The Effect of miRNAs to the Regulation of Triple Negative Breast Cancer - Tikka, Pauli (fi=Turun yliopisto|en=University of Turku|, 2014)
Triple negative breast cancer (TNBC) forms a specific subgroup of breast cancer. The roles of microRNAs (miRNAs) on the carcinogenesis of TNBC cells were studied with statistical and mathematical methods using the expression signals of messenger RNAs (mRNAs) and miRNAs. The target genes of miRNAs were found by analysing the most relevant target gene methods and internet databases. An enrichment analysis, with Fisher exact tests, was conducted to miRNAs that were significantly dysregulating their target genes in TNBC. These tests revealed 21 enriched miRNAs. A hierarchical clustering analysis was then performed to specific set of target gene pairs by using their enriched miRNA related Hamming distances with Ward’s method. The hypergeometric enrichment tests for these clusters yielded many biological processes, molecular functions, and pathways indicating that miRNAs had multitude of regulative tasks in TNBC. Clearly observable regulation between the target genes and miRNAs in TNBC cells was searched by mixed integer programming (MIP) modelling, which is an extended version of linear programming. MIP employs both real and integer variables when it minimizes the error arising from the comparison of the real expression signals of mRNAs to their miRNA related estimates. The gene of Ataxin 1 protein (ATXN1) resulted in the most reliable correlation (82%) in MIP using TNBC samples. Ataxin 1 is a substantial component of the notch signalling pathway regulating differentiation. MIP model also showed that the down-regulation of ATXN1 in TNBC is mostly due to hsa-miR-96-5p, and down-regulation of a gene of Leucine Zipper Protein 1 (LUZP1) by an enriched miRNA hsa-miR-29b-3p. MIP models and enrichment tests yielded compatible results compared to literature. In ensuing studies histone modifications, transcription factors, and the distance dependency of the target gene sites of miRNAs should be employed in the model. This could give a more proper description of the potency of some of the well correlated enriched miRNAs to work as biomarkers.