Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics

• Published in: PloS one Vol. 7; no. 11; p. e48889
• Main Authors: Zhou, Xinchun, Mao, Jinghe, Ai, Junmei, Deng, Youping, Roth, Mary R, Pound, Charles, Henegar, Jeffrey, Welti, Ruth, Bigler, Steven A
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.11.2012; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Analysis; Antigens; Arabidopsis; Bioinformatics; Biological markers; Biology; Biomarkers; Biomarkers, Tumor - blood; Cancer; Cancer diagnosis; Cellular structure; Cluster analysis; Clustering; Computational Biology - methods; Computer Science; Diagnosis; Energy storage; Humans; Identification methods; Lecithin; Lipids; Lipids - blood; Male; Mass spectrometry; Mathematics; Medical diagnosis; Medicine; Membrane lipids; Metabolomics - methods; Middle Aged; Molecular structure; Pathology; Patients; Phosphatidylcholine; Phosphatidylethanolamine; Populations; Principal Component Analysis; Principal components analysis; Prognosis; Prostate cancer; Prostatic Neoplasms - blood; Prostatic Neoplasms - diagnosis; Scientific imaging; Sensitivity; Signaling; Species; Statistical analysis; Statistical methods; Statistics; Studies; United States > US; Mississippi; Kansas
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0048889

Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer. Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer. Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy.