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Background  Gene expression profiling of human breast tumors has uncovered several molecular signatures that can divide breast cancer patients into good and poor outcome groups. However, these signatures typically comprise many genes (~50-100), and the prognostic tests associated with identifying these signatures in patient tumor specimens require complicated methods, which are not routinely available in most hospital pathology laboratories, thus limiting their use. Hence, there is a need for more practical methods to predict patient survival.    Methods  We modified a feature selection algorithm and used survival analysis to derive a 2-gene signature that accurately predicts breast cancer patient survival.    Results  We developed a tree based decision method that segregated patients into various risk groups using  KIAA0191  expression in the context of  E2F1  expression levels. This approach led to highly accurate survival predictions in a large cohort of breast cancer patients using only a 2-gene signature.    Conclusions  Our observations suggest a possible relationship between  E2F1  and  KIAA0191  expression that is relevant to the pathogenesis of breast cancer. Furthermore, our findings raise the prospect that the practicality of patient prognosis methods may be improved by reducing the number of genes required for analysis. Indeed, our  E2F1/KIAA0191  2-gene signature would be highly amenable for an immunohistochemistry based test, which is commonly used in hospital laboratories.

E2F1 and KIAA0191 expression predicts breast cancer patient survival