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Genetic classification of ovarian carcinoma based on microsatellite analysis: relationship to clinicopathological
Zhang Huan1, Kentaro Nakayama, Naomi Nakayama
1Department of Obstetrics and Gynecology, Shimane University School of Medicine, Izumo 693-8501, P.R. China.
Abstract:
Ovarian carcinomas can progress through two pathways of genomic instability: chromosomal instability (CIN) and microsatellite instability (MSI). However, it is unknown whether these two mechanisms could be distinguished from each other in the molecular characteristics in ovarian carcinomas. We hypothesized that these two pathways are not always independent in ovarian carcinomas. We classified 51 ovarian carcinomas based on their MSI and CIN status using microsatellite analysis and assessed whether these carcinogenic pathways affect the clinicopathological features and patient survival. Of the 51 cases, 77.4% of the tumors were microsatellite stable (MSS), 5.9% were MSI-Low (MSI-L) whilst, 16.7% were MSI-High (MSI-H). Overall, 56.8% of the tumors had at least one loss of heterozygosity (LOH) event, i.e., 56.8% CIN. Notably, we identified a significant degree of overlap between the MSI and CIN pathways. Of the 34 tumors with LOH events (CIN), 5 (14.7%) were MSI-H. In addition, of the 7 tumors that were MSI-H, 5 (71.4%) had one or more LOH events (CIN). We also identified a group of 29.4% of all tumors that did not demonstrate any evidence of either of the two pathways of genomic instability as they were MSS/MSI-L with no evidence of LOH events (CIN negative). Furthermore, patients with CIN with MSS/MSI-L have a significantly shorter overall survival compared to those in other genetic categories (P=0.019). Cox regression analysis revealed that tumors with CIN with MSS/MSI-L exhibit a poor prognostic outcome after adjustment for FIGO stage and grade. These findings suggest that some ovarian carcinomas have a significant degree of overlap between the two pathways of genomic instability and that the genetic classification using microsatellite markers may represent a potential new biomarker of risk prediction in ovarian carcinoma.
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