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Brief Bioinform. 2020 Mar 23;21(2):368-394. doi: 10.1093/bib/bby120.
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Identification of gene signatures from RNA-seq data using Pareto-optimal cluster algorithm.使用帕累托最优聚类算法从RNA测序数据中识别基因特征。
BMC Syst Biol. 2018 Dec 21;12(Suppl 8):126. doi: 10.1186/s12918-018-0650-2.
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Identification of the copy number variant biomarkers for breast cancer subtypes.鉴定乳腺癌亚型的拷贝数变异生物标志物。
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MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction.MDHGI:用于 miRNA 疾病关联预测的矩阵分解和异质图推理。
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Predicting miRNA-disease association based on inductive matrix completion.基于归纳矩阵补全的 miRNA-疾病关联预测。
Bioinformatics. 2018 Dec 15;34(24):4256-4265. doi: 10.1093/bioinformatics/bty503.
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Analysis of the miRNA-mRNA-lncRNA network in human estrogen receptor-positive and estrogen receptor-negative breast cancer based on TCGA data.基于 TCGA 数据的人类雌激素受体阳性和雌激素受体阴性乳腺癌的 miRNA-mRNA-lncRNA 网络分析。
Gene. 2018 Jun 5;658:28-35. doi: 10.1016/j.gene.2018.03.011. Epub 2018 Mar 5.
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Systematical analysis of lncRNA-mRNA competing endogenous RNA network in breast cancer subtypes.系统分析乳腺癌亚型中的 lncRNA-mRNA 竞争内源性 RNA 网络。
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lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis.用于预测乳腺癌内在亚型和预后的长链非编码RNA基因特征
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Subtype identification from heterogeneous TCGA datasets on a genomic scale by multi-view clustering with enhanced consensus.通过具有增强一致性的多视图聚类,从基因组规模的异质TCGA数据集中进行亚型识别。
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LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction.LRSSLMDA:用于miRNA-疾病关联预测的拉普拉斯正则化稀疏子空间学习
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四种乳腺癌亚型中特定微小RNA-信使核糖核酸调控对的鉴定

Identification of specific microRNA-messenger RNA regulation pairs in four subtypes of breast cancer.

作者信息

Guo Ling, Zhang Aihua, Xiong Jie

机构信息

College of Electrical Engineering, Northwest University for Nationalities, Lanzhou, 730030, People's Republic of China.

College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, People's Republic of China.

出版信息

IET Syst Biol. 2020 Jun;14(3):120-126. doi: 10.1049/iet-syb.2019.0086.

DOI:10.1049/iet-syb.2019.0086
PMID:32406376
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8687302/
Abstract

Four subtypes of breast cancer, luminal A, luminal B, basal-like, human epidermal growth factor receptor-enriched, have been identified based on gene expression profiles of human tumours. The goal of this study is to find whether the same groups' genes would exhibit different networks among the four subtypes. Differential expressed genes between each of the four subtypes and the normal samples were identified. The overlaps between the four groups of differentially expressed genes were used to construct regulations networks for each of the four subtypes. Univariate and multivariate Cox regressions were employed to test the genes in the four regulation networks. This study demonstrated that the common genes in four subtypes showed different regulation. Also, the hsa-miR-182 and decorin pair performs different functions among the four subtypes of breast cancer. The result indicated that heterogeneity of breast cancer is not only reflected in the different expression patterns among different genes, but also in the different regulatory networks of the same group of genes.

摘要

基于人类肿瘤的基因表达谱,已鉴定出四种乳腺癌亚型,即腔面A型、腔面B型、基底样型、人表皮生长因子受体富集型。本研究的目的是探究同一组基因在这四种亚型中是否会呈现出不同的网络。确定了四种亚型中的每一种与正常样本之间的差异表达基因。利用四组差异表达基因之间的重叠部分构建了四种亚型各自的调控网络。采用单变量和多变量Cox回归对四个调控网络中的基因进行检验。本研究表明,四种亚型中的共同基因表现出不同的调控。此外,hsa-miR-182和核心蛋白聚糖对在四种乳腺癌亚型中发挥不同的功能。结果表明,乳腺癌的异质性不仅体现在不同基因之间不同的表达模式上,还体现在同一组基因不同的调控网络中。