Qualitative and quantitative profiling of six different categories of urinary phospholipids (PLs) from patients with prostate cancer was performed to develop an analytical method for the discovery of candidate biomarkers by shotgun lipidomics method. Using nanoflow liquid chromatography-electrospray ionization-tandem mass spectrometry, we identified the molecular structures of a total of 70 PL molecules (21 phosphatidylcholines (PCs), 11 phosphatidylethanolamines (PEs), 17 phosphatidylserines (PSs), 11 phosphatidylinositols (PIs), seven phosphatidic acids, and three phosphatidylglycerols) from urine samples of healthy controls and prostate cancer patients by data-dependent collision-induced dissociation. Identified molecules were quantitatively examined by comparing the MS peak areas. From statistical analyses, one PC, one PE, six PSs, and two PIs among the PL species showed significant differences between controls and cancer patients (p < 0.05, Student's t test), with concentration changes of more than threefold. Cluster analysis of both control and patient groups showed that 18:0/18:1-PS and 16:0/22:6-PS were 99% similar in upregulation and that the two PSs (18:1/18:0, 18:0/20:5) with two PIs (18:0/18:1 and 16:1/20:2) showed similar (>95%) downregulation. The total amount of each PL group was compared among prostate cancer patients according to the Gleason scale as larger or smaller than 6. It proposes that the current study can be utilized to sort out possible diagnostic biomarkers of prostate cancer.

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A qualitative analysis tool (LiPilot) for identifying phospholipids (PLs), including lysophospholipids (LPLs), from biological mixtures is introduced. The developed algorithm utilizes raw data obtained from nanoflow liquid chromatography-electrospray ionization-tandem mass spectrometry experiments of lipid mixture samples including retention time and m/z values of precursor and fragment ions from data-dependent, collision-induced dissociation. Library files based on typical fragmentation patterns of PLs generated with an LTQ-Velos ion trap mass spectrometer are used to identify PL or LPL species by comparing experimental fragment ions with typical fragment ions in the library file. Identification is aided by calculating a confidence score developed in our laboratory to maximize identification efficiency. Analysis includes the influence of total ion intensities of matched and unmatched fragment ions, the difference in m/z values between observed and theoretical fragment ions, and a weighting factor used to differentiate regioisomers through data filtration. The present study focused on targeted identification of particular PL classes. The identification software was evaluated using a mixture of 24 PL and LPL standards. The software was further tested with a human urinary PL mixture sample, with 93 PLs and 22 LPLs identified.

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