Dysregulated lncRNA-miRNA-mRNA Network Reveals Patient Survival-Associated Modules and RNA Binding Proteins in Invasive Breast Carcinoma
Dysregulated lncRNA-miRNA-mRNA Network Reveals Patient Survival-Associated Modules and RNA Binding Proteins in Invasive Breast Carcinoma
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DOI：
10.3389/fgene.2019.01284
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发表时间：
2020-01-15
影响因子：
3.7
通讯作者：
Jiang, Chunjie
中科院分区：
文献类型：
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作者：
Dong, Yu;Xiao, Yang;Jiang, Chunjie
乳腺癌是女性最常见的恶性肿瘤，但临床上有效的生物标志物很少。以往的研究表明非编码RNA在乳腺癌的诊断、预后和治疗选择中起着重要作用，并提出了在不同水平整合分子以解释乳腺癌机制的意义。在这里，我们收集了转录组数据，包括长非编码RNA（lncRNA），microRNA（miRNA）和mRNA，来自癌症基因组图谱（TCGA）项目的1，200个样本，包括1079个浸润性乳腺癌样本和104个正常样本。我们鉴定了差异表达的lncRNAs、miRNAs和区分浸润性癌样本与正常样本的mRNAs。我们进一步构建了由差异表达的lncRNA、miRNA和mRNA组成的整合失调网络，并发现了管家和癌症相关功能。此外，在失调网络中发现了58种参与维持细胞存活所必需的生物过程的RNA结合蛋白（RBP），其中10种与总存活率相关。此外，我们确定了两个模块，将患者分为高风险和低风险亚组。这两个模块的表达模式在浸润性癌与正常样品中有显著差异，并且一些分子是乳腺癌的高置信度生物标志物。总之，这些数据证明了改善浸润性乳腺癌预后预测的重要临床应用。
Breast cancer is the most common cancer in women, but few biomarkers are effective in clinic. Previous studies have shown the important roles of non-coding RNAs in diagnosis, prognosis, and therapy selection for breast cancer and have suggested the significance of integrating molecules at different levels to interpret the mechanism of breast cancer. Here, we collected transcriptome data including long non-coding RNA (lncRNA), microRNA (miRNA), and mRNA for similar to 1,200 samples, including 1079 invasive breast carcinoma samples and 104 normal samples, from The Cancer Genome Atlas (TCGA) project. We identified differentially expressed lncRNAs, miRNAs, and mRNAs that distinguished invasive carcinoma samples from normal samples. We further constructed an integrated dysregulated network consisting of differentially expressed lncRNAs, miRNAs, and mRNAs and found housekeeping and cancer-related functions. Moreover, 58 RNA binding proteins (RBPs) involved in biological processes that are essential to maintain cell survival were found in the dysregulated network, and 10 were correlated with overall survival. In addition, we identified two modules that stratify patients into high- and low-risk subgroups. The expression patterns of these two modules were significantly different in invasive carcinoma versus normal samples, and some molecules were high-confidence biomarkers of breast cancer. Together, these data demonstrated an important clinical application for improving outcome prediction for invasive breast cancers.