An integrative approach for the identification of prognostic and predictive biomarkers in XXXX .

@entity1 @entity15339 @entity5 @entity18621 @entity10885 @entity14 @entity373 @entity371 @entity372 @entity9218
INTRODUCTION: @entity14 is the third most common @entity5 in the world, a small fraction of which is represented by locally advanced @entity5 (LARC). If not medically contraindicated, preoperative chemoradiotherapy, represent the standard of care for LARC @entity1 . Unfortunately, @entity1 shows a wide range of response rates in which approximately 20% has a complete pathological response, whereas in 20 to 40% the response is poor or absent. RESULTS: The following specific gene signature, able to discriminate responders' @entity1 from non-responders, were founded: @entity15339 , @entity372 , @entity371 , @entity9218 , @entity373 , @entity10885 and @entity18621 . These genes are mainly involved in immune system pathways and interact with drugs traditionally used in the adjuvant treatment of @entity5 . DISCUSSION: The present study suggests that new ideas for therapy could be found not only limited to studying genes differentially expressed between the two groups of @entity1 but deepening the mechanisms, associated to response, in which they are involved. METHODS: Gene expression studies performed by: Agostini et al., Rimkus et al. and Kim et al. have been merged through a meta-analysis of the raw data. Gene expression data-sets have been processed using A-MADMAN. Common differentially expressed gene (DEG) were identified through SAM analysis. To further characterize the identified DEG we deeply investigated its biological role using an integrative computational biology approach.
