Untargeted metabolomics of prostate cancer zwitterionic and positively charged compounds in urine

Prostate cancer, a leading cause of cancer-related deaths worldwide, principally occurs in over 50-yearold men. Nowadays there is urgency to discover biomarkers alternative to prostate-specific antigen, as it cannot discriminate patients with benign prostatic hyperplasia from clinically significant forms of prostatic cancer. In the present paper, 32 benign prostatic hyperplasia and 41 prostatic cancer urine samples were collected and analyzed. Polar and positively charged metabolites were therein investigated using an analytical platform comprising an up to 40-fold analyte enrichment step by graphitized carbon black solid-phase extraction, HILIC separation, and untargeted high-resolution mass spectrometry analysis. These classes of compounds are often neglected in common metabolomics experiments even though previous studies reported their significance in cancer biomarker discovery. The complex metabolomics big datasets, generated by the UHPLC-HRMS, were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest data and their analysis by the Multivariate CurveResolution Alternating Least Squares chemometrics method. This approach allowed the resolution and tentative identification of the metabolites differentially expressed by the two data sets. Among these, amino acids and carnitine derivatives were tentatively identified highlighting the importance of the proposed methodology for cancer biomarker research. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study focused on analyzing polar and positively charged metabolites in urine samples from benign prostatic hyperplasia and prostatic cancer patients, utilizing an analytical platform with up to 40-fold analyte enrichment. By applying the ROIMCR procedure to complex metabolomics datasets generated by UHPLC-HRMS, differential metabolites were successfully resolved and tentatively identified, underscoring the importance of this approach in cancer biomarker research. The study highlights the significance of previously neglected metabolite classes in cancer biomarker discovery and the value of the proposed methodology.