Abstract
Molecularly targeted therapies significantly contribute to the efforts of personalized approaches for cancer diagnosis and chemotherapeutic treatment. One of a critical step to identify target molecules is to determine the most representative features for different patient's sub-groups. Breast cancer, one of the most heterogeneous cancer has five main subtypes, so accurately identify gene signatures associated with intrinsic subtypes based on currently available data resource will benefit for precision medicine. Traditional ways to identify subtype-specific targeted molecular biomarkers are based on the platforms such as microarray, or immunohistochemistry (IHC) markers. Very few studies using RNAseq data to predict cancer subtype, due to the limited data resource. Gene expression in RNAseq platform is highly correlated with the microarray. However, it shows superior benefits than microarray in many ways, such as detection of low abundance transcripts, the broader dynamic range of gene expression in both coding and non-coding genes. Therefore, RNAseq platform will become the predominant tool for transcriptome analysis in the long term. To benefit the identification of molecule targets for transcriptomic data including RNAseq and microarray expression, we developed a new feature selection method. Our results of classification accuracy for intrinsic subtypes and prognostic evaluation of selected genes outperform than the traditional PAM50 gene signature, which is derived from microarray and widely used in clinical practice.