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Gene Expression Profiling of Microsatellite Unstable and Microsatellite Stable Endometrial Cancers Indicates Distinct Pathways of Aberrant Signaling

¹ Laboratory of Biosystems and Cancer, National Cancer Institute, Bethesda, Maryland; ² Walter Reed Army Medical Center, Washington, District of Columbia; and ³ Department of Obstetrics and Gynecology/Division of Gynecologic Oncology, Duke University, Durham, North Carolina

Requests for reprints: John I. Risinger, Laboratory of Biosystems and Cancer, NIH/National Cancer Institute, Room 5046, Building 37, 9000 Rockville Pike, MSC 4264, Bethesda, MD. Phone: 301-594-8503; E-mail: risingej{at}mail.nih.gov.

Microsatellite instability (MSI) is a molecular phenotype present in 25% of endometrial cancers. We examined the global gene expression profiles of early-stage endometrioid endometrial cancers with and without the MSI phenotype to test the hypothesis that MSI phenotype may determine a unique molecular signature among otherwise similar cancers. Unsupervised principal component analysis of the expression data from these cases indicated two distinct groupings of cancers based on MSI phenotype. A relatively small number of array features (392) at high statistical value (P < 0.001) were identified that drive the instability signature in these cancers; 109 of these transcripts differed by at least 2-fold. These data identify distinct gene expression profiles for MSI and microsatellite stable (MSS) cancers, which suggest that cancers with MSI develop in part by different mechanisms from their similar stable counterparts. In particular, we found evidence that two members of the secreted frizzled related protein family (SFRP1 and SFRP4) were more frequently down-regulated in MSI cancers as compared with MSS cancers. Down-regulation was accompanied by promoter hypermethylation for SFRP1. SFRP1 was hypermethylated in 8 of 12 MSI cancers whereas only 3 of 16 MSS cancers were methylated. The WNT target fibroblast growth factor 18 was found to be up-regulated in MSI cancers. These data classify histologically similar endometrioid endometrial cancers into two distinct groupings with implications affecting therapy and prevention.

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