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

Eleonora Amante, Alberto Salomone, Eugenio Alladio, Marco Vincenti*, Francesco Porpiglia, Rasmus Bro

*Corresponding author for this work

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Abstract

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.

Original languageEnglish
Article number3063
JournalMolecules
Volume24
Issue number17
Number of pages10
ISSN1420-3049
DOIs
Publication statusPublished - 2019

Keywords

  • Alignment
  • Gas chromatography-mass spectrometry (GC-MS)
  • PARAFAC2
  • Prostate carcinoma
  • Untargeted metabolomics

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