Dataset Information

Identification of Plasma Glycosphingolipids as Potential Biomarkers for Prostate Cancer (PCa) Status.

ABSTRACT: 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 trihexosylceramide and tetrahexosylceramide were most closely associated with high-aggressive PCa. The Cancer Genome Atlas was queried to determine gene alterations within glycosphingolipid metabolism that are associated with PCa and other cancers. Genes that encode enzymes associated with the metabolism of glycosphingolipids were altered in 12% of PCa and >30% of lung, uterine, and ovarian cancers. These data suggest that the identified plasma (glyco)sphingolipids should be further validated for their association with aggressive PCa, suggesting that specific sphingolipids may be included in a diagnostic signature for PCa.

SUBMITTER: Snider AJ

PROVIDER: S-EPMC7600119 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Identification of Plasma Glycosphingolipids as Potential Biomarkers for Prostate Cancer (PCa) Status.

Snider Ashley J AJ Seeds Michael C MC Johnstone Laurel L Snider Justin M JM Hallmark Brian B Dutta Rahul R Moraga Franco Cristina C Parks John S JS Bensen Jeannette T JT Broeckling Corey D CD Mohler James L JL Smith Gary J GJ Fontham Elizabeth T H ETH Lin Hui-Kuan HK Bresette William W Sergeant Susan S Chilton Floyd H FH

Biomolecules 20200930 10

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) o ...[more]

PMID: 33007922

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Project description:BackgroundLipids 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 findingsUsing 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/significanceUsing 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.

Project description:BackgroundScreening and diagnosis of prostate cancer (PCa) is hampered by an inability to predict who has the potential to develop fatal disease and who has indolent cancer. Studies have identified multiple genetic risk loci for PCa incidence, but it is unknown whether they could be used as biomarkers for PCa-specific mortality (PCSM).ObjectiveTo examine the association of 47 established PCa risk single-nucleotide polymorphisms (SNPs) with PCSM.Design, setting, and participantsWe included 10 487 men who had PCa and 11 024 controls, with a median follow-up of 8.3 yr, during which 1053 PCa deaths occurred.Outcome measurements and statistical analysisThe main outcome was PCSM. The risk allele was defined as the allele associated with an increased risk for PCa in the literature. We used Cox proportional hazards regression to calculate the hazard ratios of each SNP with time to progression to PCSM after diagnosis. We also used logistic regression to calculate odds ratios for each risk SNP, comparing fatal PCa cases to controls.Results and limitationsAmong the cases, we found that 8 of the 47 SNPs were significantly associated (p<0.05) with time to PCSM. The risk allele of rs11672691 (intergenic) was associated with an increased risk for PCSM, while 7 SNPs had risk alleles inversely associated (rs13385191 [C2orf43], rs17021918 [PDLIM5], rs10486567 [JAZF1], rs6465657 [LMTK2], rs7127900 (intergenic), rs2735839 [KLK3], rs10993994 [MSMB], rs13385191 [C2orf43]). In the case-control analysis, 22 SNPs were associated (p<0.05) with the risk of fatal PCa, but most did not differentiate between fatal and nonfatal PCa. Rs11672691 and rs10993994 were associated with both fatal and nonfatal PCa, while rs6465657, rs7127900, rs2735839, and rs13385191 were associated with nonfatal PCa only.ConclusionsEight established risk loci were associated with progression to PCSM after diagnosis. Twenty-two SNPs were associated with fatal PCa incidence, but most did not differentiate between fatal and nonfatal PCa. The relatively small magnitudes of the associations do not translate well into risk prediction, but these findings merit further follow-up, because they may yield important clues about the complex biology of fatal PCa.Patient summaryIn this report, we assessed whether established PCa risk variants could predict PCSM. We found eight risk variants associated with PCSM: One predicted an increased risk of PCSM, while seven were associated with decreased risk. Larger studies that focus on fatal PCa are needed to identify more markers that could aid prediction.

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Dataset Information

Identification of Plasma Glycosphingolipids as Potential Biomarkers for Prostate Cancer (PCa) Status.

Publications

Identification of Plasma Glycosphingolipids as Potential Biomarkers for Prostate Cancer (PCa) Status.

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