Meta-analysis of gene expression profiles associated with histological classification and survival in 829 ovarian cancer samples

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...

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Bibliographic Details
Published in:International journal of cancer 2012-07, Vol.131 (1), p.95-105
Main Authors: Fekete, Tibor, Rásó, Erzsébet, Pete, Imre, Tegze, Bálint, Liko, István, Munkácsy, Gyöngyi, Sipos, Norbert, Rigó Jr, János, Györffy, Balázs
Format: Article
Language:English
Subjects:
Aged
bioinformatics
Biological and medical sciences
Biomarkers, Tumor - genetics
Cancer
Databases, Genetic
Datasets
Disease Progression
Disease-Free Survival
Female
Female genital diseases
gamma-Synuclein - genetics
Gene expression
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Gynecology. Andrology. Obstetrics
Histology
Humans
Medical research
Medical sciences
Meta-analysis
Middle Aged
Neoplasm Proteins - genetics
Oligonucleotide Array Sequence Analysis
Ovarian cancer
Ovarian carcinoma
Ovarian Neoplasms - genetics
Ovarian Neoplasms - mortality
Ovarian Neoplasms - pathology
Ovary - anatomy & histology
Ovary - metabolism
Ovary - pathology
Receptors, Estrogen - genetics
Receptors, Progesterone - genetics
RT-PCR
Survival
tau Proteins - genetics
Tetraspanins - genetics
Transcriptomics
Tumors
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Summary: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.
ISSN:0020-7136
1097-0215
DOI:10.1002/ijc.26364