Robust method for identification of prognostic gene signatures from gene expression profiles.
Woogwang SimJungsul LeeChulhee ChoiPublished in: Scientific reports (2017)
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.
Keyphrases
- genome wide
- gene expression
- dna methylation
- copy number
- cell cycle
- bioinformatics analysis
- genome wide identification
- mitochondrial dna
- immune response
- end stage renal disease
- cell proliferation
- genome wide analysis
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- signaling pathway
- toll like receptor
- patient reported outcomes
- decision making