Bazyari Mohammad Javad, Aghaee-Bakhtiari Seyed Hamid
Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, 37552 Mashhad University of Medical Sciences , Mashhad, Iran.
Bioinformatics Research Center,Basic Sciences Research Institute, 37552 Mashhad University of Medical Sciences , Mashhad, Iran.
J Integr Bioinform. 2024 Dec 25;21(4). doi: 10.1515/jib-2022-0036. eCollection 2024 Dec 1.
Breast cancer has the highest incidence and is the fifth cause of death in cancers. Progression is one of the important features of breast cancer which makes it a life-threatening cancer. MicroRNAs are small RNA molecules that have pivotal roles in the regulation of gene expression and they control different properties in breast cancer such as progression. Recently, systems biology offers novel approaches to study complicated biological systems like miRNAs to find their regulatory roles. One of these approaches is analysis of weighted co-expression network in which genes with similar expression patterns are considered as a single module. Because the genes in one module have similar expression, it is rational to think the same regulatory elements such as miRNAs control their expression. Herein, we use WGCNA to find important modules related to breast cancer progression and use hypergeometric test to perform miRNA target enrichment analysis and find important miRNAs. Also, we use negative correlation between miRNA expression and modules as the second filter to ensure choosing the right candidate miRNAs regarding to important modules. We found hsa-mir-23b, hsa-let-7b and hsa-mir-30a are important miRNAs in breast cancer and also investigated their roles in breast cancer progression.
乳腺癌发病率最高,是癌症死亡的第五大原因。进展是乳腺癌的重要特征之一,这使其成为一种危及生命的癌症。微小RNA是在基因表达调控中起关键作用的小RNA分子,它们控制着乳腺癌的不同特性,如进展。最近,系统生物学提供了新的方法来研究像微小RNA这样复杂的生物系统,以发现它们的调控作用。其中一种方法是加权共表达网络分析,在该分析中,具有相似表达模式的基因被视为一个单一模块。由于一个模块中的基因具有相似的表达,因此有理由认为相同的调控元件(如微小RNA)控制它们的表达。在此,我们使用加权基因共表达网络分析来找到与乳腺癌进展相关的重要模块,并使用超几何检验进行微小RNA靶标富集分析,以找到重要的微小RNA。此外,我们将微小RNA表达与模块之间的负相关作为第二个筛选标准,以确保针对重要模块选择合适的候选微小RNA。我们发现hsa - mir - 23b、hsa - let - 7b和hsa - mir - 30a是乳腺癌中的重要微小RNA,并研究了它们在乳腺癌进展中的作用。