Optimizing molecular signatures for predicting prostate cancer recurrence.

ABSTRACT:

Background

The derivation of molecular signatures indicative of disease status and predictive of subsequent behavior could facilitate the optimal choice of treatment for prostate cancer patients.

Methods

In this study, we conducted a computational analysis of gene expression profile data obtained from 79 cases, 39 of which were classified as having disease recurrence, to investigate whether advanced computational algorithms can derive more accurate prognostic signatures for prostate cancer.

Results

At the 90% sensitivity level, a newly derived prognostic genetic signature achieved 85% specificity. This is the first reported genetic signature to outperform a clinically used postoperative nomogram. Furthermore, a hybrid prognostic signature derived by combination of the

SUBMITTER: Sun Y 

PROVIDER: S-EPMC3425355 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature