Prostate Cancer Associated Lipid Signatures in Serum Studied by ESI-Tandem Mass Spectrometryas Potential New Biomarkers (original) (raw)
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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.
Identification of plasma lipid species as promising diagnostic markers for prostate cancer
BMC Medical Informatics and Decision Making, 2020
Background Prostate cancer is a very common and highly fatal in men. Current non-invasive detection methods like serum biomarker are unsatisfactory. Biomarkers with high accuracy for diagnostic of prostate cancer are urgently needed. Many lipid species have been found related to various cancers. The purpose of our study is to explore the diagnostic value of lipids for prostate cancer. Results Using triple quadruple liquid chromatography electrospray ionization tandem mass spectrometry, we performed lipidomics profiling of 367 lipids on a total 114 plasma samples from 30 patients with prostate cancer, 38 patients with benign prostatic hyperplasia (BPH), and 46 male healthy controls to evaluate the lipids as potential biomarkers in the diagnosis of prostate cancer. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was used to construct the potential mechanism pathway. After statistical analysis, five lipids were identified as a panel of potential biomarkers for the detec...
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.
Prostate cancer has an increasing incidence and there is an urgent need for development of new serum biomarkers for early diagnostic as the ones known are ineffective. The aim of the study was to use untargeted metabolomics in order to identify and characterize small metabolite fingerprints in patients with normal vs pathologic values of PSA (previously determined by electrochemiluminiscence). A cohort of one hundred patients with different Prostate Specific amtigen values were investigated by untargeted metabolomics. The serum small metabolite profile determined by high performance liquid chromatography coupled with mass spectrometry, LC-QTOF(ESI +)MS in order to identify specific biomarkers, for normal patient group (PSA = 0-4 ng.ml) and four pathologic groups, having PSA values from 4 to >1000 ng/ml. The major molecules identified in the samples were polar phospholipids, maily lysophosphatidyl choline derivatives, having m/z values from 496 to 524, like LPC(O-16:0/O-1:0), LPC(18:1/2:0) or PS(18:1(9Z)/0:0), LPC(18:2(9Z,12Z)/0:0 and their isomers and LPC(O-18:1(11Z)/2:0), respectively. Also, small molecules (free fatty acids and prostaglandin derivatives) were identified and are significantly different in pathologic vs normal serum samples. Generally the pathologic samples had increased concentrations of all above mentioned molecules. The Principal Component analysis showed , by plot and loadings scores, significant clustering of normal vs pathological groups.
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.
Mass spectrometry-based metabolomic profiling of prostate cancer -a pilot study
Journal of Cancer Metastasis and Treatment , 2019
Aim: Prostate cancer (PCa) is the most commonly diagnosed non-skin cancer among men. Serum prostate-specific antigen level is used as a standard PCa biomarker for over 20 years. However, it has only 33% specificity and 86% sensitivity (for the cutoff value for prostate biopsy of > 4 ng/mL). This leads to overdiagnosis and overtreatment. In-depth insight into PCa metabolomics enables discovery of novel PCa biomarkers. Methods: Metabolomic alternation in PCa serum, urine and interstitial fluid was examined using gold-nanoparticle-based laser mass spectrometry imaging. This study included 5 patients who underwent prostate biopsy with positive result, 5 patients with negative result and 10 healthy controls. Results: Over two hundred differentiating metabolites (87 in urine, 54 in serum and 78 in interstitial fluid) were detected. Four, twenty two and ten metabolites from urine, serum and interstitial fluid respectively showed statistical significant differential abundance between cancer and control group. Conclusion: Comprehensive metabolomic profile of PCa has been identified. Out of 36 metabolites, 20 were identified and should be further evaluated in clinical trials as a potential PCa biomarker. Urine concentration of triglyceride (12:0/20:1) showed over 10 times higher abundance in PCa samples in comparison to healthy controls and is considered the most promising potential biomarker.
Proceedings of the National Academy of Sciences of the United States of America, 2017
Accurate identification of prostate cancer in frozen sections at the time of surgery can be challenging, limiting the surgeon's ability to best determine resection margins during prostatectomy. We performed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) on 54 banked human cancerous and normal prostate tissue specimens to investigate the spatial distribution of a wide variety of small metabolites, carbohydrates, and lipids. In contrast to several previous studies, our method included Krebs cycle intermediates (m/z <200), which we found to be highly informative in distinguishing cancer from benign tissue. Malignant prostate cells showed marked metabolic derangements compared with their benign counterparts. Using the "Least absolute shrinkage and selection operator" (Lasso), we analyzed all metabolites from the DESI-MS data and identified parsimonious sets of metabolic profiles for distinguishing between cancer and normal tissue. In an independ...