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

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.With $43,000 cases in Europe and $22,000 cases in the United States of America each year, ovarian carcinoma is the eighth most frequent malignant tumor in the female population. Although some improvements were achieved in the 5-year survival due to improved efficiency of surgery and treatment with empirically optimized combinations of cytotoxic drugs, the overall cure rate today remains as low as 30%. The most likely explanation for this is the high heterogeneity of ovarian carcinomas.Subtypes of ovarian cancer are recognized based on grade and on histologic subtypes. While high-grade malignancies grow rapidly, are relatively chemosensitive and evolve without a definitive precursor lesion, low-grade tumors grow more slowly, are more resistant to chemotherapy and share molecular characteristics with other low-malignant potential neoplasms. 1 Expression profiling studies have shown that high-grade tumors cluster separately from low-grade carcinomas and borderline tumors. 2,3 About 90% of epithelial ovarian cancers are clonal. 4 This is also reflected in their classification into four different main histotypes of high-grade serous (resembling normal cells of the fallopian tube), endometrioid (cells of the endometrium), mucinous (endocervix) and clear cell (vagina) cancers. The correlation between the different subtypes and their precursor cells were already confirmed by altered gene expression patterns. 5 These subtypes show further differences regarding their epidemiology, genetic changes, gene expression, tumor markers and chemotherapy response. Meanwhile, similarities were also described between high-grade sero...

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