Meta-analysis of gene expression profiles associated with histological classification and survival in 829 ovarian cancer samples - Fekete - 2011 - International Journal of Cancer
Abstract
Transcriptomic
analysis of global gene expression in ovarian carcinoma can identify
dysregulated genes capable to serve as molecular markers for histology
subtypes and survival. The aim of our study was to validate previous
candidate signatures in an independent setting and to identify single
genes capable to serve as biomarkers for ovarian cancer progression. As
several datasets are available in the GEO today, we were able to perform
a true meta-analysis. First, 829 samples (11 datasets) were downloaded,
and the predictive power of 16 previously published gene sets was
assessed. Of these, eight were capable to discriminate histology
subtypes, and none was capable to predict survival. To overcome the
differences in previous studies, we used the 829 samples to identify new
predictors. Then, we collected 64 ovarian cancer samples (median
relapse-free survival 24.5 months) and performed TaqMan Real Time
Polimerase Chain Reaction (RT-PCR) analysis for the best 40 genes
associated with histology subtypes and survival. Over 90% of
subtype-associated genes were confirmed. Overall survival was
effectively predicted by hormone receptors (PGR and ESR2) and by TSPAN8.
Relapse-free survival was predicted by MAPT and SNCG. In summary, we
successfully validated several gene sets in a meta-analysis in large
datasets of ovarian samples. Additionally, several individual genes
identified were validated in a clinical cohort.