BACKGROUND: We have recently demonstrated that simple ratios of the expression levels of selected genes in @entity5 samples can be used to distinguish among types of @entity5 . We examined whether this technique could predict treatment-related outcome for @entity1 with @entity2615 . METHODS: We used gene expression profiling data previously collected from 17 @entity2615 @entity1 with different overall survival times to define two outcome-related groups of @entity1 and to train an expression ratio-based outcome predictor model. A Student's t test was used to identify genes among the two outcome groups that had statistically significant, inversely correlated expression levels; those genes were used to form prognostic expression ratios. We used a combination of several highly accurate expression ratios and cross-validation techniques to assess the internal consistency of this predictor model, quantitative reverse transcription-polymerase chain reaction of @entity5 RNA to confirm the microarray data, and Kaplan-Meier survival analysis to validate the model among an independent set of 29 @entity5 . All statistical tests were two-sided. RESULTS: We developed an expression ratio-based test capable of identifying 100% (17/17) of the samples used to train the model. This test remained highly accurate (88%, 15/17) after cross-validation. A four-gene expression ratio test statistically significantly (P =.0035) predicted treatment-related @entity1 outcome in @entity2615 independent of the histologic subtype of the @entity5 . CONCLUSIONS: Gene expression ratio-based analysis accurately predicts treatment-related outcome in @entity2615 samples. This technique could impact the clinical treatment of @entity2615 by allowing the preoperative identification of @entity1 with widely divergent prognoses.

Using gene expression ratios to predict outcome among XXXX with @entity2615 .

@entity5 :: ('MESH:D009369', 'Disease') :: ['tumor', 'thoracic malignancies', 'mesothelioma tumors'] @entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity2615 :: ('MESH:D008654', 'Disease') :: ['mesothelioma']
@entity1 :: (9606,Species) :: ['patients']
