Identification of plasma lipid species as promising diagnostic markers for prostate cancer (original) (raw)
Related papers
Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics
PLoS ONE, 2012
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.
Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Animal Science and Biotechnologies, 2016
Introduction: Lipidomics can offer an instant picture of the lipophilic metabolites from tissues and biofluids and can indicate the evolution of different pathologies, such as hyperplasia or different types of cancers. Related to these pathologies, Prostate Serum Antigen (PSA), proved to have a low grade prediction for an accurate diagnosis. Meanwhile, untargeted or targeted metabolomics became a useful advanced technology to discover new biomarkers for a better diagnosis.Aims: To realize an adequate procedure based on liquid chromatography coupled with mass spectrometry (HPLC-MS) to determine the profile of lipids from blood serum, followed by adequate biostatistics.Materials and Methods: Blood samples, obtained from healthy men and patients with prostate benign hyperplasia, post-biopsy cancer and post-surgery cancer were processed for lipid extraction and subjected to HPLC–ESI(+)QTOF-MS measurements, followed by the multivariate analysis (PCA and Cluster Analysis) with Unscrambl...
Lipidomics as a Diagnostic Tool for Prostate Cancer
Cancers
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...
2020
Dysregulated lipid metabolism is a prominent feature of prostate cancer that is driven by androgen receptor (AR) signaling. Herein, we used quantitative mass spectrometry to define the “lipidome” in prostate tumors with matched benign tissues (n=21), independent tissues (n=47), and primary prostate explants cultured with a clinical AR antagonist, enzalutamide (n=43). Significant differences in lipid composition were detected and spatially visualized in tumors compared to matched benign samples. Notably, tumors featured higher proportions of monounsaturated lipids overall and elongated fatty acid chains in phosphatidylinositol and phosphatidylserine lipids. Significant associations between lipid profile and malignancy were validated in unmatched samples, and PL composition was characteristically altered in patient tissues that responded to AR inhibition. Importantly, targeting of altered tumor-related lipid features, via inhibition of acetyl CoA carboxylase 1, significantly reduced c...
Lipid profiles of prostate cancer cells
Oncotarget
Lipids are important cellular components which can be significantly altered in a range of disease states including prostate cancer. Here, a unique systematic approach has been used to define lipid profiles of prostate cancer cell lines, using quantitative mass spectrometry (LC-ESI-MS/MS), FTIR spectroscopy and fluorescent microscopy. All three approaches identified significant difference in the lipid profiles of the three prostate cancer cell lines (DU145, LNCaP and 22RV1) and one non-malignant cell line (PNT1a). Specific lipid classes and species, such as phospholipids (e.g., phosphatidylethanolamine 18:1/16:0 and 18:1/18:1) and cholesteryl esters, detected by LC-ESI-MS/MS, allowed statistical separation of all four prostate cell lines. Lipid mapping by FTIR revealed that variations in these lipid classes could also be detected at a single cell level, however further investigation into this approach would be needed to generate large enough data sets for quantitation. Visualisation by fluorescence microscopy showed striking variations that could be observed in lipid staining patterns between cell lines allowing visual separation of cell lines. In particular, polar lipid staining by a fluorescent marker was observed to increase significantly in prostate cancer lines cells, when compared to PNT1a cells, which was consistent with lipid quantitation by LC-ESI-MS/MS and FTIR spectroscopy. Thus, multiple technologies can be employed to either quantify or visualise changes in lipid composition, and moreover specific lipid profiles could be used to detect and phenotype prostate cancer cells.
Metabolic markers in blood can separate prostate cancer from benign prostatic hyperplasia
British Journal of Cancer, 2015
Background: An individualised risk-stratified screening for prostate cancer (PCa) would select the patients who will benefit from further investigations as well as therapy. Current detection methods suffer from low sensitivity and specificity, especially for separating PCa from benign prostatic conditions. We have investigated the use of metabolomics analyses of blood samples for separating PCa patients and controls with benign prostatic hyperplasia (BPH). Methods: Blood plasma and serum samples from 29 PCa patient and 21 controls with BPH were analysed by metabolomics analysis using magnetic resonance spectroscopy, mass spectrometry and gas chromatography. Differences in blood metabolic patterns were examined by multivariate and univariate statistics. Results: By combining results from different methodological platforms, PCa patients and controls were separated with a sensitivity and specificity of 81.5% and 75.2%, respectively. Conclusions: The combined analysis of serum and plasma samples by different metabolomics measurement techniques gave successful discrimination of PCa and controls, and provided metabolic markers and insight into the processes characteristic of PCa. Our results suggest changes in fatty acid (acylcarnitines), choline (glycerophospholipids) and amino acid metabolism (arginine) as markers for PCa compared with BPH.
Identification of Plasma Glycosphingolipids as Potential Biomarkers for Prostate Cancer (PCa) Status
Biomolecules
Prostate cancer (PCa) is the most common male cancer and the second leading cause of cancer death in United States men. Controversy continues over the effectiveness of prostate-specific antigen (PSA) for distinguishing aggressive from indolent PCa. There is a critical need for more specific and sensitive biomarkers to detect and distinguish low- versus high-risk PCa cases. Discovery metabolomics were performed utilizing ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) on plasma samples from 159 men with treatment naïve prostate cancer participating in the North Carolina-Louisiana PCa Project to determine if there were metabolites associated with aggressive PCa. Thirty-five identifiable plasma small molecules were associated with PCa aggressiveness, 15 of which were sphingolipids; nine common molecules were present in both African-American and European-American men. The molecules most associated with PCa aggressiveness were glycosphingolipids; levels of trih...
Journal of Proteome Research, 2014
Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men. The prevalent diagnosis method is based on the serum prostate-specific antigen (PSA) screening test, which suffers from low specificity, overdiagnosis, and overtreatment. In this work, untargeted metabolomic profiling of age-matched serum samples from prostate cancer patients and healthy individuals was performed using ultraperformance liquid chromatography coupled to high-resolution tandem mass spectrometry (UPLC-MS/MS) and machine learning methods. A metabolite-based in vitro diagnostic multivariate index assay (IVDMIA) was developed to predict the presence of PCa in serum samples with high classification sensitivity, specificity, and accuracy. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent PSA test. Within the discriminant panel, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed chromatographically by standards. Numerous discriminant metabolites were mapped in the steroid hormone biosynthesis pathway. The identification of fatty acids, amino acids, lysophospholipids, and bile acids provided further insights into the metabolic alterations associated with the disease. With additional work, the results presented here show great potential toward implementation in clinical settings.
Association of Prostate Cancer and Lipid Profile: A Case-Control Study
Journal of Medical and Health Studies
There are inconsistent findings concerning the association between the serum concentrations of lipid parameters and prostate cancer (PCa), particularly in Caucasian men. There is limited data on men of African ancestry. The study examined the relationship between serum total cholesterol (TC) levels and its fractions and PCa in a hospital-based case-control study in Jamaica. The serum levels of TC, triglycerides (TG), HDL-cholesterol (HDL-C), and LDL-cholesterol (LDL-C) in 46 male patients (cases) who underwent prostate biopsy were measured over an eighteen month period. There were 32 patients without PCa who served as controls. The serum lipid concentrations between cases and controls were compared using an independent samples t-test. Multiple linear regression and binary logistic regression were used to assess the relationship between lipids and overall PCa, as well as disease severity. Based on the results, there were no significant differences between the concentrations of lipids...