Abstract

It was hypothesized that analysis of global gene expression in ovarian carcinoma can identify dysregulated genes that can serve as molecular markers and provide further insight into carcinogenesis and provide the basis for development of new diagnostic tools as well as new targeted therapy protocols. By applying bioinformatics tools for screening of biomedical databases, a gene expression profile databank, specific for ovarian carcinoma, was constructed with utilizable data sets published in 28 studies that applied different array technology platforms. The data sets were divided into four compartments: (i) genes associated with carcinogenesis: in 14 studies, 1881 genes were extracted, 75 genes were identified in more than one study, and only 4 genes (PRKCBP1, SPON1, TACSTD1, and PTPRM) were identified in three studies. (ii) Genes associated with histologic subtypes: in four studies, 463 genes could be identified, but none of them was identified in more than a single study. (iii) Genes associated with therapy response: in seven studies, 606 genes were identified from which 38 were differentially regulated in at least two studies, 3 genes (TMSB4X, GRN, and TJP1) in three studies, and 1 gene (IFITM1) in four studies. (iv) Genes associated with prognosis and progression: 254 genes were found in seven studies. From these genes, merely three were identified in at least two different studies. This snapshot of available gene expression data not only provides independently described potential diagnostic and therapeutic targets for ovarian carcinoma but also emphasizes the drawbacks of the current state of global gene expression analyses in ovarian cancer.