Identification of distinct miRNA target regulation between breast cancer molecular subtypes using AGO2-PAR-CLIP and patient datasets

摘要

Background: Various micro RNAs (mi RNAs) are up- or downregulated in tumors. However, the repression of cognate mi RNA targets responsible for the phenotypic effects of this dysregulation in patients remains largely unexplored. To define mi RNA targets and associated pathways, together with their relationship to outcome in breast cancer, we integrated patient-paired mi RNA-m RNA expression data with a set of validated mi RNA targets and pathway inference. Results: To generate a biochemically-validated set of mi RNA-binding sites, we performed argonaute-2 photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (AGO2-PAR-CLIP) in MCF7 cells. We then defined putative mi RNA-target interactions using a computational model, which ranked and selected additional Target Scan-predicted interactions based on features of our AGO2-PAR-CLIP binding-site data. We subselected modeled interactions according to the abundance of their constituent mi RNA and m RNA transcripts in tumors, and we took advantage of the variability of mi RNA expression within molecular subtypes to detect mi RNA repression. Interestingly, our data suggest that mi RNA families control subtype-specific pathways; for example, mi R-17, mi R-19a, mi R-25, and mi R-200b show high mi RNA regulatory activity in the triple-negative, basal-like subtype, whereas mi R-22 and mi R-24 do so in the HER2 subtype. An independent dataset validated our findings for mi R-17 and mi R-25, and showed a correlation between the expression levels of mi R-182 targets and overall patient survival. Pathway analysis associated mi R-17, mi R-19a, and mi R-200b with leukocyte transendothelial migration. Conclusions: We combined PAR-CLIP data with patient expression data to predict regulatory mi RNAs, revealing poten- tial therapeutic targets and prognostic markers in breast cancer.