Abstract
Purpose: Cancer of the ovary confers the worst prognosis among women with gynecological malignancies, primarily because most ovarian cancers are diagnosed at late stage. Hence, there is a substantial need to develop new diagnostic biomarkers to enable detection of ovarian cancer at earlier stages, which would confer better prognosis. In addition, the identification of druggable targets is of substantial interest to find new therapeutic strategies for ovarian cancer. Methods: The expression of 22,500 genes in a series of 67 serous papillary carcinomas was compared with 9 crudely enriched normal ovarian tissue samples by RNA hybridization on oligonucleotide microarrays. Multiple genes with near-uniformly expression were elevated in carcinomas of varying grade and malignant potential, including several previously described genes (e.g., MUC-1, CD9, CD24, claudin 3, and mesothelin). We performed immunohistochemical staining with antibodies against several of the proteins encoded by differentially expressed genes in an independent cohort of 71 cases of paraffin-embedded ovarian cancer samples. Results: We found striking differences in EpCAM (p < 0.005), CD9 (p < 0.001), MUC-1 (p < 0.001), and claudin 3 proteins (p < 0.001) but not for mesothelin (p > 0.05) using the Mann-Whitney U test. Conclusions: Protein expression of a majority of the differentially expressed genes tested was found to be elevated in ovarian carcinomas and, as such, define potential new biomarkers or targets.
| Original language | English |
|---|---|
| Journal | Journal of Cancer Research and Clinical Oncology |
| Volume | 139 |
| Issue number | 2 |
| Pages (from-to) | 347-355 |
| Number of pages | 9 |
| ISSN | 0171-5216 |
| DOIs | |
| Publication status | Published - 02.2013 |
Funding
Acknowledgments We thank Lisa Sapinoso for preparing the gene array analyses. We also thank Herta Bettendorf for her excellent staining of the tissue microarrays. This work was partially supported by Deutsche Krebshilfe/German Cancer Aid grant 70-3099-Me1 and by NCI-grant 5R21CA152794-02. We thank Ulrike Schulz at Medi-Stat for her excellent support in preparing statistical analyses.