Knockdown of breast cancer master regulators: siRNA targeting PTTG1 and SPDEF in MCF-7 cells.

Genome-wide association studies for breast cancer have identified over 80 different risk regions in the genome, with the FGFR2 locus consistently identified as the most strongly associated locus. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Here we use a systems biology approach to elucidate the regulatory networks operating in breast cancer and examine the role of FGFR2 in mediating risk. Using model systems we identify FGFR2-regulated genes and, combining variant set enrichment and eQTL analysis, show that these are preferentially linked to breast cancer risk loci. Our results support the concept that cancer-risk associated genes cluster in pathways The data consists of 18 microarray samples after knocking down PTTG1 and SPDEF in MCF-7 cells. The data have been pre-processed in R using the Beadarray package, and are presented in the form of log2 expression values. The experiment was carried out on Humanv4 BeadChips arrays interrogating 48107 randomly-distributed bead-types, and in this experiment there was a mean of 22 beads per bead-type (Standard Deviation of 5)

全基因组关联研究（Genome-wide association studies）已在乳腺癌基因组中鉴定出超过80个不同的风险区域，其中FGFR2基因座始终被鉴定为关联性最强的基因座。然而，目前我们对FGFR2基因座介导疾病风险的具体机制，以及多个风险基因座协同致病的通路仍知之甚少。本研究采用系统生物学方法，解析乳腺癌中发挥功能的调控网络，并探究FGFR2在介导疾病风险中的作用。通过模型实验系统，我们鉴定出FGFR2调控的靶基因，并结合变异集富集分析与表达数量性状位点（expression quantitative trait locus, eQTL）分析，证实这些靶基因优先与乳腺癌风险基因座存在关联。本研究结果支持癌症风险相关基因在通路中聚集这一学术观点。本数据集包含在MCF-7细胞中敲低PTTG1与SPDEF基因后获得的18份微阵列样本。该数据已在R语言环境中通过Beadarray软件包完成预处理，以log2表达值的形式呈现。本次实验采用Humanv4 BeadChips芯片，可检测48107个随机分布的微球类型，且每个微球类型的平均微球数为22（标准差为5）。