Unraveling the glycosphingolipid metabolism by leveraging transcriptome-weighted network analysis on neuroblastic tumors.

IF 6 3区 医学 Q1 CELL BIOLOGY Cancer & Metabolism Pub Date : 2024-10-24 DOI:10.1186/s40170-024-00358-y
Arsenij Ustjanzew, Annekathrin Silvia Nedwed, Roger Sandhoff, Jörg Faber, Federico Marini, Claudia Paret
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引用次数: 0

Abstract

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: 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: 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: 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.

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利用神经母细胞瘤转录组加权网络分析揭示糖磷脂代谢过程
背景:糖磷脂(GSL)是一种膜脂,由神经酰胺骨架与糖分子连接组成。神经节苷脂的生物合成是 GSL 新陈代谢的一部分,其中涉及由特定酶催化的连续反应,这些酶部分具有较差的底物特异性。GSL 在癌症中会发生失调,因此可作为潜在的生物标记物用于个性化治疗或亚型分类。然而,GSL 图谱的分析非常复杂,需要专门的技术,而这些技术目前并不包括在肿瘤分子委员会等场合普遍采用的高通量检测方法中:在这项研究中,我们结合转录组数据和代谢网络的拓扑信息,开发了一种方法来区分神经节苷脂代谢途径中四个系列的酶活性。我们为反应活性评分(RAS)引入了三种调整选项,并通过将该方法应用于神经母细胞瘤(NTs),包括神经母细胞瘤(NB)、神经节母细胞瘤(GNB)和神经节细胞瘤(GN),证明了它们在探索性分析和比较分析中的应用。此外,我们还结合早先发表的相同肿瘤的 GSL 测量结果对结果进行了解释:结果:通过使用基于网络拓扑和转换概率(TPs)的加权方案调整RAS值,神经节苷脂代谢的各个系列得以区分,从而能够对NT实体中的GSL概况进行精细分析。值得注意的是,我们提出的调整方法揭示了神经节苷脂系列在 NB 和 GNB 之间的不同参与。此外,MYCN基因的表达是众所周知的NT预后标志,它似乎与GD2等治疗相关神经节苷脂的表达相关。通过无监督学习,我们发现了基于GSL代谢改变的NB亚群:我们的研究证明了调整RAS值在区分神经节苷脂代谢亚型中的作用,突出了根据鞘脂特征识别新型癌症亚群的潜力。这些发现有助于更好地了解NT中神经节苷脂的失调,并可能对这些肿瘤和其他GSL代谢失调的肿瘤实体的分层和靶向治疗策略产生影响。
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来源期刊
自引率
1.70%
发文量
17
审稿时长
14 weeks
期刊介绍: Cancer & Metabolism welcomes studies on all aspects of the relationship between cancer and metabolism, including: -Molecular biology and genetics of cancer metabolism -Whole-body metabolism, including diabetes and obesity, in relation to cancer -Metabolomics in relation to cancer; -Metabolism-based imaging -Preclinical and clinical studies of metabolism-related cancer therapies.
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