论文标题
使用贝叶斯方法在获得的拉帕替尼抗性中发现新型的癌症生物标记
Discovering novel cancer bio-markers in acquired lapatinib resistance using Bayesian methods
论文作者
论文摘要
基因/蛋白质在我们体内不单独工作，而是作为一组，他们执行的某些活动表示为途径。信号转导途径（STP）是从控制多种细胞活性的蛋白质到蛋白质传递生物学信号的一些重要途径。但是，许多癌症等疾病的生长和恶性肿瘤针对其中一些信号通路，但是对其潜在机制进行神秘化是一项非常复杂的任务。在这项研究中，我们使用一种完全贝叶斯的方法来开发方法论，在两条条件的高通量基因表达数据的情况下，在异常STP中发现新型驱动器生物标志物。该项目，即Pathturber（途径扰动驱动器），应用于源自Lapatinib（EGFR/她的双重抑制剂）敏感和抗性样品（SKBR3）的全球基因表达数据集（EGFR/她的双重抑制剂）。差异表达分析揭示了512个差异表达的基因（DEG）及其信号通路富集分析显示，有22个单呼吸途径，包括PI3K-AKT，HIPPO，HIPPO，趋化因子和TGF-Beta Singalling途径，在Lapatinib抗性中高度失调，而TGF-Beta Singalling途径高度失调。接下来，我们使用三种马尔可夫链蒙特卡洛（MCMC）抽样方法（即邻域采样器（NS）（NS）和命中率（HAR）采样，它已经证明了与其他优势相结合的，我们将在TGF-BETA STP中对TGF-Beta STP中的异常活动作为因果贝叶斯网络（BN）作为因果贝叶斯网络（BN）进行建模。最先进的方法。接下来，我们检查了最佳BN的结构特征作为一个统计过程，该过程使用$ P_1 $ -MODEL，一类特殊的指数随机图模型（ERGM）和MCMC方法生成全局结构，用于其超参数采样...。
Genes/Proteins do not work alone within our body, rather as a group they perform certain activities indicated as pathways. Signalling transduction pathways (STPs) are some of the important pathways that transmit biological signals from protein-to-protein controlling several cellular activities. However, many diseases such as cancer target some of these signalling pathways for their growth and malignance, but demystifying their underlying mechanisms are a very complicated tasks. In this study, we use a fully Bayesian approach to develop methodologies in discovering novel driver bio-markers in aberrant STPs given two-conditional high-throughput gene expression data. This project, namely PathTurbEr (Pathway Perturbation Driver), is applied on a global gene expression dataset derived from the lapatinib (an EGFR/HER dual inhibitor) sensitive and resistant samples from breast cancer cell lines (SKBR3). Differential expression analysis revealed 512 differentially expressed genes (DEGs) and their signalling pathway enrichment analysis revealed 22 singalling pathways as aberrated including PI3K-AKT, Hippo, Chemokine, and TGF-beta singalling pathway as highly dysregulated in lapatinib resistance. Next, we model the aberrant activities in TGF-beta STP as a causal Bayesian network (BN) from given observational datasets using three Markov Chain Monte Carlo (MCMC) sampling methods, i.e. Neighbourhood sampler (NS) and Hit-and-Run (HAR) sampler, which has already proven to have more robust inference with lower chances of getting stuck at local optima and faster convergence compared to other state-of-art methods. Next, we examined the structural features of the optimal BN as a statistical process that generates the global structure using, $p_1$-model, a special class of Exponential Random Graph Models (ERGMs) and MCMC methods for their hyper-parameter sampling....
