Abstract：
Identification of cancer subtypes based on molecular knowledge is crucial for improving the patient diagnosis, prognosis, and treatment. In this work, we integrated copy number variations (CNVs) and transcriptomic data of Kidney Papillary Renal Cell Carcinoma (KIRP) using a network diffusion strategy to stratify cancers into clinically and biologically relevant subtypes. We constructed GeneNet, a KIRP specific gene expression network from RNA-seq data. The copy number variation data was projected onto GeneNet and propagated on the network for clustering. We identified robust subtypes that are biologically informative and significantly associated with patient survival, tumor stage and clinical subtypes of KIRP. We performed a Singular Value Decomposition (SVD) analysis of KIRP subtypes, which revealed the genes/silent players related to poor survival. A differential gene expression analysis between subtypes showed that genes related to immune, extracellular matrix organization, and genomic instability are upregulated in the poor survival group. Overall, the network-based approach revealed the molecular subtypes of KIRP and captured the relationship between gene expression and CNVs. This framework can be further expanded to integrate other omics data.
摘要：
基于分子知识识别癌症亚型对于改善患者诊断至关重要。预后,和治疗。在这项工作中,我们使用网络扩散策略整合了肾乳头状肾细胞癌(KIRP)的拷贝数变异(CNVs)和转录组数据,将癌症分为临床和生物学相关亚型.我们建造了GeneNet,来自RNA-seq数据的KIRP特异性基因表达网络。将拷贝数变异数据投影到GeneNet上并在网络上传播用于聚类。我们确定了具有生物学信息并与患者生存显着相关的强大亚型，肿瘤分期和KIRP的临床亚型。我们对KIRP亚型进行了奇异值分解(SVD)分析，这揭示了与生存不良有关的基因/沉默的球员。亚型之间的差异基因表达分析表明，与免疫相关的基因，细胞外基质组织，和基因组不稳定性在生存不良组中上调。总的来说,基于网络的方法揭示了KIRP的分子亚型，并捕获了基因表达与CNVs之间的关系。该框架可以进一步扩展以整合其他组学数据。
