New approach in determination of urinary diagnostic markers for prostate cancer by MALDI-TOF/MS.
Buszewska-Forajta M, Pomastowski P, Monedeiro F, Król-Górniak A, Adamczyk P, Markuszewski MJ, Buszewski B.
Talanta. 2022; 236(): 122843

Abstract

In our study, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS) is proposed as a novel tool, which can be applied to analyze lipids in urine samples. For this reason, the main aim of the study was to develop and optimize the preparation protocol for urine samples in lipidomics, using urine samples obtained from patients with diagnosed cancer and non-cancer controls. Several conditions like extraction method and types of matrices were evaluated. For this purpose, two methods for the extraction of lipids, namely modified Folch and Bligh & Dyer were employed. Furthermore, two types of matrices (alpha-cyano-4-hydroxycinnamic acid (HCCA) and 2,5-dihydroxybenzoic acid (DHB)) for the separation of lipids into individual components was tested. The results of this study can serve as an essential source for the selection of appropriate extraction methods and the appropriate choice of a matrix for the purification and identification of a particular class of lipid in human biological fluids. Based on it, Bligh & Dyer method associated with the usage of HCCA matrix was found to be the most effective for lipidomics using MALDI-TOF/MS. The optimized method was applied to compare the lipid profile of 139 urine samples collected from both healthy individuals and patients with prostate cancer. The tandem spectroscopic analysis allowed to identify lysophosphatidylcholine, phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, and triacylglycerols in urine samples. Finally, MALDI-TOF/MS analysis enabled to discriminate between the two tested groups (healthy individuals and patients with prostate cancer). A preliminary statistical model suggested that classification accuracy ranging from 83.3 to100.0% may be achieved by using pre-selected MS signals. CI - Copyright (c) 2021. Published by Elsevier B.V.



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