Identification of novel miRNAs as diagnostic molecules for detection of breast cancer using in silico approaches

Abstract

Breast cancer (BC) is the most common cancer in women worldwide, and is the second most common cancer in the world, responsible for more than 500 000 deaths annually. Estimates are that 1 in 8 women will develop BC in their lifetime. In South Africa, BC in women affects about 16.6 % of the population and could see a 78 % increase in cases by 2030. The failure of conventional diagnostic tools to detect BC from an early onset has revealed the need for diagnostic tools that would enable early diagnosis of BC. The current diagnostic tools include breast self-examination, mammography magnetic resonance imaging, ultrasonography and serum biomarkers; BRACA1, BRACA2, HER2. These conventional methods lack sensitivity, specificity and positive predictive value, and some of these diagnostic tools may be expensive and quite invasive. Therefore, novel diagnostic tools such as microRNAs which address the short comings of current methods are required for early diagnosis as well as BC management. MicroRNAs are a class of non-coding RNA molecules, which are important in RNA stability and gene expression. Various methodologies have been employed to identify novel microRNAs for diagnostics such as bioinformatics, also referred to as in silico analysis. The aim of this study is to identify novel microRNAs that can potentially detect BC at its earliest stage.