Zhang Lin, Morris Jeffrey S, Zhang Jiexin, Orlowski Robert Z, Baladandayuthapani Veerabhadran
Postdoctoral fellow, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Professor, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Cancer Inform. 2014 Dec 7;13(Suppl 2):113-23. doi: 10.4137/CIN.S13787. eCollection 2014.
It is well-established that the development of a disease, especially cancer, is a complex process that results from the joint effects of multiple genes involved in various molecular signaling pathways. In this article, we propose methods to discover genes and molecular pathways significantly associated with clinical outcomes in cancer samples. We exploit the natural hierarchal structure of genes related to a given pathway as a group of interacting genes to conduct selection of both pathways and genes. We posit the problem in a hierarchical structured variable selection (HSVS) framework to analyze the corresponding gene expression data. HSVS methods conduct simultaneous variable selection at the pathway (group level) and the gene (within-group) level. To adapt to the overlapping group structure present in the pathway-gene hierarchy of the data, we developed an overlap-HSVS method that introduces latent partial effect variables that partition the marginal effect of the covariates and corresponding weights for a proportional shrinkage of the partial effects. Combining gene expression data with prior pathway information from the KEGG databases, we identified several gene-pathway combinations that are significantly associated with clinical outcomes of multiple myeloma. Biological discoveries support this relationship for the pathways and the corresponding genes we identified.
众所周知，疾病尤其是癌症的发展是一个复杂的过程，它是由参与各种分子信号通路的多个基因的联合作用导致的。在本文中，我们提出了一些方法来发现与癌症样本临床结果显著相关的基因和分子通路。我们利用与给定通路相关的基因的自然层次结构，将其作为一组相互作用的基因，来进行通路和基因的选择。我们将这个问题置于层次结构变量选择（HSVS）框架中，以分析相应的基因表达数据。HSVS方法在通路（组水平）和基因（组内）水平上同时进行变量选择。为了适应数据中通路-基因层次结构中存在的重叠组结构，我们开发了一种重叠HSVS方法，该方法引入了潜在的部分效应变量，这些变量划分了协变量的边际效应以及部分效应比例收缩的相应权重。将基因表达数据与来自KEGG数据库的先验通路信息相结合，我们确定了几个与多发性骨髓瘤临床结果显著相关的基因-通路组合。生物学发现支持了我们所确定的通路和相应基因之间的这种关系。
