Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics
News Apr 08, 2013
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.
The article is published online in the journal PLoS ONE and is free to access.
CRISPR Reveals New Targets for Promising Cancer DrugsNews
Novel screening method identifies new drug targets that could potentially enhance the effectiveness of PD-1 checkpoint inhibitors, a promising new class of cancer immunotherapy.READ MORE
New Algorithms Help Extract 3-D Biological Structure from Limited DataNews
CAMERA researchers capitalize on their Multi-Tiered Iterative Phasing approach to determine molecular structure of proteins and viruses from X-ray free electron laser data.READ MORE
Big-Data Analysis Points Toward New Drug Discovery MethodNews
A research team has developed a computational method to systematically probe massive amounts of open-access data to discover new ways to use drugs, including some that have already been approved for other uses.
Comments | 0 ADD COMMENT
EMBO Workshop: Integrating Systems Biology: From Networks to Mechanisms to Models
Apr 15 - Apr 17, 2018
EMBL Course: Introduction to Next Generation Sequencing
Apr 09 - Apr 12, 2018
EMBL Course: Introduction to Metabolomics Analysis
Mar 20 - Mar 23, 2018
EMBL Course: RNA Sequencing Library Preparation - How Low Can You Go?
Mar 19 - Mar 23, 2018
EMBL Course: Analysis and Integration of Transcriptome and Proteome Data
Mar 12 - Mar 16, 2018