Brief. Bioinformatics - Ranking prognosis markers in cancer genomic studies.

Tópicos

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Resumo

In cancer research, high-throughput genomic studies have been extensively conducted, searching for markers associated with cancer diagnosis, prognosis and variation in response to treatment. In this article, we analyze cancer prognosis studies and investigate ranking markers based on their marginal prognosis power. To avoid ambiguity, we focus on microarray gene expression studies where genes are the markers, but note that the methodology and results are applicable to other high-throughput studies. The objectives of this study are 2-fold. First, we investigate ranking markers under three commonly adopted semiparametric models, namely the Cox, accelerated failure time and additive risk models. Data analysis shows that the ranking may vary significantly under different models. Second, we describe a nonparametric concordance measure, which has roots in the time-dependent ROC (receiver operating characteristic) framework and relies on much weaker assumptions than the semiparametric models. In simulation, it is shown that ranking using the concordance measure is not sensitive to model specification whereas ranking under the semiparametric models is. In data analysis, the concordance measure generates rankings significantly different from those under the semiparametric models.

Resumo Limpo

cancer research highthroughput genom studi extens conduct search marker associ cancer diagnosi prognosi variat respons treatment articl analyz cancer prognosi studi investig rank marker base margin prognosi power avoid ambigu focus microarray gene express studi gene marker note methodolog result applic highthroughput studi object studi fold first investig rank marker three common adopt semiparametr model name cox acceler failur time addit risk model data analysi show rank may vari signific differ model second describ nonparametr concord measur root timedepend roc receiv oper characterist framework reli much weaker assumpt semiparametr model simul shown rank use concord measur sensit model specif wherea rank semiparametr model data analysi concord measur generat rank signific differ semiparametr model

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