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microRNA(miRNA)被广泛报道能参与乳腺癌的病理发生发展过程.但是,在这些病理过程中,miRNA通过哪些信号通路来参与这些过程还不甚清楚.例如,在乳腺癌的组织学分级分化过程中,人们对于miRNA如何与其靶标基因相互作用来调控乳腺癌的分化还知之甚少.本研究通过计算的方法研究miRNA在乳腺癌组织学分级分化过程中的作用.通过寻找miRNA靶标基因集合在基因芯片数据中乳腺癌Ⅰ级和Ⅲ级间显著差异表达,鉴定了15个候选miRNAs,其中9个关键的miRNAs通过调控差异表达的靶基因来参与6个重要的信号转导通路.在这些通路中,TGF-β信号通路中一个主要的抑制分子SMAD7蛋白被预测为上面几个关键miRNAs的靶标基因.SMAD7既能被miRNA直接调控又能被miRNA调控的信号通路调控,进而影响TGF-β信号通路在乳腺癌组织分化中的作用.因此,我们推测TGF-β信号通路作为一个核心通路在miRNA调控乳腺癌的组织分化过程中起重要作用.预测靶标基因在乳腺癌Ⅰ级和Ⅲ级的分类性能在另外3个独立的乳腺癌数据集上得到进一步验证.3个预测差异表达的关键miRNAs也通过实时荧光定量PCR在10个不同组织分级的乳腺癌病人样本中得到验证.
MicroRNAs (miRNAs) are widely reported to participate in the pathogenesis of breast cancer, but it is unclear which pathways miRNAs participate in during these histopathological processes.For example, in histological grading of breast cancer , We know little about how miRNAs interact with their target genes to regulate breast cancer differentiation.In this study, we studied the role of miRNAs in the histological grade differentiation of breast cancer by means of computational methods.Through looking for miRNA target genes in a set of genes Fifteen candidate miRNAs were identified as differentially expressed in grade I and grade III of breast cancer data, and nine of the key miRNAs involved in six important signal transduction pathways by regulating differentially expressed target genes. In these pathways , A major inhibitory molecule in the TGF-β signaling pathway, SMAD7, is predicted as a target gene for several of the above key miRNAs, which are both directly regulated by miRNAs and regulated by miRNA signaling pathways, thereby affecting the TGF-β signaling pathway In breast cancer tissue differentiation.Therefore, we speculate that TGF-β signaling pathway as a core pathway in miRNA regulation of milk Cancer tissue differentiation plays an important role in the prediction of the target gene in breast cancer grade Ⅰ and Ⅲ classification performance in the other three independent breast cancer data sets to further verify the 3 predicted differentially expressed key miRNAs also through real-time Quantitative real-time PCR was validated in 10 breast cancer patient samples with different tissue grading.