Inferring RBP-Mediated Regulation in Lung Squamous Cell Carcinoma

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation of mRNAs. Dysregulations in RBP-mediated mechanisms have been found to be associated with many steps of cancer initiation and progression. Despite this, previous studies of gene expression in cancer have ignored the effect of RBPs. To this end, we developed a lasso regression model that predicts gene expression in cancer by incorporating RBP-mediated regulation as well as the effects of other well-studied factors such as copy-number variation, DNA methylation, TFs and miRNAs. As a case study, we applied our model to Lung squamous cell carcinoma (LUSC) data as we found that there are several RBPs differentially expressed in LUSC. Including RBP-mediated regulatory effects in addition to the other features significantly increased the Spearman rank correlation between predicted and measured expression of held-out genes. Using a feature selection procedure that accounts for the adaptive search employed by lasso regularization, we identified the candidate regulators in LUSC. Remarkably, several of these candidate regulators are RBPs. Furthermore, majority of the candidate regulators have been previously found to be associated with lung cancer. To investigate the mechanisms that are controlled by these regulators, we predicted their target gene sets based on our model. We validated the target gene sets by comparing against experimentally verified targets. Our results suggest that the future studies of gene expression in cancer must consider the effect of RBP-mediated regulation.RNA结合蛋白（RNA-binding proteins, RBPs）在信使RNA（messenger RNA, mRNA）的转录后调控过程中发挥关键作用。现有研究表明，RBP介导的调控机制失调与癌症发生与进展的多个环节密切相关。尽管如此，既往癌症基因表达相关研究往往忽视了RBPs的调控效应。为此，我们构建了套索回归（lasso regression）模型，通过整合RBP介导的调控作用，以及拷贝数变异（copy-number variation）、DNA甲基化（DNA methylation）、转录因子（transcription factors, TFs）和微小RNA（microRNAs, miRNAs）等已被广泛研究的调控因子的效应，实现癌症基因表达的预测。作为案例研究，我们将该模型应用于肺鳞状细胞癌（Lung squamous cell carcinoma, LUSC）数据集，原因在于我们发现LUSC组织中存在多种差异表达的RBPs。相较于仅纳入其他特征，加入RBP介导的调控效应后，留验基因的预测表达量与实测表达量之间的斯皮尔曼等级相关系数（Spearman rank correlation）显著提升。通过采用适配套索正则化自适应搜索机制的特征选择流程，我们鉴定出了LUSC中的候选调控因子。值得注意的是，其中诸多候选调控因子均为RBPs。此外，大部分候选调控因子此前已被证实与肺癌存在关联。为探究这些调控因子所介导的调控机制，我们基于本模型预测了它们的靶基因集，并通过与实验验证的靶标进行比对，完成了靶基因集的有效性验证。本研究结果提示，未来癌症基因表达领域的相关研究必须纳入RBP介导的调控效应。