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Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics

BACKGROUND: 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 mo...

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Autores principales: Zhou, Xinchun, Mao, Jinghe, Ai, Junmei, Deng, Youping, Roth, Mary R., Pound, Charles, Henegar, Jeffrey, Welti, Ruth, Bigler, Steven A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3495963/
https://www.ncbi.nlm.nih.gov/pubmed/23152813
http://dx.doi.org/10.1371/journal.pone.0048889
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author Zhou, Xinchun
Mao, Jinghe
Ai, Junmei
Deng, Youping
Roth, Mary R.
Pound, Charles
Henegar, Jeffrey
Welti, Ruth
Bigler, Steven A.
author_facet Zhou, Xinchun
Mao, Jinghe
Ai, Junmei
Deng, Youping
Roth, Mary R.
Pound, Charles
Henegar, Jeffrey
Welti, Ruth
Bigler, Steven A.
author_sort Zhou, Xinchun
collection PubMed
description BACKGROUND: 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. METHODOLOGY/PRINCIPAL FINDINGS: 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. CONCLUSIONS/SIGNIFICANCE: 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.
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spelling pubmed-34959632012-11-14 Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics Zhou, Xinchun Mao, Jinghe Ai, Junmei Deng, Youping Roth, Mary R. Pound, Charles Henegar, Jeffrey Welti, Ruth Bigler, Steven A. PLoS One Research Article BACKGROUND: 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. METHODOLOGY/PRINCIPAL FINDINGS: 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. CONCLUSIONS/SIGNIFICANCE: 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. Public Library of Science 2012-11-12 /pmc/articles/PMC3495963/ /pubmed/23152813 http://dx.doi.org/10.1371/journal.pone.0048889 Text en © 2012 Zhou et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Zhou, Xinchun
Mao, Jinghe
Ai, Junmei
Deng, Youping
Roth, Mary R.
Pound, Charles
Henegar, Jeffrey
Welti, Ruth
Bigler, Steven A.
Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics
title Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics
title_full Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics
title_fullStr Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics
title_full_unstemmed Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics
title_short Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics
title_sort identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3495963/
https://www.ncbi.nlm.nih.gov/pubmed/23152813
http://dx.doi.org/10.1371/journal.pone.0048889
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