Breast cancer is a highly heterogeneous disease and the most common incidence affecting women both in Ireland and worldwide. Amidst the multiple variants, basal-like breast cancer (BLBC) is a highly aggressive form with limited targeted treatment options owing to the lack of hormonal receptors and displays poor prognosis. Intrinsic gene expression profiling of BLBC samples have identified the existence of additional variants and the absence of detectable protein receptors pose a bottleneck scenario in early detection that is yet to be overcome. The integration of transcriptomic profiling is a robust approach for characterizing BLBC and for predicting the outcomes. MicroRNAs (miRNAs) are small non-coding RNA molecules that are endogenously expressed and play a key role in the regulation of gene expression at the post-transcriptional level, thereby leading to variations in the expression levels of proteins. Through this study, our aim is to explore the miRNA expression values in BLBC tumor core biopsies, non-invasive liquid biopsy samples (plasma/blood) and expand the molecular characterization of BLBCs, stratifying the samples to subgroups. Towards this, high-throughput miRNA expression data of basal/triple negative breast tumor subtype obtained from the Swedish trial study SCAN-B initiative will be compared against the miRNA expression profiles from The Cancer Genome Atlas (TCGA). The selection of prognostic miRNAs will be carried out through an integrated bioinformatics approach. Screening for the expression of significant miRNAs will be undertaken in plasma. The initial sample processing phase will be standardized to determine the ideal sample source for the extraction and screening of miRNAs and to improve the efficacy of utilization of miRNAs for the detection of BLBC subtypes. Significantly expressed miRNAs that differentiates BLBC from other tumor subtypes and display a good performance in predicting poor prognosis will be selected for future functional enrichment validation through in vitro experiments.
