A time course-dependent metastatic gene expression signature predicts outcome in human metastatic melanomas.

ABSTRACT:

Background

The prognosis of patients with metastatic melanomas is extremely heterogeneous. Therefore, identifying high-risk subgroups by using innovative prediction models would help to improve selection of appropriate management options.

Methods

In this study, two datasets (GSE7929 and GSE7956) of mRNA expression microarray in an animal melanoma model were normalized by frozen Robust Multi-Array Analysis and then combined by the distance-weighted discrimination method to identify time course-dependent metastasis-related gene signatures by Biometric Research Branch-ArrayTools (BRB)-ArrayTools. Then two datasets (GSE8401 and GSE19234) of clinical melanoma samples with relevant clinical and survival data were used to validate the prognosis signature.

Results

A novel 192

SUBMITTER: Chen R 

PROVIDER: S-EPMC4149277 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature