To address the role of cancer-stroma interactions, we performed gene expression profiling of both cancer and stroma, using matching samples of endometrial cancer (EC), and analyzed the relationship between the gene expression pattern and prognosis in EC. Sixty EC cases were included in this study (38 nonrecurrent and 22 recurrent). Cancer and stroma were separated by performing laser capture microdissection, and microarray analysis was performed separately on cancer and stromal cells. Genes related with progression-free survival (PFS) in cancer and stroma were analyzed using the Cox regression model, and we established a formula, based on the gene expression pattern of cancer and stroma, to predict recurrence using logistic regression. We estimated the accuracy of the formula using the 0.632 method. All cases were classified based on the 79 selected genes of cancer and stroma related to PFS, based on unsupervised clustering. A total of 143 genes in cancer, and 79 genes in stroma were significantly related with PFS. The estimated area under the curve of receiver operating characteristics curve in cancer and stroma to predict recurrence were 0.800 and 0.758, respectively. Based on the 79 genes of cancer, the 22 recurrent cases were divided into two groups, which generally correlated with the histological grade. In contrast, based on the 79 genes of stroma, the 22 recurrent cases displayed homogeneous gene expression, unrelated to the histological grade. We conclude that gene expression profiles of cancer and stroma can predict the recurrence of EC and stromal that gene expression does not depend on the cancer grade.
ASJC Scopus subject areas