  BACKGROUND: Breast cancer has emerged as one of the most prevalent cancers
among women leading to a high mortality rate. Due to the heterogeneous nature
of breast cancer, there is a need to identify differentially expressed genes
associated with breast cancer subtypes for its timely diagnosis and treatment.
OBJECTIVE: To identify a small gene set for each of the four breast cancer
subtypes that could act as its signature, the paper proposes a novel algorithm
for gene signature identification. METHODS: The present work uses interpretable
AI methods to investigate the predictions made by the deep neural network
employed for subtype classification to identify biomarkers using the TCGA
breast cancer RNA Sequence data. RESULTS: The proposed algorithm led to the
discovery of a set of 43 differentially expressed gene signatures. We achieved
a competitive average 10-fold accuracy of 0.91, using neural network
classifier. Further, gene set analysis revealed several relevant pathways, such
as GRB7 events in ERBB2 and p53 signaling pathway. Using the Pearson
correlation matrix, we noted that the subtype-specific genes are correlated
within each subtype. CONCLUSIONS: The proposed technique enables us to find a
concise and clinically relevant gene signature set.
