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首页> 外文期刊>Biophysical Chemistry: An International Journal Devoted to the Physical Chemistry of Biological Phenomena >Predicting neuroblastoma using developmental signals and a logic-based model
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Predicting neuroblastoma using developmental signals and a logic-based model

机译：使用发育信号和基于逻辑模型预测神经母细胞瘤

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摘要

Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic indicators of neuroblastoma, and find differences in the number and probability distribution of steady state outcomes. We validate the mechanistic model assumptions using RNAseq of the SHSY5Y human neuroblastoma cell line to define the input states and confirm the predicted outcome with antibody staining. Lastly, we apply input gene signatures from 77 published human patient samples and show that our model makes more accurate disease outcome predictions for early stage disease than any current neuroblastoma gene list. These findings highlight the predictive strength of a logic-based model based on developmental genes and offer a better understanding of the molecular network interactions during neuroblastoma disease progression.
机译：来自儿科神经母细胞瘤癌症的人类患者样品的基因组信息和已知结果导致特定的基因列表提出了疾病进展的高风险。然而，对基因表达相关性而不是机械洞察力的依赖表明了有限的潜力，并表明对更好地预测神经母细胞瘤进展的分子网络模型的关键需求。在该研究中，我们在6-基因输入逻辑模型中构建和模拟发育基因和下游信号的分子网络，该模型预测了基于四个细胞状态的结果，包括细胞分化，增殖，细胞凋亡和血管生成。我们模拟酪氨酸受体激酶，TRKA和TRKB，神经母细胞瘤的两个预后指标的错误表达，并发现稳态结果的数量和概率分布的差异。我们使用Shsy5Y人神经母细胞瘤细胞系的RNA阵列来验证机械模型假设，以确定输入状态，并确认抗体染色的预测结果。最后，我们从77名已发表的人类患者样品应用输入基因签名，并表明我们的模型使早期疾病的预测更准确地进行早期疾病的预测，而不是任何目前的神经母细胞瘤基因名单。这些发现突出了基于发育基因的基于逻辑的模型的预测强度，并在神经母细胞瘤病进展期间更好地了解分子网络相互作用。

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