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@ARTICLE{Ustjanzew:294328,
      author       = {A. Ustjanzew and A. S. Nedwed and R. Sandhoff$^*$ and J.
                      Faber and F. Marini and C. Paret},
      title        = {{U}nraveling the glycosphingolipid metabolism by leveraging
                      transcriptome-weighted network analysis on neuroblastic
                      tumors.},
      journal      = {Cancer $\&$ metabolism},
      volume       = {12},
      number       = {1},
      issn         = {2049-3002},
      address      = {London},
      publisher    = {Biomed Central},
      reportid     = {DKFZ-2024-02161},
      pages        = {29},
      year         = {2024},
      abstract     = {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.},
      keywords     = {GD2 (Other) / Ganglioneuroblastoma (Other) / Ganglioneuroma
                      (Other) / Ganglioside (Other) / Glycosphingolipids (Other) /
                      Metabolic graph (Other) / Neuroblastoma (Other) / Reaction
                      activity score (Other)},
      cin          = {A411},
      ddc          = {610},
      cid          = {I:(DE-He78)A411-20160331},
      pnm          = {311 - Zellbiologie und Tumorbiologie (POF4-311)},
      pid          = {G:(DE-HGF)POF4-311},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39449099},
      doi          = {10.1186/s40170-024-00358-y},
      url          = {https://inrepo02.dkfz.de/record/294328},
}