Correlation between chronological and physiological age of males from their multivariate urinary endogenous steroid profile and prostatic carcinoma-induced deviation

Comprehensive biomarker profiles and chemometric filtering of urinary metabolomics for effective discrimination of prostate carcinoma from benign hyperplasia

Prostate cancer (PCa) is the most commonly diagnosed cancer in male individuals, principally affecting men over 50 years old, and is the leading cause of cancer-related deaths. Actually, the measurement of prostate-specific antigen level in blood is affected by limited sensitivity and specificity and cannot discriminate PCa from benign prostatic hyperplasia patients (BPH). In the present paper, 20 urine samples from BPH patients and 20 from PCa patients were investigated to develop a metabolomics strategy useful to distinguish malignancy from benign hyperplasia. A UHPLC-HRMS untargeted approach was carried out to generate two large sets of candidate biomarkers. After mass spectrometric analysis, an innovative chemometric data treatment was employed involving PLS-DA classification with repeated double cross-validation and permutation test to provide a rigorously validated PLS-DA model. Simultaneously, this chemometric approach filtered out the most effective biomarkers and optimized their relative weights to yield the highest classification efficiency. An unprecedented portfolio of prostate carcinoma biomarkers was tentatively identified including 22 and 47 alleged candidates from positive and negative ion electrospray (ESI+ and ESI-) datasets. The PLS-DA model based on the 22 ESI+ biomarkers provided a sensitivity of 95 ± 1% and a specificity of 83 ± 3%, while that from the 47 ESI- biomarkers yielded an 88 ± 3% sensitivity and a 91 ± 2% specificity. Many alleged biomarkers were annotated, belonging to the classes of carnitine and glutamine metabolites, C21 steroids, amino acids, acetylcholine, carboxyethyl-hydroxychroman, and dihydro(iso)ferulic acid.

Optimization and validation of a GC-MS quantitative method for the determination of an extended estrogenic profile in human urine: Variability intervals in a population of healthy women

An analytical method based on GC-MS was developed for the determination of a wide panel of urinary estrogens, together with their principal metabolites. Because of the low concentration of estrogens in urine, an efficient sample pre-treatment was optimized by a design of experiment (DoE) procedure to achieve satisfactory sensitivity. A second DoE was built for the optimization of the chromatographic run, with the purpose of reaching the most efficient separation of analytes with potentially interfering ions and similar chromatographic properties. The method was fully validated using a rigorous calibration strategy: from several replicate analyses of blank urine samples spiked with the analytes, calibration models were built with particular attention to the study of heteroscedasticity and quadraticity. Other validation parameters, including the limit of detection, intra-assay precision and accuracy, repeatability, selectivity, specificity, and carry-over, were obtained using the same set of data. Further experiments were performed to evaluate matrix effect and extraction recovery. Then the urinary estrogen profiles of 138 post-menopausal healthy women were determined. These profiles provide a representation of physiological concentration ranges, which, in forthcoming studies, will be matched on the base of multivariate statistics with the urinary estrogenic profile of women with breast or ovarian cancer.

Experimental and statistical protocol for the effective validation of chromatographic analytical methods

The validation of analytical methods is of crucial importance in several fields of application. A new protocol for the validation of chromatographic methods has been proposed. The overall protocol is described in a parallel paper, where the case of a multi-targeted gas chromatography – mass spectrometry (GC–MS) method for the determination of androgens in human urine is in-depth discussed. The purpose of this paper is to report the details about the GC–MS separation and detection of the target analytes, and to provide the mathematical formulas needed to perform the validation of the principal parameters. Briefly, the validation protocol foresees the repetition of three calibration curves in three different days, providing a total amount of nine replicates. Such a structured design allows to use the same experiments to•

perform a rigorous calibration study, by the evaluation of heteroscedasticity, comparison of several weights and linear/quadratic calibration curves.

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determine several parameters which are traditionally computed from dedicated experiments, namely intra- and inter-day accuracy and precision, limit of detection, specificity, selectivity, ion abundance repeatability, and carry over.

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Finally, few further experiments are necessary to evaluate the retention time repeatability, matrix effect and extraction recovery.

Prospective evaluation of urinary steroids and prostate carcinoma-induced deviation: preliminary results

BACKGROUND

The serum prostate-specific antigen is the most widespread biomarker for prostate disease. Its low specificity for prostatic malignancies is a matter of concern and the reason why new biomarkers for screening purposes are needed. The correlation between altered production of the main steroids and prostate carcinoma (PCa) occurrence is historically known. The purpose of this study is to evaluate the modifications of a comprehensive urinary endogenous steroidal profile (USP) induced by PCa, by multivariate statistical methods.

METHODS

A total of 283 Italian subjects were included in the study, 139 controls and 144 PCa-affected patients. The USP, including 17 steroids and five urinary steroidal ratios, was quantitatively evaluated using gas chromatography coupled with single quadrupole mass spectrometry (GC-MS). The data were interpreted using a chemometric, multivariate approach (intrinsically more sensible to alterations with respect to traditional statistics) and a model for the discrimination of cancer-affected profiles was built.

RESULTS

Two multivariate classification models were calculated, the former including three steroids with the highest statistical significance (e.g. testosterone, etiocholanolone and 7β-OH-DHEA) and PSA values, the latter considering the three steroids' levels only. Both models yielded high sensitivity and specificity scores near to 70%, resulting significantly higher than PSA alone.

CONCLUSIONS

Three USP steroids resulted significantly altered in our PCa population. These preliminary results, combined with the simplicity and low-cost of the analysis, open to further investigation of the potential role of this restricted USP in PCa diagnosis.

Untargeted Metabolomic Profile for the Detection of Prostate Carcinoma-Preliminary Results from PARAFAC2 and PLS-DA Models

Prostate-specific antigen (PSA) is the main biomarker for the screening of prostate cancer (PCa), which has a high sensibility (higher than 80%) that is negatively offset by its poor specificity (only 30%, with the European cut-off of 4 ng/mL). This generates a large number of useless biopsies, involving both risks for the patients and costs for the national healthcare systems. Consequently, efforts were recently made to discover new biomarkers useful for PCa screening, including our proposal of interpreting a multi-parametric urinary steroidal profile with multivariate statistics. This approach has been expanded to investigate new alleged biomarkers by the application of untargeted urinary metabolomics. Urine samples from 91 patients (43 affected by PCa; 48 by benign hyperplasia) were deconjugated, extracted in both basic and acidic conditions, derivatized with different reagents, and analyzed with different gas chromatographic columns. Three-dimensional data were obtained from full-scan electron impact mass spectra. The PARADISe software, coupled with NIST libraries, was employed for the computation of PARAFAC2 models, the extraction of the significative components (alleged biomarkers), and the generation of a semiquantitative dataset. After variables selection, a partial least squares–discriminant analysis classification model was built, yielding promising performances. The selected biomarkers need further validation, possibly involving, yet again, a targeted approach.