Purpose: High-grade serous ovarian cancers are heterogeneous not only in terms of clinical outcome but also at the molecular level. Our aim was to establish a novel risk classification system based on a gene expression signature for predicting overall survival, leading to suggesting novel therapeutic strategies for high-risk patients.Experimental Design: In this large-scale cross-platform study of six microarray data sets consisting of 1,054 ovarian cancer patients, we developed a gene expression signature for predicting overall survival by applying elastic net and 10-fold cross-validation to a Japanese data set A (n ¼ 260) and evaluated the signature in five other data sets. Subsequently, we investigated differences in the biological characteristics between high-and low-risk ovarian cancer groups.Results: An elastic net analysis identified a 126-gene expression signature for predicting overall survival in patients with ovarian cancer using the Japanese data set A (multivariate analysis, P ¼ 4 Â 10 À20 ). We validated its predictive ability with five other data sets using multivariate analysis (Tothill's data set, P ¼ 1 Â 10 À5 ; Bonome's data set, P ¼ 0.0033; Dressman's data set, P ¼ 0.0016; TCGA data set, P ¼ 0.0027; Japanese data set B, P ¼ 0.021). Through gene ontology and pathway analyses, we identified a significant reduction in expression of immune-response-related genes, especially on the antigen presentation pathway, in high-risk ovarian cancer patients.Conclusions: This risk classification based on the 126-gene expression signature is an accurate predictor of clinical outcome in patients with advanced stage high-grade serous ovarian cancer and has the potential to develop new therapeutic strategies for high-grade serous ovarian cancer patients.
To elucidate the mechanisms of rapid progression of serous ovarian cancer, gene expression profiles from 43 ovarian cancer tissues comprising eight early stage and 35 advanced stage tissues were carried out using oligonucleotide microarrays of 18 716 genes. By non-negative matrix factorization analysis using 178 genes, which were extracted as stage-specific genes, 35 advanced stage cases were classified into two subclasses with superior (n = 17) and poor (n = 18) outcome evaluated by progression-free survival (log rank test, P = 0.03). Of the 178 stagespecific genes, 112 genes were identified as showing different expression between the two subclasses. Of the 48 genes selected for biological function by gene ontology analysis or Ingenuity Pathway Analysis, five genes (ZEB2, CDH1, LTBP2, COL16A1, and ACTA2) were extracted as candidates for prognostic factors associated with progression-free survival. The relationship between high ZEB2 or low CDH1 expression and shorter progression-free survival was validated by real-time RT-PCR experiments of 37 independent advanced stage cancer samples. ZEB2 expression was negatively correlated with CDH1 expression in advanced stage samples, whereas ZEB2 knockdown in ovarian adenocarcinoma SKOV3 cells resulted in an increase in CDH1 expression. Multivariate analysis showed that high ZEB2 expression was independently associated with poor prognosis. Furthermore, the prognostic effect of E-cadherin encoded by CDH1 was verified using immunohistochemical analysis of an independent advanced stage cancer samples set (n = 74). These findings suggest that the expression of epithelial-mesenchymal transition-related genes such as ZEB2 and CDH1 may play important roles in the invasion process of advanced stage serous ovarian cancer. (Cancer Sci 2009; 100: 1421-1428) T he serous type, comprising approximately 50% of ovarian cancers, is the most aggressive histology and has a tendency to be detected as advanced stage at the time of diagnosis. (1,2) Patients with advanced stage serous ovarian cancer are managed with surgical cytoreduction followed by platinum and taxane-based chemotherapy. Serous ovarian cancer is moderately chemosensitive and initially responds to postoperative chemotherapy, but the survival of patients with advanced stage remains poor. Because the majority of early stage ovarian cancers are asymptomatic and there is as yet no reliable screening test, it is difficult to diagnose early stage serous ovarian cancer. Therefore, the molecular mechanisms of progression in serous ovarian cancer should provide valuable clues for early detection and improved prognosis.The development of microarray technology permits analysis of the expression levels of thousands of genes in cancer cells, and several studies have shown that microarrays can be used to identify gene expression profiles associated with surgery outcome, response to chemotherapy, grade, and survival in ovarian cancers.(3-17) However, there are limited reports of microarray analysis on tumor progression. (18)(19)(20) Serous o...
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