BACKGROUND More than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need more accurate stratification to establish an individualized precise treatment plan for the patients to improve the long-term survival rate. RESULTS We focus on extracting features and providing a workflow to improve survival prediction for neuroblastoma patients. With a workflow for gene co-expression network (GCN) mining in microarray and RNA-Seq datasets, we extracted molecular features from each co-expressed module and summarized them into eigengenes. Then we adopted the lasso-regularized Cox proportional hazards model to select the most informative eigengene features regarding association to the risk of metastasis. Nine eigengenes were selected which show strong association with patient survival prognosis. All of the nine corresponding gene modules also have highly enriched biological functions or cytoband locations. Three of them are unique modules to RNA-Seq data, which complement the modules from microarray data in terms of survival prognosis. We then merged all eigengenes from these unique modules and used an integrative method called Similarity Network Fusion to test the prognostic power of these eigengenes for prognosis. The prognostic accuracies are significantly improved as compared to using all eigengenes, and a subgroup of patients with very poor survival rate was identified. CONCLUSIONS We first compared GCNs mined from microarray and RNA-seq data. We discovered that each data modality yields unique GCNs, which are enriched with clear biological functions. Then we do module unique analysis and use lasso-cox model to select survival-associated eigengenes. Integration of unique and survival-associated eigengenes from both data types provides complementary information that leads to more accurate survival prognosis. REVIEWERS Reviewed by Susmita Datta, Marco Chierici and Dimitar Vassilev.

中文翻译：

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基于生存相关共表达基因模块的综合分析，用于预测神经母细胞瘤患者的生存时间。

背景技术在低风险组中，超过90％的成神经细胞瘤患者可以治愈，而高危疾病患者中只有不到50％可以治愈。由于高危患者的预后仍然较差，因此我们需要进行更准确的分层，以建立针对患者的个性化精确治疗计划，以提高长期生存率。结果我们专注于提取特征并提供改善神经母细胞瘤患者生存预测的工作流程。通过在微阵列和RNA-Seq数据集中挖掘基因共表达网络（GCN）的工作流程，我们从每个共表达模块中提取了分子特征并将其概括为特征基因。然后，我们采用套索正则化的Cox比例风险模型来选择与转移风险相关性最丰富的特征基因特征。选择了九个与患者生存预后密切相关的特征基因。九个相应的基因模块都具有高度丰富的生物学功能或细胞带位置。其中三个是RNA-Seq数据的独特模块，可在生存预后方面补充微阵列数据的模块。然后，我们合并了这些独特模块中的所有特征基因，并使用一种称为“相似性网络融合”的集成方法来测试这些特征基因对预后的预​​测能力。与使用所有本征基因相比，预后准确性显着提高，并且确定了亚组的生存率非常低的患者。结论我们首先比较了从微阵列和RNA-seq数据中提取的GCN。我们发现，每种数据形式都会产生唯一的GCN，富含清晰的生物学功能。然后，我们进行模块唯一分析，并使用套索-考克斯模型选择与生存相关的本征基因。来自这两种数据类型的独特的和与生存相关的本征基因的整合提供了补充信息，可导致更准确的生存预后。评论者：Susmita Datta，Marco Chierici和Dimitar Vassilev。