Differential co-expression network analysis with DCoNA reveals isomiR targeting aberrations in prostate cancer

Abstract

We developed DCoNA - a statistical tool that allows one to identify pair interactions, which correlation significantly changes between two conditions. Comparing DCoNA with the state-of-the-art analog, we showed that DCoNA is a faster, more accurate, and less memory-consuming tool. We applied DCoNA to prostate mRNA/miRNA-seq data collected from The Cancer Genome Atlas (TCGA) and compared predicted regulatory interactions of miRNA isoforms (isomiRs) and their target mRNAs between normal and cancer samples. As a result, almost all highly expressed isomiRs lost negative correlation with their targets in prostate cancer samples compared to ones without the pathology. One exception to this trend was the canonical isomiR of hsa-miR-93-5p acquiring cancerspecific targets. Further analysis showed that cancer aggresiveness increased with the expression of this isomiR in both TCGA primary tumor samples and 153 blood plasma samples of own patients cohort analyzed by miRNA microarrays.