Identification of breast cancer candidate genes using gene co-expression and protein-protein interaction information

Breast cancer (BC) is one of the most common malignancies that could threaten female health. As the molecular mechanism of BC has not yet been completely discovered, identification of related genes of this disease is an important area of research that could provide new insights into gene function as well as potential treatment targets. Here we used subnetwork extraction algorithms to identify novel BC related genes based on the known BC genes (seed genes), gene co-expression profiles and protein-protein interaction network. We computationally predicted seven key genes (EPHX2, GHRH, PPYR1, ALPP, KNG1, GSK3A and TRIT1) as putative genes of BC. Further analysis shows that six of these have been reported as breast cancer associated genes, and one (PPYR1) as cancer associated gene. Lastly, we developed an expression signature using these seven key genes which significantly stratified 1660 BC patients according to relapse free survival (hazard ratio [HR], 0.55; 95% confidence interval [CI], 0.46–0.65; Logrank p = 5.5e−13). The 7-genes signature could be established as a useful predictor of disease prognosis in BC patients. Overall, the identified seven genes might be useful prognostic and predictive molecular markers to predict the clinical outcome of BC patients.