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Identification of lipidomic profiles associated with drug-resistant prostate cancer cells

Ingram, Lishann M., Finnerty, Morgan C., Mansoura, Maryam, Chou, Chau-Wen, Cummings, Brian S.·2021·Lipids in Health and Disease
DOI 10.1186/s12944-021-01437-5PMC PMC7890620

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Abstract

Background: The association of circulating lipids with clinical outcomes of drug-resistant castration-resistant prostate cancer (DR-CRPC) is not fully understood. While it is known that increases in select lipids correlate to decreased survival, neither the mechanisms mediating these alterations nor the correlation of resistance to drug treatments is well characterized. Methods: This gap-in-knowledge was addressed using in vitro models of non-cancerous, hormone-sensitive, CRPC and drug-resistant cell lines combined with quantitative LC-ESI-Orbitrap-MS (LC-ESI-MS/MS) lipidomic analysis and subsequent analysis such as Metaboanalyst and Lipid Pathway Enrichment Analysis (LIPEA). Results: Several lipid regulatory pathways were identified that are associated with Docetaxel resistance in prostate cancer (PCa). These included those controlling glycerophospholipid metabolism, sphingolipid signaling and ferroptosis. In total, 7460 features were identified as being dysregulated between the cell lines studied, and 21 lipid species were significantly altered in drug-resistant cell lines as compared to nonresistant cell lines. Docetaxel resistance cells (PC3-Rx and DU145-DR) had higher levels of phosphatidylcholine (PC), oxidized lipid species, phosphatidylethanolamine (PE), and sphingomyelin (SM) as compared to parent control cells (PC-3 and DU-145). Alterations were also identified in the levels of phosphatidic acid (PA) and diacylglyceride (DAG), whose levels are regulated by Lipin (LPIN), a phosphatidic acid phosphatase that converts PA to DAG. Data derived from cBioPortal demonstrated a population of PCa patients expressing mutations aligning with amplification of LPIN1, LPIN2 and LPIN3 genes. Lipin amplification in these genes correlated to decreased survival in these patients. Lipin-1 mRNA expression also showed a similar trend in PCa patient data. Lipin-1, but not Lipin-2 or − 3, was detected in several prostate cancer cells, and was increased in 22RV1 and PC-3 cell lines. The increased expression of Lipin-1 in these cells correlated with the level of PA. Conclusion: These data identify lipids whose levels may correlate to Docetaxel sensitivity and progression of PCa. The data also suggest a correlation between the expression of Lipin-1 in cells and patients with regards to prostate cancer cell aggressiveness and patient survivability. Ultimately, these data may be useful for identifying markers of lethal and/or metastatic prostate cancer.

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Fig. 1 Heat map of differentially altered metabolites associated with non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines and media as determined by LC-ESI-MS/MS. Data were derived from a minimum of 3 extractions from 3 different passages per cell line
Fig. 1 Heat map of differentially altered metabolites associated with non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines and media as determined by LC-ESI-MS/MS. Data were derived from a minimum of 3 extractions from 3 different passages per cell lineCC BY · PubMed Central
Fig. 2 LIPEA pathway analysis. Identification of lipids pathways enriched in prostate cancer cells (LNCaP, 22RV1, PC-3, DU-145, PC3-Rx and DU145-DR) as compared to non-cancerous cells (PNT2 and RWPE1). Data were derived from a minimum of 3 extractions from 3 different passages, where increases in lipids were mapped to genes identified in the KEGG database
Fig. 2 LIPEA pathway analysis. Identification of lipids pathways enriched in prostate cancer cells (LNCaP, 22RV1, PC-3, DU-145, PC3-Rx and DU145-DR) as compared to non-cancerous cells (PNT2 and RWPE1). Data were derived from a minimum of 3 extractions from 3 different passages, where increases in lipids were mapped to genes identified in the KEGG databaseCC BY · PubMed Central
Fig. 3 Alterations of phospholipid and acyl glycerol classes were observed in non-cancerous prostate cells (blue), hormone-sensitive (dark red), castration-resistant (CR) (green), and Docetaxel resistant (DR) (red) prostate cancer cells. LC-ESI-MS/MS analysis of these classes in comparison to the Kennedy Pathway demonstrates alterations of the lipid abundance within progressing prostate cancer cells. The enzymes mediating the remodeling are shown in blue along the arrows. CHPT1 Cholinephosphotransferase 1, CEPT1 Choline/ethanolaminephosphotransferase 1, DAG diacylglycerol, DGAT diacylglycerol acyltransferase, PA phosphatidic acid, PAP phosphatidic acid phosphatase, PC phosphatidylcholine, PLD Phospholipase D, PE phosphatidylethanolamine, PEMT Phosphatidylethanolamine-N-methyltransferase, PG phosphatidylglycerol, PGP phosphatidylglycerol phosphatase, PS phosphatidylserine, PSS1/ PSS2 phosphatidylserine synthase, PISD Phosphatidylserine decarboxylases, TAG triacylglycerol
Fig. 3 Alterations of phospholipid and acyl glycerol classes were observed in non-cancerous prostate cells (blue), hormone-sensitive (dark red), castration-resistant (CR) (green), and Docetaxel resistant (DR) (red) prostate cancer cells. LC-ESI-MS/MS analysis of these classes in comparison to the Kennedy Pathway demonstrates alterations of the lipid abundance within progressing prostate cancer cells. The enzymes mediating the remodeling are shown in blue along the arrows. CHPT1 Cholinephosphotransferase 1, CEPT1 Choline/ethanolaminephosphotransferase 1, DAG diacylglycerol, DGAT diacylglycerol acyltransferase, PA phosphatidic acid, PAP phosphatidic acid phosphatase, PC phosphatidylcholine, PLD Phospholipase D, PE phosphatidylethanolamine, PEMT Phosphatidylethanolamine-N-methyltransferase, PG phosphatidylglycerol, PGP phosphatidylglycerol phosphatase, PS phosphatidylserine, PSS1/ PSS2 phosphatidylserine synthase, PISD Phosphatidylserine decarboxylases, TAG triacylglycerolCC BY · PubMed Central
Fig. 4 a Differential cloud plot demonstrating dysregulated features between hormone-sensitive, castration-resistant, Docetaxel resistant cells and non-cancerous cells (PNT2 and RWPE1) as determined by LC-ESI-MS/MS (P-value < 0.05 threshold, fold change > 1.5 threshold). b Differential expression of lipid features in non-cancerous prostate cells (b) as compared to hormone-sensitive (HS), castration-resistant (CR) and Docetaxel resistant (DR) prostate cancer cells. Only those features whose levels vary significantly (P < 0.05) are projected on the heat map. Each row represents a metabolite feature and each column represents a sample
Fig. 4 a Differential cloud plot demonstrating dysregulated features between hormone-sensitive, castration-resistant, Docetaxel resistant cells and non-cancerous cells (PNT2 and RWPE1) as determined by LC-ESI-MS/MS (P-value < 0.05 threshold, fold change > 1.5 threshold). b Differential expression of lipid features in non-cancerous prostate cells (b) as compared to hormone-sensitive (HS), castration-resistant (CR) and Docetaxel resistant (DR) prostate cancer cells. Only those features whose levels vary significantly (P < 0.05) are projected on the heat map. Each row represents a metabolite feature and each column represents a sampleCC BY · PubMed Central
Fig. 5 Comparison of phosphatidylcholine (PC) in non-cancerous (PNT2 and RWPE1) and hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MS. Normalized peak areas between all cells are shown for a phosphatidylcholine (PC), b 36:1 PC c 12:0–24:1 PC and d 14:0-22:2 PC
Fig. 5 Comparison of phosphatidylcholine (PC) in non-cancerous (PNT2 and RWPE1) and hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MS. Normalized peak areas between all cells are shown for a phosphatidylcholine (PC), b 36:1 PC c 12:0–24:1 PC and d 14:0-22:2 PCCC BY · PubMed Central
Fig. 6 Comparison of a 38:4 and b 18:0–22:6 PC levels in non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MS
Fig. 6 Comparison of a 38:4 and b 18:0–22:6 PC levels in non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MSCC BY · PubMed Central
Fig. 7 Comparison of a lysophosphocholine (LPC), b LPC and c 20:4 LPC levels in non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MS
Fig. 7 Comparison of a lysophosphocholine (LPC), b LPC and c 20:4 LPC levels in non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MSCC BY · PubMed Central
Fig. 8 Comparison of oxidized phosphatidylcholines (OxPC) levels in non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines and media. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MS. Normalized peak areas between all cells are shown for a oxidized phosphatidylcholine (OxPC), b oxidized lysophosphatidylcholine (OxLPC)
Fig. 8 Comparison of oxidized phosphatidylcholines (OxPC) levels in non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines and media. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MS. Normalized peak areas between all cells are shown for a oxidized phosphatidylcholine (OxPC), b oxidized lysophosphatidylcholine (OxLPC)CC BY · PubMed Central
Fig. 9 Comparison of phosphatidylethanolamine (PE) levels in non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines and media. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MS. Normalized peak areas between all cells are shown for phosphatidylethanolamine (PE), b OxPE
Fig. 9 Comparison of phosphatidylethanolamine (PE) levels in non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines and media. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MS. Normalized peak areas between all cells are shown for phosphatidylethanolamine (PE), b OxPECC BY · PubMed Central
Fig. 10 Comparison of sphingomyelin (SM) levels in non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines and media. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MS. Normalized peak areas between all cells are shown for a sphingomyelin (SM) and b 34:1 + H SM
Fig. 10 Comparison of sphingomyelin (SM) levels in non-cancerous (PNT2 and RWPE1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (PC-3 and DU-145) and Docetaxel resistant (PC3-Rx and DU145-DR) prostate cell lines and media. Data are indicative of 6 samples (6 distinct passages) per group and are expressed as mean ± the SEM (*q < 0.05 **q < 0.01*** q < 0.001). Each symbol represents an individual lipid feature as identified by MS/MS. Normalized peak areas between all cells are shown for a sphingomyelin (SM) and b 34:1 + H SMCC BY · PubMed Central
Fig. 11 a Alterations in lipin genes LPIN1, LPIN2 and LPIN3 within four types of prostate cancer from sixteen studies. Amplification is the major alteration in all types, excluding general prostate cancer. b Overall survivability in prostate cancer patients with (red) or without (blue) lipin gene alteration. There are 28 samples with alterations in LPIN1, 37 samples from 30 patients with alterations in LPIN2 and 27 samples out of 26 patients with LPIN3 alterations. c mRNA expression, accurate transcript quantification from RNA-Seq data (RSEM) as a function of putative copy-number alterations in mutation type. There are 10,712 samples from 32 studies on the horizontal axis, 6961 samples from 16 studies on the vertical axis and 6909 samples from 16 studies at the intersection of the two axes. The figures were generated from cBioPortal for Cancer Genomics. d Variation in expression levels of lipin genes were observed in patients with (red) and without (green) prostate cancer. The method for differential analysis was a one-way ANOVA, using disease state (Tumor or Normal) as variable for calculating differential expression. Pair-wise gene expression correlations, using the Pearson method, of the lipin genes were analyzed based on TCGA and GTEx databases. The non-log scale for calculation and use the log-scale axis for visualization. The figures were generated from GEPIA 2. PRAD prostate adenocarcinoma, T tumor, N normal, TPM transcripts per million
Fig. 11 a Alterations in lipin genes LPIN1, LPIN2 and LPIN3 within four types of prostate cancer from sixteen studies. Amplification is the major alteration in all types, excluding general prostate cancer. b Overall survivability in prostate cancer patients with (red) or without (blue) lipin gene alteration. There are 28 samples with alterations in LPIN1, 37 samples from 30 patients with alterations in LPIN2 and 27 samples out of 26 patients with LPIN3 alterations. c mRNA expression, accurate transcript quantification from RNA-Seq data (RSEM) as a function of putative copy-number alterations in mutation type. There are 10,712 samples from 32 studies on the horizontal axis, 6961 samples from 16 studies on the vertical axis and 6909 samples from 16 studies at the intersection of the two axes. The figures were generated from cBioPortal for Cancer Genomics. d Variation in expression levels of lipin genes were observed in patients with (red) and without (green) prostate cancer. The method for differential analysis was a one-way ANOVA, using disease state (Tumor or Normal) as variable for calculating differential expression. Pair-wise gene expression correlations, using the Pearson method, of the lipin genes were analyzed based on TCGA and GTEx databases. The non-log scale for calculation and use the log-scale axis for visualization. The figures were generated from GEPIA 2. PRAD prostate adenocarcinoma, T tumor, N normal, TPM transcripts per millionCC BY · PubMed Central
Fig. 12 a Western blot for Lipin-1 (~ 125 kDa) and GAPDH expression (~ 36 kDa) in non-cancerous prostate cells (PNT2 and RWPE-1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (CR) (PC-3 and DU-145), Docetaxel resistant (DR) (PC3-RX and DU145-DR) prostate cells and hepatocellular carcinoma (HepG2) cells (positive control). Immunoblots are representative of at least 3 (n = 3) separate experiments using 3 separate passages. b Densitometry of Lipin-1 protein normalized to GAPDH within non-cancerous prostate cells (blue), hormone-sensitive (orange), castration-resistant (CR) (green), Docetaxel resistant (DR) (red) prostate, and hepatocellular carcinoma (black) cell lines
Fig. 12 a Western blot for Lipin-1 (~ 125 kDa) and GAPDH expression (~ 36 kDa) in non-cancerous prostate cells (PNT2 and RWPE-1), hormone-sensitive (LNCaP and 22RV1), castration-resistant (CR) (PC-3 and DU-145), Docetaxel resistant (DR) (PC3-RX and DU145-DR) prostate cells and hepatocellular carcinoma (HepG2) cells (positive control). Immunoblots are representative of at least 3 (n = 3) separate experiments using 3 separate passages. b Densitometry of Lipin-1 protein normalized to GAPDH within non-cancerous prostate cells (blue), hormone-sensitive (orange), castration-resistant (CR) (green), Docetaxel resistant (DR) (red) prostate, and hepatocellular carcinoma (black) cell linesCC BY · PubMed Central

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Research works printed but not linked: 62

  1. Chandrasekar T, et al. Mechanisms of resistance in castration-resistant prostate cancer (CRPC). Transl Androl Urol. 2015;4(3):365–80.
  2. George DJ, Kantoff PW, Lin DW. New and emerging treatments for advanced prostate cancer. Clin Adv Hematol Oncol. 2011;9(6 Suppl 12):1–15.
  3. Hultsch S, et al. Association of tamoxifen resistance and lipid reprogramming in breast cancer. BMC Cancer. 2018;18(1):850. · DOI
  4. Cheng C, et al. Glucose-mediated N-glycosylation of SCAP is essential for SREBP-1 activation and tumor growth. Cancer Cell. 2015;28(5):569–81. · DOI
  5. Guo, D., et al., EGFR signaling through an Akt-SREBP-1–dependent, rapamycin-resistant pathway sensitizes glioblastomas to antilipogenic therapy. Sci. Signal., 2009. 2(101): p. ra82-ra82. · DOI · Paper page
  6. Acevedo, A., et al., LIPEA: Lipid Pathway Enrichment Analysis. bioRxiv, 2018: p. 274969. · DOI
  7. Twum-Ampofo J, et al. Metabolic targets for potential prostate cancer therapeutics. Curr Opin Oncol. 2016;28(3):241–7. · DOI
  8. Ngo DC, et al. Introduction to the molecular basis of cancer metabolism and the Warburg effect. Mol Biol Rep. 2015;42(4):819–23. · DOI
  9. Medes G, Thomas A, Weinhouse S. Metabolism of neoplastic tissue. IV. A study of lipid synthesis in neoplastic tissue slices in vitro. Cancer Res. 1953;13(1):27–9.
  10. Ookhtens M, et al. Liver and adipose tissue contributions to newly formed fatty acids in an ascites tumor. Am J Phys. 1984;247(1 Pt 2):R146–53.
  11. Beloribi-Djefaflia S, Vasseur S, Guillaumond F. Lipid metabolic reprogramming in cancer cells. Oncogenesis. 2016;5(1):e189. · DOI
  12. DeBerardinis RJ, et al. The biology of Cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 2008;7(1):11–20. · DOI · Paper page
  13. Bartlett GR. Phosphorus assay in column chromatography. J Biol Chem. 1959;234(3):466–8. · DOI
  14. Maes E, et al. Determination of variation parameters as a crucial step in designing TMT-based clinical proteomics experiments. PLoS One. 2015;10(3):e0120115. · DOI
  15. Kinsey GR, et al. Decreased iPLA2γ expression induces lipid peroxidation and cell death and sensitizes cells to oxidant-induced apoptosis. J Lipid Res. 2008;49(7):1477–87. · DOI
  16. Zhang L, Peterson BL, Cummings BS. The effect of inhibition of Ca2+−independent phospholipase A2 on chemotherapeutic-induced death and phospholipid profiles in renal cells. Biochem Pharmacol. 2005;70(11):1697–706. · DOI
  17. Peterson B, et al. Alterations in phospholipid and fatty acid lipid profiles in primary neocortical cells during oxidant-induced cell injury. Chem Biol Interact. 2008;174(3):163–76. · DOI
  18. Kalli A, et al. Evaluation and optimization of mass spectrometric settings during data-dependent acquisition mode: focus on LTQ-Orbitrap mass analyzers. J Proteome Res. 2013;12(7):3071–86. · DOI
  19. Pluskal T, et al. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 2010;11:395. · DOI
  20. Tautenhahn R, et al. XCMS online: a web-based platform to process untargeted metabolomic data. Anal Chem. 2012;84(11):5035–9. · DOI
  21. Koelmel JP, et al. LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data. BMC Bioinformatics. 2017;18(1):331. · DOI
  22. Koelmel JP, et al. Expanding Lipidome coverage using LC-MS/MS data-dependent acquisition with automated exclusion list generation. J Am Soc Mass Spectrom. 2017;28(5):908–17. · DOI · Paper page
  23. Chong J, et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 2018;46(W1):W486–w494. · DOI
  24. Kinsey GR, et al. Decreased iPLA2gamma expression induces lipid peroxidation and cell death and sensitizes cells to oxidant-induced apoptosis. J Lipid Res. 2008;49(7):1477–87. · DOI
  25. Cerami E, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4. · DOI · Paper page
  26. Gao J, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1. · DOI · Paper page
  27. Tang Z, et al. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556–60. · DOI
  28. Trygg J, Wold S. Orthogonal projections to latent structures (O-PLS). J Chemom. 2002;16(3):119–28. · DOI
  29. Trygg J, Holmes E, Lundstedt T. Chemometrics in metabonomics. J Proteome Res. 2007;6(2):469–79. · DOI
  30. Richman EL, et al. Choline intake and risk of lethal prostate cancer: incidence and survival. Am J Clin Nutr. 2012;96(4):855–63. · DOI · Paper page
  31. Fernandis AZ, Wenk MR. Membrane lipids as signaling molecules. Curr Opin Lipidol. 2007;18(2):121–8. · DOI
  32. Brzozowski JS, et al. Lipidomic profiling of extracellular vesicles derived from prostate and prostate cancer cell lines. Lipids Health Dis. 2018;17(1):211. · DOI
  33. Ponnusamy S, et al. Sphingolipids and cancer: ceramide and sphingosine-1-phosphate in the regulation of cell death and drug resistance. Future Oncol. 2010;6(10):1603–24. · DOI
  34. van Meer G, Voelker DR, Feigenson GW. Membrane lipids: where they are and how they behave. Nat Rev Mol Cell Biol. 2008;9(2):112–24. · DOI · Paper page
  35. Dixon SJ, et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell. 2012;149(5):1060–72. · DOI · Paper page
  36. Latunde-Dada GO. Ferroptosis: role of lipid peroxidation, iron and ferritinophagy. Biochim Biophys Acta Gen Subj. 2017;1861(8):1893–900. · DOI · Paper page
  37. Agmon E, et al. Modeling the effects of lipid peroxidation during ferroptosis on membrane properties. Sci Rep. 2018;8(1):5155. · DOI
  38. Agmon E, Stockwell BR. Lipid homeostasis and regulated cell death. Curr Opin Chem Biol. 2017;39:83–9. · DOI
  39. Stockwell BR, et al. Ferroptosis: a regulated cell death Nexus linking metabolism, redox biology, and disease. Cell. 2017;171(2):273–85. · DOI · Paper page
  40. Lin HM, et al. A distinct plasma lipid signature associated with poor prognosis in castration-resistant prostate cancer. Int J Cancer. 2017;141(10):2112–20. · DOI
  41. Sorvina A, et al. Lipid profiles of prostate cancer cells. Oncotarget. 2018;9(85):35541–52. · DOI · Paper page
  42. Cheng JC, et al. Radiation-induced acid ceramidase confers prostate cancer resistance and tumor relapse. J Clin Invest. 2013;123(10):4344–58. · DOI
  43. Ogretmen B, Hannun YA. Biologically active sphingolipids in cancer pathogenesis and treatment. Nat Rev Cancer. 2004;4(8):604–16. · DOI
  44. Merchant TE, et al. Phospholipid profiles of human colon cancer using 31P magnetic resonance spectroscopy. Int J Color Dis. 1991;6(2):121–6. · DOI
  45. Perrotti F, et al. Advances in Lipidomics for Cancer biomarkers discovery. Int J Mol Sci. 2016;17(12):1992. · DOI
  46. Perry DK, Hannun YA. The role of ceramide in cell signaling. Biochim Biophys Acta. 1998;1436(1–2):233–43. · DOI
  47. Taha TA, Mullen TD, Obeid LM. A house divided: ceramide, sphingosine, and sphingosine-1-phosphate in programmed cell death. Biochim Biophys Acta. 2006;1758(12):2027–36. · DOI
  48. Wang X-Z, et al. Aberrant sphingolipid signaling is involved in the resistance of prostate Cancer cell lines to chemotherapy. Cancer Res. 1999;59(22):5842–8.
  49. Schneider G, et al. Bioactive lipids, LPC and LPA, are novel prometastatic factors and their tissue levels increase in response to radio/chemotherapy. Molecular cancer research : MCR. 2014;12(11):1560–73. · DOI
  50. Okita M, et al. Elevated levels and altered fatty acid composition of plasma lysophosphatidylcholine (lysoPC) in ovarian cancer patients. Int J Cancer. 1997;71(1):31–4. · DOI
  51. Zhou X, et al. Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics. PLoS One. 2012;7(11):e48889. · DOI
  52. Phuyal S, et al. The ether lipid precursor hexadecylglycerol stimulates the release and changes the composition of exosomes derived from PC-3 cells. J Biol Chem. 2015;290(7):4225–37. · DOI
  53. Lisec J, et al. Cancer cell lipid class homeostasis is altered under nutrient-deprivation but stable under hypoxia. BMC Cancer. 2019;19(1):501. · DOI
  54. Burch TC, et al. Comparative Metabolomic and Lipidomic analysis of phenotype stratified prostate cells. PLoS One. 2015;10(8):e0134206. · DOI
  55. Jung JH, et al. Phospholipids of tumor extracellular vesicles stratify gefitinib-resistant nonsmall cell lung cancer cells from gefitinib-sensitive cells. Proteomics. 2015;15(4):824–35. · DOI
  56. Bevers EM, Comfurius P, Zwaal RF. Regulatory mechanisms in maintenance and modulation of transmembrane lipid asymmetry: pathophysiological implications. Lupus. 1996;5(5):480–7. · DOI
  57. Escribá PV. Membrane-lipid therapy: a new approach in molecular medicine. Trends Mol Med. 2006;12(1):34–43. · DOI
  58. Escriba PV, et al. Membranes: a meeting point for lipids, proteins and therapies. J Cell Mol Med. 2008;12(3):829–75. · DOI · Paper page
  59. Zhang TY, et al. Management of castrate resistant prostate cancer-recent advances and optimal sequence of treatments. Curr Urol Rep. 2013;14(3):174–83. · DOI
  60. Pascual F, Carman GM. Phosphatidate phosphatase, a key regulator of lipid homeostasis. Biochim Biophys Acta. 2013;1831(3):514–22. · DOI
  61. Liu Y. Fatty acid oxidation is a dominant bioenergetic pathway in prostate cancer. Prostate Cancer Prostatic Dis. 2006;9(3):230–4. · DOI
  62. Brohée L, et al. Lipin-1 regulates cancer cell phenotype and is a potential target to potentiate rapamycin treatment. Oncotarget. 2015;6(13):11264–80. · DOI

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Metadata

Authors
Ingram, L. M., Finnerty, M. C., Mansoura, M., Chou, C.-W., & Cummings, B. S.
ORCID
Cummings, Brian S.
Venue
Lipids in Health and Disease
Publisher
Springer Science and Business Media LLC
Published
2021-02-17
Volume
20
Issue
1
ISSN
1476-511X
DOI
10.1186/s12944-021-01437-5
PMC
PMC7890620
Cited by
42
References
3
Language
en
Licence
Funders
  • National Institutes of Health (S10RR028859)
  • U.S. Department of Defense (PC150431 GRANT11996600)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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