Robust method for identification of prognostic gene signatures from gene expression profiles

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In the last decade, many attempts have been made to use gene expression profiles to identify prognostic genes for various types of cancer. Previous studies evaluating the prognostic value of genes suffered by failing to solve the critical problem of classifying patients into different risk groups based on specific gene expression threshold levels. Here, we present a novel method, called iterative patient partitioning (IPP), which was inspired by the receiver operating characteristic (ROC) curve, is based on the log-rank test and overcomes the threshold decision problem. We applied IPP to analyze datasets pertaining to various subtypes of breast cancer. Using IPP, we discovered both novel and well-studied prognostic genes related to cell cycle/proliferation or the immune response. The novel genes were further analyzed using copy-number alteration and mutation data, and these results supported their relationship with prognosis.
Publisher
NATURE PUBLISHING GROUP
Issue Date
2017-12
Language
English
Article Type
Article
Keywords

NEGATIVE BREAST-CANCER; REACTOME PATHWAY KNOWLEDGEBASE; MESSENGER-RNA EXPRESSION; METASTASIS; ACTIVATION; CARCINOMA; TAMOXIFEN; NETWORKS; REVEALS; MARKER

Citation

SCIENTIFIC REPORTS, v.7

ISSN
2045-2322
DOI
10.1038/s41598-017-17213-4
URI
http://hdl.handle.net/10203/237679
Appears in Collection
BiS-Journal Papers(저널논문)
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