Integrated analysis reveals microRNA networks coordinately expressed with key proteins in breast cancer [miRNA]. Homo sapiens

BACKGROUND: The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets. METHODS: Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs. RESULTS: An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line. CONCLUSIONS: A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer. Overall design: The miRNA expression profiling of 283 breast cancer samples was performed using the 8x15k “Human miRNA Microarray Kit release 14.0 (V2)” with design id 029297 from Agilent (Agilent Technologies, Santa Clara, CA, USA). In brief, 100 ng total RNA was dephosphorylated, labeled and hybridized for 20 hours, following the manufacturer’s protocol. Scanning was performed on Agilent Scanner G2565A, signals were extracted using Feature Extraction v10.7.3.1 and the subsequent data processing was performed using the GeneSpring software v11.0 (Agilent Technologies). In brief, the miRNA signal intensities were log2-transformed and normalized to the 90th percentile, and miRNAs that were detected in less than 10% of the samples were excluded. This resulted in 421 unique mature miRNAs.

【背景】微小RNA（microRNA）在乳腺癌蛋白质表达失调中所发挥的作用，目前仅得到部分阐明。为深入解析该调控机制，本研究针对3个独立数据集，探究了微小RNA与信使RNA（mRNA）的表达对蛋白质表达的联合调控效应。
【方法】本研究将蛋白质表达建模为信使RNA与微小RNA表达量的幂次多元线性函数。首先将该模型应用于283例乳腺肿瘤的全基因组微小RNA表达谱，以及105个筛选获得的癌症相关基因的mRNA与蛋白质表达数据。模型同时考量了单一个微小RNA以及全部微小RNA的联合调控效应：在后一种场景下，采用Lasso惩罚回归（Lasso penalized regression）方法以检测多个微小RNA的协同调控作用。
【结果】本研究构建了乳腺癌相互作用组图谱，全面呈现微小RNA与蛋白质表达间的所有直接与间接关联。分析结果显示，乳腺癌中微小RNA与蛋白质表达存在广泛协同调控模式，多个微小RNA簇与多个蛋白质簇之间存在显著关联。上述结果随后在2个独立的乳腺癌数据集中得到验证，且部分微小RNA-蛋白质关联在乳腺癌细胞系中完成了功能验证。
【结论】在剔除mRNA表达的顺式作用（in-cis effect）效应后，本研究构建了乳腺癌中微小RNA与105个已知癌症相关蛋白质的共表达全景图谱。分析表明，多个微小RNA协同调控蛋白质表达失调，是乳腺癌中常见的作用模式。
【整体实验设计】本研究采用安捷伦（Agilent Technologies，美国加利福尼亚州圣克拉拉市）编号为029297的8×15k版"Human miRNA Microarray Kit release 14.0 (V2)"微阵列试剂盒，对283例乳腺癌样本进行miRNA表达谱分析。具体流程严格遵循厂商说明书：取100 ng总RNA进行去磷酸化、荧光标记，随后杂交20小时。扫描使用安捷伦G2565A扫描仪完成，信号提取采用Feature Extraction v10.7.3.1软件，后续数据处理使用GeneSpring v11.0软件（安捷伦科技）完成。简要处理步骤如下：将miRNA信号强度进行log2转换，并以90百分位数进行标准化；剔除在少于10%的样本中被检测到的miRNA，最终得到421个独特的成熟miRNA。