Unraveling the glycosphingolipid metabolism by leveraging transcriptome-weighted network analysis on neuroblastic tumors.
GD2
Ganglioneuroblastoma
Ganglioneuroma
Ganglioside
Glycosphingolipids
Metabolic graph
Neuroblastoma
Reaction activity score
Journal
Cancer & metabolism
ISSN: 2049-3002
Titre abrégé: Cancer Metab
Pays: England
ID NLM: 101607582
Informations de publication
Date de publication:
24 Oct 2024
24 Oct 2024
Historique:
received:
15
01
2024
accepted:
11
10
2024
medline:
25
10
2024
pubmed:
25
10
2024
entrez:
25
10
2024
Statut:
epublish
Résumé
Glycosphingolipids (GSLs) are membrane lipids composed of a ceramide backbone linked to a glycan moiety. Ganglioside biosynthesis is a part of the GSL metabolism, which involves sequential reactions catalyzed by specific enzymes that in part have a poor substrate specificity. GSLs are deregulated in cancer, thus playing a role as potential biomarkers for personalized therapy or subtype classification. However, the analysis of GSL profiles is complex and requires dedicated technologies, that are currently not included in the commonly utilized high-throughput assays adopted in contexts such as molecular tumor boards. In this study, we developed a method to discriminate the enzyme activity among the four series of the ganglioside metabolism pathway by incorporating transcriptome data and topological information of the metabolic network. We introduced three adjustment options for reaction activity scores (RAS) and demonstrated their application in both exploratory and comparative analyses by applying the method on neuroblastic tumors (NTs), encompassing neuroblastoma (NB), ganglioneuroblastoma (GNB), and ganglioneuroma (GN). Furthermore, we interpreted the results in the context of earlier published GSL measurements in the same tumors. By adjusting RAS values using a weighting scheme based on network topology and transition probabilities (TPs), the individual series of ganglioside metabolism can be differentiated, enabling a refined analysis of the GSL profile in NT entities. Notably, the adjustment method we propose reveals the differential engagement of the ganglioside series between NB and GNB. Moreover, MYCN gene expression, a well-known prognostic marker in NTs, appears to correlate with the expression of therapeutically relevant gangliosides, such as GD2. Using unsupervised learning, we identified subclusters within NB based on altered GSL metabolism. Our study demonstrates the utility of adjusting RAS values in discriminating ganglioside metabolism subtypes, highlighting the potential for identifying novel cancer subgroups based on sphingolipid profiles. These findings contribute to a better understanding of ganglioside dysregulation in NT and may have implications for stratification and targeted therapeutic strategies in these tumors and other tumor entities with a deregulated GSL metabolism.
Sections du résumé
BACKGROUND
BACKGROUND
Glycosphingolipids (GSLs) are membrane lipids composed of a ceramide backbone linked to a glycan moiety. Ganglioside biosynthesis is a part of the GSL metabolism, which involves sequential reactions catalyzed by specific enzymes that in part have a poor substrate specificity. GSLs are deregulated in cancer, thus playing a role as potential biomarkers for personalized therapy or subtype classification. However, the analysis of GSL profiles is complex and requires dedicated technologies, that are currently not included in the commonly utilized high-throughput assays adopted in contexts such as molecular tumor boards.
METHODS
METHODS
In this study, we developed a method to discriminate the enzyme activity among the four series of the ganglioside metabolism pathway by incorporating transcriptome data and topological information of the metabolic network. We introduced three adjustment options for reaction activity scores (RAS) and demonstrated their application in both exploratory and comparative analyses by applying the method on neuroblastic tumors (NTs), encompassing neuroblastoma (NB), ganglioneuroblastoma (GNB), and ganglioneuroma (GN). Furthermore, we interpreted the results in the context of earlier published GSL measurements in the same tumors.
RESULTS
RESULTS
By adjusting RAS values using a weighting scheme based on network topology and transition probabilities (TPs), the individual series of ganglioside metabolism can be differentiated, enabling a refined analysis of the GSL profile in NT entities. Notably, the adjustment method we propose reveals the differential engagement of the ganglioside series between NB and GNB. Moreover, MYCN gene expression, a well-known prognostic marker in NTs, appears to correlate with the expression of therapeutically relevant gangliosides, such as GD2. Using unsupervised learning, we identified subclusters within NB based on altered GSL metabolism.
CONCLUSION
CONCLUSIONS
Our study demonstrates the utility of adjusting RAS values in discriminating ganglioside metabolism subtypes, highlighting the potential for identifying novel cancer subgroups based on sphingolipid profiles. These findings contribute to a better understanding of ganglioside dysregulation in NT and may have implications for stratification and targeted therapeutic strategies in these tumors and other tumor entities with a deregulated GSL metabolism.
Identifiants
pubmed: 39449099
doi: 10.1186/s40170-024-00358-y
pii: 10.1186/s40170-024-00358-y
doi:
Types de publication
Journal Article
Langues
eng
Pagination
29Subventions
Organisme : Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
ID : Projektnummer 318346496 - SFB1292/2 TP19N
Informations de copyright
© 2024. The Author(s).
Références
Schnaar RL. The biology of gangliosides. Adv Carbohydr Chem Biochem. 2019;76:113–48. https://doi.org/10.1016/bs.accb.2018.09.002 .
doi: 10.1016/bs.accb.2018.09.002
pubmed: 30851743
Sipione S, Monyror J, Galleguillos D, Steinberg N, Kadam V. Gangliosides in the Brain: Physiology. Pathophysiology and Therapeutic Applications Front Neurosci. 2020;14:572965. https://doi.org/10.3389/fnins.2020.572965 .
doi: 10.3389/fnins.2020.572965
pubmed: 33117120
Gault CR, Obeid LM, Hannun YA. An overview of sphingolipid metabolism: from synthesis to breakdown. Adv Exp Med Biol. 2010;688:1–23. https://doi.org/10.1007/978-1-4419-6741-1_1 .
doi: 10.1007/978-1-4419-6741-1_1
pubmed: 20919643
pmcid: 3069696
Schnaar RL, Sandhoff R, Tiemeyer M, Kinoshita T. In: Varki A, Cummings RD, Esko JD, Stanley P, Hart GW, Aebi M, et al., editors. Glycosphingolipids. 4th ed. Cold Spring Harbor (NY); 2022. pp. 129–40. https://doi.org/10.1101/glycobiology.4e.11 .
Sandhoff R, Sandhoff K. Emerging concepts of ganglioside metabolism. FEBS Lett. 2018;592(23):3835–64. https://doi.org/10.1002/1873-3468.13114 .
doi: 10.1002/1873-3468.13114
pubmed: 29802621
Vasques JF, de Jesus Goncalves RG, da Silva-Junior AJ, Martins RS, Gubert F, Mendez-Otero R. Gangliosides in nervous system development, regeneration, and pathologies. Neural Regen Res. 2023;18(1):81–6. https://doi.org/10.4103/1673-5374.343890 .
doi: 10.4103/1673-5374.343890
pubmed: 35799513
Yu RK, Nakatani Y, Yanagisawa M. The role of glycosphingolipid metabolism in the developing brain. J Lipid Res. 2009;50 Suppl(Suppl):S440–5. https://doi.org/10.1194/jlr.R800028-JLR200 .
Svennerholm L, Bostrom K, Fredman P, Mansson JE, Rosengren B, Rynmark BM. Human brain gangliosides: developmental changes from early fetal stage to advanced age. Biochim Biophys Acta. 1989;1005(2):109–17. https://doi.org/10.1016/0005-2760(89)90175-6 .
doi: 10.1016/0005-2760(89)90175-6
pubmed: 2775765
Vajn K, Viljetic B, Degmecic IV, Schnaar RL, Heffer M. Differential distribution of major brain gangliosides in the adult mouse central nervous system. PLoS ONE. 2013;8(9):e75720. https://doi.org/10.1371/journal.pone.0075720 .
doi: 10.1371/journal.pone.0075720
pubmed: 24098718
pmcid: 3787110
Jin X, Yang GY. Pathophysiological roles and applications of glycosphingolipids in the diagnosis and treatment of cancer diseases. Prog Lipid Res. 2023;91:101241. https://doi.org/10.1016/j.plipres.2023.101241 .
doi: 10.1016/j.plipres.2023.101241
pubmed: 37524133
Cao S, Hu X, Ren S, Wang Y, Shao Y, Wu K, et al. The biological role and immunotherapy of gangliosides and GD3 synthase in cancers. Front Cell Dev Biol. 2023;11:1076862. https://doi.org/10.3389/fcell.2023.1076862 .
doi: 10.3389/fcell.2023.1076862
pubmed: 36824365
pmcid: 9941352
Balis FM, Busch CM, Desai AV, Hibbitts E, Naranjo A, Bagatell R, et al. The ganglioside G(D2) as a circulating tumor biomarker for neuroblastoma. Pediatr Blood Cancer. 2020;67(1):e28031. https://doi.org/10.1002/pbc.28031 .
doi: 10.1002/pbc.28031
pubmed: 31612589
Terzic T, Cordeau M, Herblot S, Teira P, Cournoyer S, Beaunoyer M, et al. Expression of Disialoganglioside (GD2) in Neuroblastic Tumors: A Prognostic Value for Patients Treated With Anti-GD2 Immunotherapy. Pediatr Dev Pathol. 2018;21(4):355–62. https://doi.org/10.1177/1093526617723972 .
doi: 10.1177/1093526617723972
pubmed: 29067879
Lee MC, Kim BW, Kim JS, Lee JS, Kim KS, Lee JH, et al. Neuronal differentiation of human neuroblastoma SH-SY5Y cells by gangliosides. Brain Tumor Pathol. 1997;14(1):5–11. https://doi.org/10.1007/BF02478862 .
doi: 10.1007/BF02478862
pubmed: 9384796
Paret C, Ustjanzew A, Ersali S, Seidmann L, Jennemann R, Ziegler N, et al. GD2 Expression in Medulloblastoma and Neuroblastoma for Personalized Immunotherapy: A Matter of Subtype. Cancers (Basel). 2022;14(24). https://doi.org/10.3390/cancers14246051 .
Shawraba F, Hammoud H, Mrad Y, Saker Z, Fares Y, Harati H, et al. Biomarkers in Neuroblastoma: An Insight into Their Potential Diagnostic and Prognostic Utilities. Curr Treat Options Oncol. 2021;22(11):102. https://doi.org/10.1007/s11864-021-00898-1 .
doi: 10.1007/s11864-021-00898-1
pubmed: 34580780
Rodriguez EF, Jones R, Miller D, Rodriguez FJ. Neurogenic Tumors of the Mediastinum. Semin Diagn Pathol. 2020;37(4):179–86. https://doi.org/10.1053/j.semdp.2020.04.004 .
doi: 10.1053/j.semdp.2020.04.004
pubmed: 32448592
Shimada H, Ikegaki N. Genetic and Histopathological Heterogeneity of Neuroblastoma and Precision Therapeutic Approaches for Extremely Unfavorable Histology Subgroups. Biomolecules. 2022;12(1). https://doi.org/10.3390/biom12010079 .
Shimada H, Ambros IM, Dehner LP, Hata J, Joshi VV, Roald B. Terminology and morphologic criteria of neuroblastic tumors: recommendations by the International Neuroblastoma Pathology Committee. Cancer. 1999;86(2):349–63.
doi: 10.1002/(SICI)1097-0142(19990715)86:2<349::AID-CNCR20>3.0.CO;2-Y
pubmed: 10421272
Wu ZL, Schwartz E, Seeger R, Ladisch S. Expression of GD2 ganglioside by untreated primary human neuroblastomas. Cancer Res. 1986;46(1):440–3.
pubmed: 3940209
Schengrund CL. Gangliosides and Neuroblastomas. Int J Mol Sci. 2020;21(15). https://doi.org/10.3390/ijms21155313 .
Mastrangelo S, Rivetti S, Triarico S, Romano A, Attina G, Maurizi P, et al. Mechanisms, Characteristics, and Treatment of Neuropathic Pain and Peripheral Neuropathy Associated with Dinutuximab in Neuroblastoma Patients. Int J Mol Sci. 2021;22(23). https://doi.org/10.3390/ijms222312648 .
Slatnick LR, Jimeno A, Gore L, Macy ME. Naxitamab: a humanized anti-glycolipid disialoganglioside (anti-GD2) monoclonal antibody for treatment of neuroblastoma. Drugs Today (Barc). 2021;57(11):677–88. https://doi.org/10.1358/dot.2021.57.11.3343691 .
doi: 10.1358/dot.2021.57.11.3343691
pubmed: 34821881
Mount CW, Majzner RG, Sundaresh S, Arnold EP, Kadapakkam M, Haile S, et al. Potent antitumor efficacy of anti-GD2 CAR T cells in H3–K27M(+) diffuse midline gliomas. Nat Med. 2018;24(5):572–9. https://doi.org/10.1038/s41591-018-0006-x .
doi: 10.1038/s41591-018-0006-x
pubmed: 29662203
pmcid: 6214371
Yanagisawa M, Yoshimura S, Yu RK. Expression of GD2 and GD3 gangliosides in human embryonic neural stem cells. ASN Neuro. 2011;3(2). https://doi.org/10.1042/AN20110006 .
Mabe NW, Huang M, Dalton GN, Alexe G, Schaefer DA, Geraghty AC, et al. Transition to a mesenchymal state in neuroblastoma confers resistance to anti-GD2 antibody via reduced expression of ST8SIA1. Nat Cancer. 2022;3(8):976–93. https://doi.org/10.1038/s43018-022-00405-x .
doi: 10.1038/s43018-022-00405-x
pubmed: 35817829
pmcid: 10071839
Ruan S, Raj BK, Lloyd KO. Relationship of glycosyltransferases and mRNA levels to ganglioside expression in neuroblastoma and melanoma cells. J Neurochem. 1999;72(2):514–21. https://doi.org/10.1046/j.1471-4159.1999.0720514.x .
doi: 10.1046/j.1471-4159.1999.0720514.x
pubmed: 9930722
Rieke DT, de Bortoli T, Horak P, Lamping M, Benary M, Jelas I, et al. Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards. BMC Med. 2022;20(1):367. https://doi.org/10.1186/s12916-022-02560-5 .
doi: 10.1186/s12916-022-02560-5
pubmed: 36274133
pmcid: 9590222
Sha Y, Han L, Sun B, Zhao Q. Identification of a Glycosyltransferase Signature for Predicting Prognosis and Immune Microenvironment in Neuroblastoma. Front Cell Dev Biol. 2021;9:769580. https://doi.org/10.3389/fcell.2021.769580 .
doi: 10.3389/fcell.2021.769580
pubmed: 35071226
Sorokin M, Kholodenko I, Kalinovsky D, Shamanskaya T, Doronin I, Konovalov D, et al. RNA Sequencing-Based Identification of Ganglioside GD2-Positive Cancer Phenotype. Biomedicines. 2020;8(6). https://doi.org/10.3390/biomedicines8060142 .
Yang J, Han L, Sha Y, Jin Y, Li Z, Gong B, et al. A novel ganglioside-related risk signature can reveal the distinct immune landscape of neuroblastoma and predict the immunotherapeutic response. Front Immunol. 2022;13:1061814. https://doi.org/10.3389/fimmu.2022.1061814 .
doi: 10.3389/fimmu.2022.1061814
pubmed: 36605200
pmcid: 9807785
Graudenzi A, Maspero D, Di Filippo M, Gnugnoli M, Isella C, Mauri G, et al. Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power. J Biomed Inform. 2018;87:37–49. https://doi.org/10.1016/j.jbi.2018.09.010 .
doi: 10.1016/j.jbi.2018.09.010
pubmed: 30244122
Galuzzi BG, Vanoni M, Damiani C. Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells. BMC Bioinformatics. 2022;23(Suppl 6):445. https://doi.org/10.1186/s12859-022-04967-6 .
doi: 10.1186/s12859-022-04967-6
pubmed: 36284276
pmcid: 9597960
Weglarz-Tomczak E, Rijlaarsdam DJ, Tomczak JM, Brul S. GEM-Based Metabolic Profiling for Human Bone Osteosarcoma under Different Glucose and Glutamine Availability. Int J Mol Sci. 2021;22(3). https://doi.org/10.3390/ijms22031470 .
Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York; 2016. https://ggplot2.tidyverse.org .
Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal. 2006;Complex Systems:1695. https://igraph.org .
Weiss T, Taschner-Mandl S, Janker L, Bileck A, Rifatbegovic F, Kromp F, et al. Schwann cell plasticity regulates neuroblastic tumor cell differentiation via epidermal growth factor-like protein 8. Nat Commun. 2021;12(1):1624. https://doi.org/10.1038/s41467-021-21859-0 .
doi: 10.1038/s41467-021-21859-0
pubmed: 33712610
pmcid: 7954855
Zhang X, Jonassen I. RASflow: an RNA-Seq analysis workflow with Snakemake. BMC Bioinformatics. 2020;21(1):110. https://doi.org/10.1186/s12859-020-3433-x .
doi: 10.1186/s12859-020-3433-x
pubmed: 32183729
pmcid: 7079470
Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37(8):907–15. https://doi.org/10.1038/s41587-019-0201-4 .
doi: 10.1038/s41587-019-0201-4
pubmed: 31375807
pmcid: 7605509
Anders S, Pyl PT, Huber W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–9. https://doi.org/10.1093/bioinformatics/btu638 .
doi: 10.1093/bioinformatics/btu638
pubmed: 25260700
Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, et al. Gencode 2021. Nucleic Acids Res. 2021;49(D1):D916–23. https://doi.org/10.1093/nar/gkaa1087 .
doi: 10.1093/nar/gkaa1087
pubmed: 33270111
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. https://doi.org/10.1186/s13059-014-0550-8 .
doi: 10.1186/s13059-014-0550-8
pubmed: 25516281
pmcid: 4302049
Mohamed A, Hancock T, Nguyen CH, Mamitsuka H. NetPathMiner: R/Bioconductor package for network path mining through gene expression. Bioinformatics. 2014;30(21):3139–41. https://doi.org/10.1093/bioinformatics/btu501 .
doi: 10.1093/bioinformatics/btu501
pubmed: 25075120
pmcid: 4609018
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. https://doi.org/10.1093/nar/28.1.27 .
doi: 10.1093/nar/28.1.27
pubmed: 10592173
pmcid: 102409
Furukawa K, Tokuda N, Okuda T, Tajima O, Furukawa K. Glycosphingolipids in engineered mice: insights into function. Semin Cell Dev Biol. 2004;15(4):389–96. https://doi.org/10.1016/j.semcdb.2004.03.006 .
doi: 10.1016/j.semcdb.2004.03.006
pubmed: 15207829
Robinson JL, Kocabas P, Wang H, Cholley PE, Cook D, Nilsson A, et al. An atlas of human metabolism. Sci Signal. 2020;13(624). https://doi.org/10.1126/scisignal.aaz1482 .
Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2018. https://www.ncbi.nlm.nih.gov/pubmed/30531897 . https://doi.org/10.1038/nbt.4314 .
Hahsler M, Piekenbrock M, Doran D. dbscan: Fast Density-Based Clustering with R. J Stat Softw. 2019;91:1–30. https://doi.org/10.18637/jss.v091.i01 .
Lun AT, McCarthy DJ, Marioni JC. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 2016;5:2122. https://doi.org/10.12688/f1000research.9501.2 .
Zhu A, Ibrahim JG, Love MI. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. 2019;35(12):2084–92. https://doi.org/10.1093/bioinformatics/bty895 .
doi: 10.1093/bioinformatics/bty895
pubmed: 30395178
Alexa A, Rahnenfuhrer J. topGO: Enrichment analysis for Gene Ontology. R package version 2.28. 0. Cranio. 2016.
Marini F, Binder H. pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components. BMC Bioinformatics. 2019;20(1):331. https://doi.org/10.1186/s12859-019-2879-1 .
doi: 10.1186/s12859-019-2879-1
pubmed: 31195976
pmcid: 6567655
Alexa A, Rahnenfuhrer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006;22(13):1600–7. https://doi.org/10.1093/bioinformatics/btl140 .
doi: 10.1093/bioinformatics/btl140
pubmed: 16606683
Marini F, Ludt A, Linke J, Strauch K. GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data. BMC Bioinformatics. 2021;22(1):610. https://doi.org/10.1186/s12859-021-04461-5 .
doi: 10.1186/s12859-021-04461-5
pubmed: 34949163
pmcid: 8697502
Kang JH, Rychahou PG, Ishola TA, Qiao J, Evers BM, Chung DH. MYCN silencing induces differentiation and apoptosis in human neuroblastoma cells. Biochem Biophys Res Commun. 2006;351(1):192–7. https://doi.org/10.1016/j.bbrc.2006.10.020 .
doi: 10.1016/j.bbrc.2006.10.020
pubmed: 17055458
pmcid: 2708968
Ng RH, Lee JW, Baloni P, Diener C, Heath JR, Su Y. Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer. Front Oncol. 2022;12:914594. https://doi.org/10.3389/fonc.2022.914594 .
doi: 10.3389/fonc.2022.914594
pubmed: 35875150
pmcid: 9303011
Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol. 2012;10(4):291–305. https://doi.org/10.1038/nrmicro2737 .
doi: 10.1038/nrmicro2737
pubmed: 22367118
pmcid: 3536058
Manipur I, Granata I, Maddalena L, Guarracino MR. Clustering analysis of tumor metabolic networks. BMC Bioinformatics. 2020;21(Suppl 10):349. https://doi.org/10.1186/s12859-020-03564-9 .
doi: 10.1186/s12859-020-03564-9
pubmed: 32838750
pmcid: 7446216
Ma HW, Zeng AP. The connectivity structure, giant strong component and centrality of metabolic networks. Bioinformatics. 2003;19(11):1423–30. https://doi.org/10.1093/bioinformatics/btg177 .
doi: 10.1093/bioinformatics/btg177
pubmed: 12874056
Holme P, Huss M, Jeong H. Subnetwork hierarchies of biochemical pathways. Bioinformatics. 2003;19(4):532–8. https://doi.org/10.1093/bioinformatics/btg033 .
doi: 10.1093/bioinformatics/btg033
pubmed: 12611809
Dusad V, Thiel D, Barahona M, Keun HC, Oyarzun DA. Opportunities at the Interface of Network Science and Metabolic Modeling. Front Bioeng Biotechnol. 2020;8:591049. https://doi.org/10.3389/fbioe.2020.591049 .
doi: 10.3389/fbioe.2020.591049
pubmed: 33569373
Feist AM, Palsson BO. The biomass objective function. Curr Opin Microbiol. 2010;13(3):344–9. https://doi.org/10.1016/j.mib.2010.03.003 .
doi: 10.1016/j.mib.2010.03.003
pubmed: 20430689
pmcid: 2912156
Zhang Y, Boley D. Nonlinear multi-objective flux balance analysis of the Warburg Effect. J Theor Biol. 2022;550:111223. https://doi.org/10.1016/j.jtbi.2022.111223 .
doi: 10.1016/j.jtbi.2022.111223
pubmed: 35853493
Memon RA, Holleran WM, Uchida Y, Moser AH, Ichikawa S, Hirabayashi Y, et al. Regulation of glycosphingolipid metabolism in liver during the acute phase response. J Biol Chem. 1999;274(28):19707–13. https://doi.org/10.1074/jbc.274.28.19707 .
doi: 10.1074/jbc.274.28.19707
pubmed: 10391911
Russo D, Capolupo L, Loomba JS, Sticco L, D’Angelo G. Glycosphingolipid metabolism in cell fate specification. J Cell Sci. 2018;131(24). https://doi.org/10.1242/jcs.219204 .
Hardardottir I, Grunfeld C, Feingold KR. Effects of endotoxin and cytokines on lipid metabolism. Curr Opin Lipidol. 1994;5(3):207–15. https://doi.org/10.1097/00041433-199405030-00008 .
doi: 10.1097/00041433-199405030-00008
pubmed: 7952915
Metallo CM, Vander Heiden MG. Understanding metabolic regulation and its influence on cell physiology. Mol Cell. 2013;49(3):388–98. https://doi.org/10.1016/j.molcel.2013.01.018 .
doi: 10.1016/j.molcel.2013.01.018
pubmed: 23395269
pmcid: 3569837
Ruan S, Lloyd KO. Glycosylation pathways in the biosynthesis of gangliosides in melanoma and neuroblastoma cells: relative glycosyltransferase levels determine ganglioside patterns. Cancer Res. 1992;52(20):5725–31.
pubmed: 1394196
Berois N, Osinaga E. Glycobiology of neuroblastoma: impact on tumor behavior, prognosis, and therapeutic strategies. Front Oncol. 2014;4:114. https://doi.org/10.3389/fonc.2014.00114 .
doi: 10.3389/fonc.2014.00114
pubmed: 24904828
pmcid: 4033258
El Malki K, Wehling P, Alt F, Sandhoff R, Zahnreich S, Ustjanzew A, et al. Glucosylceramide Synthase Inhibitors Induce Ceramide Accumulation and Sensitize H3K27 Mutant Diffuse Midline Glioma to Irradiation. Int J Mol Sci. 2023;24(12). https://doi.org/10.3390/ijms24129905 .
Ly S, Anand V, El-Dana F, Nguyen K, Cai Y, Cai S, et al. Anti-GD2 antibody dinutuximab inhibits triple-negative breast tumor growth by targeting GD2(+) breast cancer stem-like cells. J Immunother Cancer. 2021;9(3). https://doi.org/10.1136/jitc-2020-001197 .
Taki T, Ishikawa D, Ogura M, Nakajima M, Handa S. Ganglioside GD1alpha functions in the adhesion of metastatic tumor cells to endothelial cells of the target tissue. Cancer Res. 1997;57(10):1882–8.
pubmed: 9157980
Hatano K, Miyamoto Y, Nonomura N, Kaneda Y. Expression of gangliosides, GD1a, and sialyl paragloboside is regulated by NF-kappaB-dependent transcriptional control of alpha2,3-sialyltransferase I, II, and VI in human castration-resistant prostate cancer cells. Int J Cancer. 2011;129(8):1838–47. https://doi.org/10.1002/ijc.25860 .
doi: 10.1002/ijc.25860
pubmed: 21165949
Jennemann R, Federico G, Mathow D, Rabionet M, Rampoldi F, Popovic ZV, et al. Inhibition of hepatocellular carcinoma growth by blockade of glycosphingolipid synthesis. Oncotarget. 2017;8(65):109201–109216. https://doi.org/10.18632/oncotarget.22648 .
Wingerter A, El Malki K, Sandhoff R, Seidmann L, Wagner DC, Lehmann N, et al. Exploiting Gangliosides for the Therapy of Ewing’s Sarcoma and H3K27M-Mutant Diffuse Midline Glioma. Cancers (Basel). 2021;13(3). https://doi.org/10.3390/cancers13030520 .
Miguel Llordes G, Medina Perez VM, Curto Simon B, Castells-Yus I, Vazquez Sufuentes S, Schuhmacher AJ. Epidemiology, diagnostic strategies, and therapeutic advances in diffuse midline glioma. J Clin Med. 2023;12(16). https://doi.org/10.3390/jcm12165261 .
Son MJ, Woolard K, Nam DH, Lee J, Fine HA. SSEA-1 is an enrichment marker for tumor-initiating cells in human glioblastoma. Cell Stem Cell. 2009;4(5):440–52. https://doi.org/10.1016/j.stem.2009.03.003 .
doi: 10.1016/j.stem.2009.03.003
pubmed: 19427293
pmcid: 7227614
Cuello HA, Segatori VI, Alberto M, Gulino CA, Aschero R, Camarero S, et al. Aberrant O-glycosylation modulates aggressiveness in neuroblastoma. Oncotarget. 2018;9(75):34176–34188. https://doi.org/10.18632/oncotarget.26169 .