Author(s): StruckLewicka W, Kordalewska M, Bujak R, Yumba Mpanga A, Markuszewski M,
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Abstract Prostate cancer (CaP) is a leading cause of cancer deaths in men worldwide. The alarming statistics, the currently applied biomarkers are still not enough specific and selective. In addition, pathogenesis of CaP development is not totally understood. Therefore, in the present work, metabolomics study related to urinary metabolic fingerprinting analyses has been performed in order to scrutinize potential biomarkers that could help in explaining the pathomechanism of the disease and be potentially useful in its diagnosis and prognosis. Urine samples from CaP patients and healthy volunteers were analyzed with the use of high performance liquid chromatography coupled with time of flight mass spectrometry detection (HPLC-TOF/MS) in positive and negative polarity as well as gas chromatography hyphenated with triple quadruple mass spectrometry detection (GC-QqQ/MS) in a scan mode. The obtained data sets were statistically analyzed using univariate and multivariate statistical analyses. The Principal Component Analysis (PCA) was used to check systems' stability and possible outliers, whereas Partial Least Squares Discriminant Analysis (PLS-DA) was performed for evaluation of quality of the model as well as its predictive ability using statistically significant metabolites. The subsequent identification of selected metabolites using NIST library and commonly available databases allows for creation of a list of putative biomarkers and related biochemical pathways they are involved in. The selected pathways, like urea and tricarboxylic acid cycle, amino acid and purine metabolism, can play crucial role in pathogenesis of prostate cancer disease. Copyright © 2014 Elsevier B.V. All rights reserved.
This article was published in J Pharm Biomed Anal
and referenced in Metabolomics:Open Access