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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Published in:	International journal of cancer Vol. 131; no. 1; pp. 95 - 105
Main Authors:	Fekete, Tibor, Rásó, Erzsébet, Pete, Imre, Tegze, Bálint, Liko, István, Munkácsy, Gyöngyi, Sipos, Norbert, Rigó Jr, János, Györffy, Balázs
Format:	Journal Article
Language:	English
Published:	Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.07.2012 Wiley-Blackwell Wiley Subscription Services, Inc
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 RNA-directed DNA polymerase Enzyme ovarian cancer Transferases Ovary cancer meta-analysis Histology Malignant tumor Gene expression Survival Female genital diseases Metaanalysis Gene expression profile Ovarian diseases Nucleotidyltransferases Cancerology RT-PCR Classification bioinformatics Molecular biology Reverse transcription polymerase chain reaction Cancer
ISSN:	0020-7136, 1097-0215, 1097-0215
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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.
AbstractList	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. [PUBLICATION 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. 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.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.
Author	Fekete, Tibor Sipos, Norbert Rigó Jr, János Pete, Imre Rásó, Erzsébet Munkácsy, Gyöngyi Liko, István Tegze, Bálint Györffy, Balázs
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Keywords	RNA-directed DNA polymerase Enzyme ovarian cancer Transferases Ovary cancer meta-analysis Histology Malignant tumor Gene expression Survival Female genital diseases Metaanalysis Gene expression profile Ovarian diseases Nucleotidyltransferases Cancerology RT-PCR Classification bioinformatics Molecular biology Reverse transcription polymerase chain reaction Cancer
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Snippet	Transcriptomic analysis of global gene expression in ovarian carcinoma can identify dysregulated genes capable to serve as molecular markers for histology...
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SubjectTerms	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
Title	Meta-analysis of gene expression profiles associated with histological classification and survival in 829 ovarian cancer samples
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