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.

MALDI-TOF MS analysis of urinary nucleosides.

As RNA turnover seems to be impaired in cancer patients, modified nucleosides have been evaluated as potential tumor markers. Modified nucleosides are mainly formed post-transcriptionally in tRNA, set free during RNA metabolism, and excreted in urine. Especially methylated nucleosides play an important role, as their levels are higher in urine from cancer patients. For structural elucidation of known and unknown nucleosides from urine samples of cancer patients, MALDI-TOF MS and MALDI-PSD were used for the first time. This technique generally ensures high sensitivity, mass resolution, and accuracy. In our analytical approach we prepurified nucleosides from urine by affinity chromatography and subsequently separated them by semipreparative high performance liquid chromatography. The different fractions were collected separately and analyzed by MALDI-TOF MS and PSD-MALDI using a mixture of six low molecular weight calibrants for internal or external calibration. The molecular totals formulas based on a mass accuracy of 10 ppm and below were calculated and a systematic data base search was performed. The inherent problem of the MALDI-technique, the reduced sensitivity for low molecular weight substances caused by matrix suppression effects, was reduced by our technique. We identified several nucleosides in urine, which were previously identified via retention times and UV spectra of standards after HPLC analysis. Eight further nucleosides were observed. This work demonstrates for the first time the potential of MALDI-TOF and PSD-MALDI in combination with semipreparative HPLC for assignment of nucleosides in urine. The particularly high mass accuracy of this mass spectrometric method provides opportunities for identifying unknown compounds.

Mass spectrometric identification of modified urinary nucleosides used as potential biomedical markers by LC-ITMS coupling.

In diseases accompanied by strong metabolic disorders, like cancer and AIDS, modifying enzymes are up- or down-regulated. As a result, many different types of metabolic end-products, including abnormal amounts of modified nucleosides, are found in urine. These nucleosides are degradation products of an impaired ribonucleic acid (RNA) metabolism, which affects the nucleoside pattern in urine. In several basic experiments we elucidated the fragmentation pathways of 16 characteristic nucleosides and six corresponding nucleic bases that occur in urine using electrospray ionization ion trap MS(5) (ESI-ITMS) experiments operated in positive ionization mode. For urinary nucleoside analysis, we developed an auto-LC-MS3 method based on prepurification via boronate gel affinity chromatography followed by reversed phase chromatography. For this purpose, an endcapped LiChroCART Superspher RP 18 column with a gradient of ammonium formate and a methanol-water mixture was used. This method gives a limit of detection of between 0.1 and 9.6 pmol for 15 standard nucleosides, depending on the basicity of the nucleoside. Overall, the detection of 36 nucleosides from urine was feasible. It was shown that this auto-LC-MS3 method is a valuable tool for assigning nucleosides from complex biological matrices, and it may be utilized in the diagnosis of diseases associated with disorders in RNA metabolism.

Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles.

Metabonomics, the study of metabolites and their roles in various disease states, is a novel methodology arising from the post-genomics era. This methodology has been applied in many fields, including work in cardiovascular research and drug toxicology. In this study, metabonomics method was employed to the diagnosis of Type 2 diabetes mellitus (DM2) based on serum lipid metabolites. The results suggested that serum fatty acid profiles determined by capillary gas chromatography combined with pattern recognition analysis of the data might provide an effective approach to the discrimination of Type 2 diabetic patients from healthy controls. And the applications of pattern recognition methods have improved the sensitivity and specificity greatly.

4-Heptanone is a metabolite of the plasticizer di(2-ethylhexyl) phthalate (DEHP) in haemodialysis patients.

BACKGROUND: There is an ongoing discussion about the risks of di(2-ethylhexyl) phthalate (DEHP) exposure for the general population as well as for specific subgroups in various medical settings. Haemodialysis patients certainly belong to the group with the highest exposure taking into account the repeated treatments over a long period of time. Many studies have shown that DEHP metabolites are more active with regard to cellular responses than DEHP itself. Although 4-heptanone has been shown to be a DEHP metabolite in rats, this has never been tested in humans. On the other hand, 4-heptanone was reported to be associated with diabetes mellitus. METHODS: After establishing analytical methods for all postulated metabolites, we analysed (i) plasma samples from 50 patients on haemodialysis and 50 controls; (ii) urine samples from 100 diabetic patients and 100 controls; and (iii) urine samples from 10 controls receiving DEHP intravenously. RESULTS: 4-Heptanone concentrations in urine did not differ between controls (128.6+/-11.4 micro g/l, mean+/- SEM) and diabetic patients (131.2+/-11.6 micro g/l) but were significantly elevated in plasma from haemodialysis patients (95.9+/-9.6 micro g/l) compared with controls (10.4+/-0.5 micro g/l). Exposure to DEHP led to a significant increase (P<0.001) of the metabolite 4-heptanone and all the proposed intermediates in urine of healthy persons within 24 h. CONCLUSIONS: These studies show that 4-heptanone is not associated with diabetes but is a major DEHP metabolite in humans. Studies concerning the toxicity of DEHP in haemodialysis patients and other highly exposed groups should therefore include 4-heptanone together with DEHP and its primary metabolites mono(2-ethylhexyl) phthalate (MEHP) and 2-ethylhexanol.

Urinary nucleosides as potential tumor markers evaluated by learning vector quantization.

Modified nucleosides were recently presented as potential tumor markers for breast cancer. The patterns of the levels of urinary nucleosides are different for tumor bearing individuals and for healthy individuals. Thus, a powerful pattern recognition method is needed. Although backpropagation (BP) neural networks are becoming increasingly common in medical literature for pattern recognition, it has been shown that often-superior methods exist like learning vector quantization (LVQ) and support vector machines (SVM). The aim of this feasibility study is to get an indication of the performance of urinary nucleoside levels evaluated by LVQ in contrast to the evaluation the popular BP and SVM networks. Urine samples were collected from female breast cancer patients and from healthy females. Twelve different ribonucleosides were isolated and quantified by a high performance liquid chromatography (HPLC) procedure. LVQ, SVM and BP networks were trained and the performance was evaluated by the classification of the test sets into the categories "cancer" and "healthy". All methods showed a good classification with a sensitivity ranging from 58.8 to 70.6% at a specificity of 88.4-94.2% for the test patterns. Although the classification performance of all methods is comparable, the LVQ implementations are superior in terms of more qualitative features: the results of LVQ networks are more reproducible, as the initialization is deterministic. The LVQ networks can be trained by unbalanced sizes of the different classes. LVQ networks are fast during training, need only few parameters adjusted for training and can be retrained by patterns of "local individuals". As at least some of these features play an important role in an implementation into a medical decision support system, it is recommended to use LVQ for an extended study.

Investigation of a capillary electrophoretic approach for direct quantification of apolipoprotein A-I in serum.

In the present study a rapid, reproducible and robust capillary electrophoresis (CE) procedure for the quantification of apolipoprotein A-I (Apo A-I) in serum without pretreatment has been developed (total run time, 11 min). The coefficients of variation (CV; n = 10) for the relative peak area are 1.8% at a concentration of 145 mg/dL and 1.6% at 196 mg/dL; and for the inter-assay 8.9% at 161 mg/dL (10 consecutive days), i.e., similar to the CVs of a high-throughput immunonephelometric routine assay. The CV for the migration time is 0.4% (n = 20). The robustness of the CE approach was tested in patient samples with hemolysis, hyperbilirubinemia and hyperlipidemia. A comparison of 99 Apo A-I serum values with results of a fixed-time immunonephelometric routine assay showed a positive constant bias of 60% (mean) for the immunonephelometric values, no deviation from linearity, but significant deviations in several samples. Investigations on interferences in the CE analyses gave no evidence that CE failed. Our study shows that CE is amenable to a fast analysis and a reproducible and reliable quantification of Apo A-I level in sera of various clinical samples.