-)(PDB) values. The results are correlated with the determination of enantiomeric purities of (E)-alpha-ionone, using stir bar sorptive extraction enantio-multidimensional gas chromatography mass spectrometry (SBSE-enantio-MDGC-MS).