Hybrid method for prediction of metastasis in breast cancer patients using gene expression signals

Abstract

Background: Using primary tumor gene expression has been shown to have the ability of finding metastasis-driving gene markers for the prediction of breast cancer recurrence (BCR). However, there are some difficulties associated with the analysis of microarray data which led to poor predictive power and inconsistency of the previously introduced gene signatures.

Methods: In this study a hybrid method was proposed for identifying more predictive gene signatures from microarray datasets. Initially, the parameters of a Rough-Set (RS) theory based feature selection method were tuned to construct a customized gene extraction algorithm. Afterward, using the RS gene selection method the most informative genes were selected from six independent breast cancer datasets. Then, the combined set of these six signature sets, containing 114 genes, was evaluated for the prediction of BCR. Finally, a meta-signature, containing 18 genes, was selected from the combination of datasets and its prediction accuracy was compared with the combined signature.

Results: The results of 10-fold cross validation test, showed acceptable misclassification error rate (MCR) over 1338 cases of breast cancer patients. In comparison with a recent similar work our approach reached more than 5% reduction in MCR using a fewer number of genes for the prediction. The results also demonstrated 7% improvement in the average accuracy in six utilized datasets, using the combined set of 114 genes in comparison with the 18-genes meta-signature.

Conclusions: In this study, a more informative gene signature was selected for the prediction of BCR using a RS based gene extraction algorithm. To conclude, combining different signatures demonstrated more stable prediction over the independent datasets.