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.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Zhou XH,Chu XY,Xue G,Xiong JH,Zhang HYdoi
10.1186/s12859-019-2674-zsubject
Has Abstractpub_date
2019-02-18 00:00:00pages
85issue
1issn
1471-2105pii
10.1186/s12859-019-2674-zjournal_volume
20pub_type
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