Sun-Wei Guo, Yan Wu, Andre Kajdacsy-Balla, Estil Strawn, Zainab Basir, Gloria Halverson, Martin J. Hessner, Xujing Wang, Kami Montgomery
Dept. of Pediatrics, Dept. of Pathology, Dept. of Obstetrics and Gynecology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
Endometriosis, defined as the ectopic presence of endometrial glands and stroma, is a common gynecological disorder that affects women of reproductive age. Although it is perhaps one of the most investigated gynecological disorders, its pathogenesis remains to this day an enigma and is often the topic of heated debate. It has been well documented that there are substantial differences between the eutopic and ectopic endometrium. However, most studies focus only one or few genes or proteins, hence the picture on the similarity and difference between eutopic and ectopic endometrium is somewhat fragmentary. In this study, we analyzed transcriptional profiles of six sets of eutopic and ectopic endometrium from six patients with endometriosis using cDNA microarrays of 9600 genes. Validation of select genes was conducted by RT-PCR. After removing low-quality spots, 6130 genes were used for analysis. With adjustment for multiple comparisons, we identified 198 genes that are up-regulated and 174 genes that are down-regulated in the ectopic endometrium as compared with eutopic endometrium. These differentially expressed genes can be further grouped, by cluster analysis, into tumor supressor genes, signal transduction, apoptosis/inhibitors, immune modulators/receptors, extracellular matrix/cell adhesion molecules, and others. Cluster analysis also indicated that the use of these 372 genes classified the six patients into two groups, which incidentally happened to be ovarian endometriosis and peritoneal endometriosis. This profiling provides a much broader view on transcriptional difference between the eutopic and ectopic endometrium, and identified numerous genes and gene families not recognized previously. The genes identified in this study could be used to identify genetic markers for endometriosis and predict treatment outcome.
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