Zhang Chunlong, Xu Yanjun, Yang Haixiu, Xu Yingqi, Dong Qun, Liu Siyao, Wu Tan, Zhang Yunpeng
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Oncotarget. 2017 Nov 30;8(67):111433-111443. doi: 10.18632/oncotarget.22811. eCollection 2017 Dec 19.
LncRNAs acting as miRNA sponges to indirectly regulate mRNAs is a novel layer of gene regulation, therefore, it is necessary to integrate lncRNA and gene levels for interpreting tumor biological mechanism. In this study, we developed a lncRNA-gene integrated strategy to infer functional activities for tumor analyses at the subpathway level. In this strategy, we reconstructed subpathway graphs by embedding lncRNA components and considered the expression levels of both genes and lncRNAs to infer subpathway activities for each tumor sample. And the activities were applied to three aspects of tumor analyses; First, the subpathway activities across tumor samples of five tumor types were analyzed, and it was observed that the samples with consistent subpathway activities were derived from the same or similar tumor types. Also, the subpathway activities could stratify samples into several subtypes which has different clinical characterization, e.g. survival status. Second, the subpathway activities between tumor and normal samples were analyzed, and the comparative results showed that subpathway activities displayed more specificities than entire pathway activities. Finally, based on the subpathway activities, we identified prognostic subpathways for lung cancer. Our subpathway-based signatures shared significant overlap with enrichment analysis results and displayed predictive power in the independent testing sets. In conclusion, our integrated strategy provided a framework to infer subpathway activities for tumor analyses and identify subpathway signatures for clinical use.
长链非编码RNA作为微小RNA海绵间接调控信使RNA是基因调控的一个新层面，因此，整合长链非编码RNA和基因水平以阐释肿瘤生物学机制很有必要。在本研究中，我们开发了一种长链非编码RNA-基因整合策略，用于在亚通路水平推断肿瘤分析的功能活性。在该策略中，我们通过嵌入长链非编码RNA成分重建亚通路图，并考虑基因和长链非编码RNA的表达水平来推断每个肿瘤样本的亚通路活性。这些活性应用于肿瘤分析的三个方面：首先，分析了五种肿瘤类型的肿瘤样本间的亚通路活性，观察到亚通路活性一致的样本来自相同或相似的肿瘤类型。此外，亚通路活性可将样本分层为具有不同临床特征（如生存状态）的几个亚型。其次，分析了肿瘤样本与正常样本之间的亚通路活性，比较结果表明亚通路活性比整个通路活性表现出更多的特异性。最后，基于亚通路活性，我们鉴定出了肺癌的预后亚通路。我们基于亚通路的特征与富集分析结果有显著重叠，并在独立测试集中显示出预测能力。总之，我们的整合策略提供了一个框架，用于推断肿瘤分析的亚通路活性并鉴定临床应用的亚通路特征。
