Differential gene expression identifies subgroups of ovarian carcinoma

Papillary serous ovarian carcinoma, the most common type of ovarian cancer, displays different biological behavior in different patients. This heterogeneity cannot be recognized by light microscopy. In this study, gene expression in 29 papillary serous ovarian carcinoma samples (21 invasive tumors and 8 borderline tumors), and 17 nonmalignant tissue types comprising 512 samples, was determined using Affymetrix U_133 oligonucleotide microarrays (Affymetrix, Inc., Santa Clara, Calif) representing approximately 40,000 known genes and expression sequence tags (ESTs). Differences in gene expression were quantified as the fold change in gene expression between the various sets of samples. A set of genes was identified that was over-expressed in the invasive ovarian carcinoma samples compared with the normal ovary samples. Principle component analysis of the set of invasive ovarian carcinomas using this set of genes revealed the existence of 2 major subgroups among the invasive ovarian carcinomas. A series of principle component analyses of the ovarian carcinomas using different gene sets composed of genes involved in different metabolic pathways also revealed the same 2 major subgroups of the invasive ovarian carcinomas. Review of the pathology by a single pathologist in a blinded manner suggested that these 2 subgroups differed in pathologic grade. Genes differentially expressed between the 2 ovarian carcinoma subsets were identified. Examination of gene expression in each ovarian carcinoma subset compared with that in 17 different normal tissue types (512 samples) revealed genes specifically over-expressed in ovarian carcinoma compared with these normal tissues. It is concluded that gene expression patterns may be useful in helping to further classify subtypes of papillary serous ovarian carcinoma that may have clinical significance. In addition, the genes identified as over-expressed in each set of serous ovarian carcinoma compared with normal tissues may represent potential biomarkers and/or targets for therapy.