前列腺癌
小RNA
计算生物学
DNA微阵列
生物
鉴别诊断
癌症
肿瘤科
基因表达
病理
医学
遗传学
基因
作者
Anton Zhiyanov,Narek Engibaryan,Stepan Nersisyan,Maxim Shkurnikov,Alexander Tonevitsky
标识
DOI:10.1093/bioinformatics/btad051
摘要
One of the standard methods of high-throughput RNA sequencing analysis is differential expression. However, it does not detect changes in molecular regulation. In contrast to the standard differential expression analysis, differential co-expression one aims to detect pairs or clusters whose mutual expression changes between two conditions.We developed DCoNA (Differential Co-expression Network Analysis) - an open-source 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 cancer-specific targets. Further analysis showed that cancer aggressiveness simultaneously increased with the expression level of this isomiR in both TCGA primary tumor samples and 153 blood plasma samples of P. Hertsen Moscow Oncology Research Institute patients' cohort analyzed by miRNA microarrays.Source code and documentation of DCoNA are available at https://github.com/zhiyanov/DCoNA.Supplementary data are available at Bioinformatics online.
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