Complex diseases are generally caused by disorders of biological networks or/and mutations in multiple genes. The efficient and systematic identification of functional modules can not only supply effective diagnosis and treatment in clinic, but also benefit in further in-depth analysis of the pathological mechanism of complex diseases. In this study, we applied the method of differential network to identify functional modules between control and disease samples, which are different from most of the current approaches that focus on differential expression. In particular, we applied our approach to analyze transcriptome data of liver cancer in The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/), and we obtained two modules associated with liver cancer. One is a functional gene module that contains a set of liver cancer-related genes, and another is an lncRNA (long non-coding RNA) module that includes liver cancer-related lncRNAs. The results of survival analysis and classification show that the functional modules cannot only be used as effective modular biomarkers to identifying liver cancer, but also predict the prognosis of liver cancer. The method can identify functional modules in genes and lncRNA from liver cancer, and these modules can be used to do prognosis detection and further study in mechanism of liver cancer.

中文翻译：

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基于差分网络分析的肝癌功能模块预测。

复杂疾病通常是由生物网络疾病或/和多个基因的突变引起的。对功能模块的有效，系统的识别，不仅可以为临床提供有效的诊断和治疗方法，还有助于进一步深入分析复杂疾病的病理机制。在这项研究中，我们应用了差异网络的方法来识别对照和疾病样本之间的功能模块，这与当前大多数关注差异表达的方法不同。特别是，我们在《癌症基因组图集》（TCGA，https：//cancergenome.nih.gov/）中应用了我们的方法来分析肝癌的转录组数据，并获得了两个与肝癌相关的模块。一个是功能基因模块，其中包含一组与肝癌相关的基因，另一个是lncRNA（长非编码RNA）模块，其中包括与肝癌相关的lncRNA。生存分析和分类的结果表明，功能模块不仅可以用作识别肝癌的有效模块化生物标志物，而且可以预测肝癌的预后。该方法可以鉴定出肝癌基因和lncRNA中的功能模块，可用于肝癌的预后检测和肝癌发生机理的进一步研究。