使用加权共表达网络分析的miRNA-mRNA表达谱之间的模块化网络推断。

IF 1.5 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Integrative Bioinformatics Pub Date : 2021-11-22 DOI:10.1515/jib-2021-0029
Nisar Wani, Debmalya Barh, Khalid Raza
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引用次数: 2

摘要

连接转录和转录后调控网络解决了阐明基因调控机制的一个重要难题。为了破译这些连接的复杂性,我们为乳腺癌数据的mRNA和miRNA表达谱构建了共表达网络模块。我们使用加权基因共表达网络分析(WGCNA)方法构建基因和miRNA共表达模块,并建立这些模块(基因/miRNA)对癌症表型的意义。这项工作还从mRNA表达数据推断出绿松石模块基因之间的相互作用网络,从miRNA表达数据推断出绿松石模块的枢纽。利用miRsystem网络工具对miRNA枢纽及其部分靶点进行通路富集分析,揭示了它们在与癌症进展相关的几个重要通路中的富集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Modular network inference between miRNA-mRNA expression profiles using weighted co-expression network analysis.

Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.

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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
期刊最新文献
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