Faculty of Biology and Biotechnology, HSE University, Moscow 101000, Russia.
Institute of Molecular Biology, The National Academy of Sciences of the Republic of Armenia, Yerevan 0014, Armenia.
Bioinformatics. 2023 Feb 3;39(2). doi: 10.1093/bioinformatics/btad051.
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 Differential Co-expression Network Analysis (DCoNA)-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.
高通量 RNA 测序分析的标准方法之一是差异表达。然而，它不能检测到分子调节的变化。与标准的差异表达分析相反，差异共表达旨在检测在两种条件之间相互表达变化的对或簇。
我们开发了差异共表达网络分析（DCoNA）——一种开源统计工具，允许识别对相互作用，其相关性在两种条件之间显著变化。将 DCoNA 与最先进的类似工具进行比较，我们表明 DCoNA 是一种更快、更准确、内存消耗更少的工具。我们将 DCoNA 应用于从癌症基因组图谱（TCGA）收集的前列腺 mRNA/miRNA-seq 数据，并比较了 miRNA 同工型（isomiRs）及其靶 mRNA 在正常和癌症样本之间的预测调节相互作用。结果表明，与没有病理的样本相比，在前列腺癌样本中，几乎所有高表达的 isomiR 与其靶基因的负相关都丧失了。这种趋势的一个例外是 hsa-miR-93-5p 的典型 isomiR 获得了癌症特异性靶基因。进一步的分析表明，这种 isomiR 的表达水平在 TCGA 原发性肿瘤样本和 153 个来自莫斯科肿瘤研究所患者队列的血浆样本中的癌症侵袭性同时增加，这些样本通过 miRNA 微阵列进行了分析。
DCoNA 的源代码和文档可在 https://github.com/zhiyanov/DCoNA 上获得。
补充数据可在生物信息学在线获得。
