Screening for genes associated with ovarian cancer prognosis / 中华医学杂志(英文版)

ABSTRACT

<p><b>BACKGROUND</b>Human epithelial ovarian cancer cell line SKOV3.ip1 is more invasive and metastatic compared with its parental line SKOV3. A total of 17 000 human genome complementary DNA microarrays were used to compare the gene expression patterns of the two cell lines. Based on this, the gene expression profiles of 22 patients with ovarian cancer were analyzed by cDNA microarray, and screened the 2-fold differentially expressed genes compared with the normal ones. We screened genes relevant to clinical prognosis of serous ovarian cancer by determining the expression profiles of ovarian cancer genes to investigate cell receptor and immunity-associated genes, and as groundwork, identify ovarian cancer-associated antigens at the gene level.</p><p><b>METHODS</b>Total RNA was extracted from 22 patients with ovarian cancer and DNA microarrays were prepared. After scanning, hybridization signals were collected and the genes that were differentially expressed twice as compared with the normal ones were screened.</p><p><b>RESULTS</b>We screened 236 genes relevant to the prognosis of ovarian cancer from the 17 000 human genome cDNA microarrays. According to gene classification, 48 of the 236 genes were cell receptor or immunity-associated genes, including 2 genes related to the International Federation of Gynecology and Obstetrics (FIGO) stage, 4 genes to histological grade, 18 genes to lymph node metastasis, 11 genes to residual disease, and 13 genes to the reactivity to chemotherapy. Several functionally important genes including fibronectin 1, pericentriolar material 1, beta-2-microglobulin, PPAR binding protein were identified through review of the literature.</p><p><b>CONCLUSIONS</b>The cDNA microarray of ovarian cancer genes developed in this study was effective and high throughput in screening the ovarian cancer-associated genes differentially expressed. Through the studies of the cell receptor and immunity-associated genes we expect to identify the molecular biology index of ovarian cancer-associated antigens.</p>