Publication | Open Access
Identification of a Gene Expression Signature for Survival Prediction in Type I Endometrial Carcinoma
21
Citations
19
References
2010
Year
PathologyEndometrial CarcinomaGene Expression ProfilingTumor BiologyGene Expression SignatureOncologyTumor HeterogeneityMolecular DiagnosticsRadiation OncologyCancer ResearchHealth SciencesMedicineCancer GeneticsGene ExpressionTumor MicroenvironmentAffected WomenCancer GenomicsEndometrial CancerSurvival Prediction
Endometrial cancer is the most common malignancy of the female reproductive tract. In many cases the prognosis is favorable, but 22% of affected women die from the disease. We aimed to study potential differences in gene expression between endometrioid adenocarcinomas from survivors (5-year survival) and nonsurvivors. Forty-five patients were included in the investigation, of which 21 were survivors and 24 were nonsurvivors. The tumors were analyzed with genome-wide expression array analysis, represented by 13,526 genes. Distinct differences in gene expression were found between the groups. A t-test established that 218 genes were significantly differentially expressed (p < 0.001) between the two survival groups, and in a cross-validation test 40 of the 45 (89%) tumors were classified correctly. The 218 differentially expressed genes were subjected to hierachical clustering analysis, which yielded two clusters both exhibiting over 80% homogeneity with respect to survival. When the additional constraint of fold change (FC > 2) was added the hierachical clustering yielded similar results. Stage I tumors are expected to have a favorable prognosis. However, in our tumor material there were six nonsurvivors with stage I tumors. Five out of six stage I nonsurvivors clustered in the nonsurvival fraction. Our findings suggest that a subgroup of early stage endometroid adenocarcinomas can be correctly classified as potentially aggressive by using molecular biology in combination with conventional markers, thereby providing a tool for a more accurate classification and risk evaluation of the individual patient.
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