Lipidomics as a Diagnostic Tool for Prostate Cancer

Simple Summary Prostate cancer (PCa) is one of the leading cancer deaths in man's world. Due to the lack of a fast and sensitive diagnostic method, PCa is only recognized in the late stadium of the disease. For this purpose, new tools are sought for sensitive diagnosis. One of them is the use of mass spectrometry. The main goal of the study was to perform target lipidomic analysis of prostate tissue with the use of two imaging methods: matrix-assisted laser desorption ionization with time-of-flight mass spectrometer, and electrospray ionization with triple quadrupole mass spectrometer. Statistical evaluation provided the knowledge about the phospholipids alteration linked with PCa progression. In practice, its recognition enables development of a quick and sensitive diagnostic method. The acquired knowledge may also lead to the increasement of hormone therapies effectiveness. The main goal of this study was to explore the phospholipid alterations associated with the development of prostate cancer (PCa) using two imaging methods: matrix-assisted laser desorption ionization with time-of-flight mass spectrometer (MALDI-TOF/MS), and electrospray ionization with triple quadrupole mass spectrometer (ESI-QqQ/MS). For this purpose, samples of PCa tissue (n = 40) were evaluated in comparison to the controls (n = 40). As a result, few classes of compounds, namely phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs), sphingomyelins (SMs), and phosphatidylethanolamines (PEs), were determined. The obtained results were evaluated by univariate (Mann-Whitney U-test) and multivariate statistical analysis (principal component analysis, correlation analysis, volcano plot, artificial neural network, and random forest algorithm), in order to select the most discriminative features and to search for the relationships between the responses of these groups of substances, also in terms of the used analytical technique. Based on previous literature and our results, it can be assumed that PCa is linked with both the synthesis of fatty acids and lipid oxidation. Among the compounds, phospholipids, namely PC 16:0/16:1, PC 16:0/18:2, PC 18:0/22:5, PC 18:1/18:2, PC 18:1/20:0, PC 18:1/20:4, and SM d18:1/24:0, were assigned as metabolites with the best discriminative power for the tested groups. Based on the results, lipidomics can be found as alternative diagnostic tool for CaP diagnosis.