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

Tibor Fekete, E. Rásó, Imre Pete, Bálint Tegze, István Liko, Gyöngyi Munkácsy, Norbert Sipos, J. Rigó, B. Györffy

Research output: Contribution to journalArticle

30 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)95-105
Number of pages11
JournalInternational Journal of Cancer
Volume131
Issue number1
DOIs
Publication statusPublished - Jul 1 2012

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Transcriptome
Ovarian Neoplasms
Meta-Analysis
Genes
Histology
Recurrence
Biomarkers
Hormones
Carcinoma
Gene Expression
Datasets

Keywords

  • bioinformatics
  • gene expression
  • histology
  • meta-analysis
  • ovarian cancer
  • RT-PCR
  • survival

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

Meta-analysis of gene expression profiles associated with histological classification and survival in 829 ovarian cancer samples. / Fekete, Tibor; Rásó, E.; Pete, Imre; Tegze, Bálint; Liko, István; Munkácsy, Gyöngyi; Sipos, Norbert; Rigó, J.; Györffy, B.

In: International Journal of Cancer, Vol. 131, No. 1, 01.07.2012, p. 95-105.

Research output: Contribution to journalArticle

Fekete, Tibor ; Rásó, E. ; Pete, Imre ; Tegze, Bálint ; Liko, István ; Munkácsy, Gyöngyi ; Sipos, Norbert ; Rigó, J. ; Györffy, B. / Meta-analysis of gene expression profiles associated with histological classification and survival in 829 ovarian cancer samples. In: International Journal of Cancer. 2012 ; Vol. 131, No. 1. pp. 95-105.
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