Research Papers:
Identifying prognostic signature in ovarian cancer using DirGenerank
PDF | HTML | Supplementary Files | How to cite
Metrics: PDF 1889 views | HTML 3311 views | ?
Abstract
Jian-Yong Wang1, Ling-Ling Chen1 and Xiong-Hui Zhou1
1College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
Correspondence to:
Xiong-Hui Zhou, email: [email protected]
Keywords: DirGenerank, ovarian cancer, prognosis, biomarker, drug target
Received: March 17, 2017 Accepted: April 26, 2017 Published: May 25, 2017
ABSTRACT
Identifying the prognostic genes in cancer is essential not only for the treatment of cancer patients, but also for drug discovery. However, it’s still a big challenge to select the prognostic genes that can distinguish the risk of cancer patients across various data sets because of tumor heterogeneity. In this situation, the selected genes whose expression levels are statistically related to prognostic risks may be passengers. In this paper, based on gene expression data and prognostic data of ovarian cancer patients, we used conditional mutual information to construct gene dependency network in which the nodes (genes) with more out-degrees have more chances to be the modulators of cancer prognosis. After that, we proposed DirGenerank (Generank in direct netowrk) algorithm, which concerns both the gene dependency network and genes’ correlations to prognostic risks, to identify the gene signature that can predict the prognostic risks of ovarian cancer patients. Using ovarian cancer data set from TCGA (The Cancer Genome Atlas) as training data set, 40 genes with the highest importance were selected as prognostic signature. Survival analysis of these patients divided by the prognostic signature in testing data set and four independent data sets showed the signature can distinguish the prognostic risks of cancer patients significantly. Enrichment analysis of the signature with curated cancer genes and the drugs selected by CMAP showed the genes in the signature may be drug targets for therapy. In summary, we have proposed a useful pipeline to identify prognostic genes of cancer patients.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License.
PII: 18189