Identifying cancer prognostic modules by module network analysis

BMC Bioinformatics

Gang Xue¹  Xiong-Hui Zhou¹  Xin-Yi Chu¹  Hong-Yu Zhang¹  Jiang-Hui Xiong² 

[1] Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University;State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center;

关键词: Module network;    Cancer prognosis;    GeneRank;    Drug targets;   

DOI  :  10.1186/s12859-019-2674-z

来源: DOAJ

【 摘 要 】

Abstract Background The identification of prognostic genes that can distinguish the prognostic risks of cancer patients remains a significant challenge. Previous works have proven that functional gene sets were more reliable for this task than the gene signature. However, few works have considered the cross-talk among functional gene sets, which may result in neglecting important prognostic gene sets for cancer. Results Here, we proposed a new method that considers both the interactions among modules and the prognostic correlation of the modules to identify prognostic modules in cancers. First, dense sub-networks in the gene co-expression network of cancer patients were detected. Second, cross-talk between every two modules was identified by a permutation test, thus generating the module network. Third, the prognostic correlation of each module was evaluated by the resampling method. Then, the GeneRank algorithm, which takes the module network and the prognostic correlations of all the modules as input, was applied to prioritize the prognostic modules. Finally, the selected modules were validated by survival analysis in various data sets. Our method was applied in three kinds of cancers, and the results show that our method succeeded in identifying prognostic modules in all the three cancers. In addition, our method outperformed state-of-the-art methods. Furthermore, the selected modules were significantly enriched with known cancer-related genes and drug targets of cancer, which may indicate that the genes involved in the modules may be drug targets for therapy. Conclusions We proposed a useful method to identify key modules in cancer prognosis and our prognostic genes may be good candidates for drug targets.

【 授权许可】

Unknown