Predicting Functional Modules of Liver Cancer Based on Differential Network Analysis

  • Bo Hu
  • Xiao ChangEmail author
  • Xiaoping LiuEmail author
Original Research Article


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,, 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.


Differential network analysis Functional modules Liver cancer LncRNA 



This work is supported by the National Natural Science Foundation of China (Grant no: 61403363) and Key Project of Natural Science of Anhui Provincial Education Department (No. KJ2016A002).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

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Copyright information

© International Association of Scientists in the Interdisciplinary Areas 2019

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsShandong University at WeihaiWeihaiChina
  2. 2.School of Statistics and Applied MathematicsAnhui University of Finance & EconomicsBengbuChina

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