Dong Yu, Xiao Yang, Shi Qihui, Jiang Chunjie
Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States.
Front Genet. 2020 Jan 15;10:1284. doi: 10.3389/fgene.2019.01284. eCollection 2019.
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 ~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.
乳腺癌是女性中最常见的癌症，但临床上有效的生物标志物却很少。先前的研究表明，非编码RNA在乳腺癌的诊断、预后及治疗选择中发挥着重要作用，并提示整合不同水平的分子以阐释乳腺癌发生机制的重要性。在此，我们从癌症基因组图谱（TCGA）项目中收集了约1200个样本的转录组数据，包括长链非编码RNA（lncRNA）、微小RNA（miRNA）和信使RNA（mRNA），其中有1079个浸润性乳腺癌样本和104个正常样本。我们鉴定出了区分浸润性癌样本与正常样本的差异表达lncRNA、miRNA和mRNA。我们进一步构建了一个由差异表达的lncRNA、miRNA和mRNA组成的整合失调网络，并发现了管家功能和癌症相关功能。此外，我们在失调网络中发现了58个参与维持细胞存活所必需生物学过程的RNA结合蛋白（RBP），其中10个与总生存期相关。另外，我们鉴定出了两个将患者分为高风险和低风险亚组的模块。这两个模块的表达模式在浸润性癌样本与正常样本中存在显著差异，且一些分子是乳腺癌的高可信度生物标志物。总之，这些数据证明了在改善浸润性乳腺癌预后预测方面的重要临床应用价值。
