Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer

被引:533
作者
Welsh, JB
Zarrinkar, PP
Sapinoso, LM
Kern, SG
Behling, CA
Monk, BJ
Lockhart, DJ
Burger, RA
Hampton, GM
机构
[1] Novartis Res Fdn, Gen Inst, San Diego, CA 92121 USA
[2] Univ Calif San Diego, Dept Pathol, San Diego, CA 92103 USA
[3] Affymetrix Inc, Santa Clara, CA 95051 USA
[4] Univ Calif Irvine, Dept Obstet & Gynecol, Div Gynecol Oncol, Med Ctr, Orange, CA 92868 USA
关键词
D O I
10.1073/pnas.98.3.1176
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Epithelial ovarian cancer is the leading cause of death from gynecologic cancer, in part because of the lack of effective early detection methods. Although alterations of several genes, such as c-erb-B2 c-myc, and p53, have been identified in a significant fraction of ovarian cancers, none of these mutations are diagnostic of malignancy or predictive of tumor behavior over time. Here, we used oligonucleotide microarrays with probe sets complementary to >6,000 human genes to identify genes whose expression correlated with epithelial ovarian cancer. We extended current microarray technology by simultaneously hybridizing ovarian RNA samples in a highly parallel manner to a single glass wafer containing 49 individual oligonucleotide arrays separated by gaskets within a custom-built chamber (termed "array-of-arrays"). Hierarchical clustering of the expression data revealed distinct groups of samples. Normal tissues were readily distinguished from tumor tissues, and tumors could he further subdivided into major groupings that correlated both to histological and clinical observations, as well as cell type-specific gene expression. A metric was devised to identify genes whose expression could be considered ideal for molecular determination of epithelial ovarian malignancies. The list of genes generated by this method was highly enriched for known markers of several epithelial malignancies, including ovarian cancer. This study demonstrates the rapidity with which large amounts of expression data can be generated. The results highlight important molecular features of human ovarian cancer and identify new genes as candidate molecular markers.
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页码:1176 / 1181
页数:6
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