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使用微阵列数据集的再分析构建ceRNA网络并识别与乳腺癌相关的枢纽差异表达基因:一个系统生物学方法

Lina Moallemi Rad¹, Arash Safarzadeh², Mohammad Taheri³

¹Department of Molecular and Cell Biology, Faculty of Basic Sciences, University of Mazandaran, Babolsar, Iran.

Pathology, research and practice

|October 7, 2023

概括

这项研究确定了乳腺癌中的竞争性内源RNA (ceRNA) 网络,揭示了关键的长非编码RNA (lncRNA),微RNA (miRNA) 和信使RNA (mRNA) 参与癌症的发展和进展.

科学领域:

在瘤学瘤学.
分子生物学分子生物学
生物信息学是一种生物信息学.

背景情况:

长非编码RNAs (lncRNAs),microRNAs (miRNAs) 和信使RNAs (mRNAs) 的相互作用在癌症病原发生过程中至关重要.
乳腺癌涉及复杂的分子相互作用,有助于其发展.

研究的目的:

开发一种in-silico方法,用于识别乳腺癌中的竞争性内源RNA (ceRNA) 网络.
发现乳腺癌治疗和诊断的潜在分子标和生物标志物.

主要方法:

利用in-silico策略分析差异表达基因 (DEGs),包括乳腺癌中的mRNA,lncRNA和miRNA.
进行了途径分析,以了解这些分子变化影响的生物过程.
基于已识别的相互作用构建了一个lncRNA/miRNA/mRNA ceRNA网络.

主要成果:

确定了1816个差异表达的mRNA,19个DElncRNA和27个DEmiRNA.
途径分析表明新陈代谢,细胞循环和信号通路的失调.
构建了一个包含2个lncRNA,15个mRNA和4个miRNA的核心ceRNA网络.

结论:

已识别的ceRNA网络为乳腺癌提供了潜在的治疗点.
网络组件可以作为乳腺癌诊断和预后的新生物标志物.

关键词:

生物标志物生物标志物乳腺癌乳腺癌乳腺癌这就是CeRNA.这是一个LncRNARNA.这就是米RNA.

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