Computational method for prediction of targets for breast cancer using siRNA approach

Abstract

The increasing incident of breast cancer, which is a leading cause of women’s death in both developed and developing countries, demands the development of novel and efficient therapies. One of the major challenges is to design drugs that can specifically target the genes or proteins responsible for breast cancer, as gene and chemotherapy both are suffering from the drug specificity issues. Several recent studies have highlighted the potential of RNA interference (RNAi)-mediated targeted silencing of breast oncogenes, which can be exploited to develop cancer cell-/target-specific therapeutic molecules. However, one of the bottlenecks of RNAi-based gene therapy is to identify the RNAi sequences for efficient and targeted suppression of oncogenes. In this chapter, we discuss the development and application of a web-based database, BOSS (http://bioinformatics.cimap.res.in/sharma/boss/index.php ), for selection of potential RNAi based on the sequences that have been used and validated for RNAi-mediated suppression of breast oncogenes. This database includes the latest information regarding used RNAi molecules that can be cost-effective and less time-consuming.