ArticleOncotarget2017
Identifying prognostic signature in ovarian cancer using DirGenerank.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Systems Pharmacology-Based Precision Therapy and Drug Combination Discovery for Breast Cancer.Cancers · 2021Article
- A Novel Six-Gene Signature for Prognosis Prediction in Ovarian Cancer.Frontiers in genetics · 2020Article
- Identifying cancer prognostic modules by module network analysis.BMC bioinformatics · 2019Article
- Article
- An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules.Frontiers in genetics · 2019Article
- A Qualitative Transcriptional Signature for Predicting Recurrence Risk for High-Grade Serous Ovarian Cancer Patients Treated With Platinum-Taxane Adjuvant Chemotherapy.Frontiers in oncology · 2019Article
- Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma.Thoracic cancer · 2018Article
- Ovarian Cancers: Genetic Abnormalities, Tumor Heterogeneity and Progression, Clonal Evolution and Cancer Stem Cells.Medicines (Basel, Switzerland) · 2018Review
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Authors and funding
3 authors.
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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.
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