Test on existence of histology subtype-specific prognostic signatures among @entity2 and @entity957 XXXX using a Cox-model based filter.

@entity1 @entity957 @entity1149 @entity2 @entity1178 @entity420
BACKGROUND: @entity1149 ( @entity1149 ) is the predominant histological type of @entity2 , accounting for up to 85% of cases. Disease stage is commonly used to determine adjuvant treatment eligibility of @entity1149 @entity1 , however, it is an imprecise predictor of the prognosis of an individual @entity1 . Currently, many researchers resort to microarray technology for identifying relevant genetic prognostic markers, with particular attention on trimming or extending a Cox regression model. @entity420 ( @entity420 ) and @entity957 ( @entity957 ) are two major histology subtypes of @entity1149 . It has been demonstrated that fundamental differences exist in their underlying mechanisms, which motivated us to postulate the existence of specific genes related to the prognosis of each histology subtype. RESULTS: In this article, we propose a simple filter feature selection algorithm with a Cox regression model as the base. Applying this method to real-world microarray data identifies a histology-specific prognostic gene signature. Furthermore, the resulting 32-gene (32/12 for @entity420 / @entity957 ) prognostic signature for early-stage @entity420 and @entity957 samples has superior predictive ability relative to two relevant prognostic signatures, and has comparable performance with signatures obtained by applying two state-of-the art algorithms separately to @entity420 and @entity957 samples. CONCLUSIONS: Our proposal is conceptually simple, and straightforward to implement. Furthermore, it can be easily adapted and applied to a range of other research settings. REVIEWERS: This article was reviewed by Leonid Hanin (nominated by Dr. Lev Klebanov), Limsoon Wong and @entity1178 Yu.
