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用于识别受干扰通路的相对表达分析

Relative expression analysis for identifying perturbed pathways.

作者信息

Eddy James A, Geman Donald, Price Nathan D

机构信息

Department of Bioengineering, University of Illinois, Urbana, IL 61801 USA.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5456-9. doi: 10.1109/IEMBS.2009.5334063.

DOI:10.1109/IEMBS.2009.5334063
PMID:19964680
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2923586/
Abstract

The computational identification from global data sets of stable and predictive patterns of gene and protein relative expression reversals offers a simple, yet powerful approach to target therapies for personalized medicine and to identify pathways that are disease-perturbed. We previously utilized this approach to identify a molecular classifier with near 100% accuracy for differentiating gastrointestinal stromal tumor (GIST) and leiomyosarcoma (LMS), two cancers that have very similar histopathology, but require very different treatments. Differential Rank Conservation (DIRAC) is a novel approach for studying gene ordering within pathways and is based on the relative expression ranks of participating genes. DIRAC provides quantitative measures of how pathway rankings differ both within and between phenotypes. DIRAC between pathways in a selected phenotype contrasts the scenarios where either (i) pathways are ranked similarly in all samples; or (ii) the ordering of pathway genes is highly varied. We examined gene expression in GIST and LMS tumor profiles and identified pathways that appear to be tightly regulated based on high conservation of gene ordering. The second form of DIRAC manifests as a change in ranking (i.e., shuffling) between phenotypes for a selected pathway. These variably expressed pathways serve as signatures for molecular classification, and the ability to accurately classify microarray samples provided strong validation for the pathway-level expression differences identified by DIRAC.

摘要

从全局数据集中通过计算识别基因和蛋白质相对表达逆转的稳定且可预测模式,为个性化医学的靶向治疗以及识别受疾病干扰的通路提供了一种简单却强大的方法。我们之前利用这种方法识别出一种分子分类器,其区分胃肠道间质瘤(GIST)和平滑肌肉瘤(LMS)的准确率接近100%,这两种癌症具有非常相似的组织病理学特征,但需要截然不同的治疗方法。差异秩守恒(DIRAC)是一种研究通路内基因排序的新方法,它基于参与基因的相对表达秩。DIRAC提供了关于通路排序在表型内和表型间如何不同的定量测量。在选定表型中不同通路之间的DIRAC对比了以下两种情况:(i)通路在所有样本中排序相似;或(ii)通路基因的排序高度变化。我们检查了GIST和LMS肿瘤图谱中的基因表达,并基于基因排序的高度保守性识别出似乎受到严格调控的通路。DIRAC的第二种形式表现为选定通路在不同表型之间的排序变化(即洗牌)。这些可变表达的通路作为分子分类的特征,并且准确分类微阵列样本的能力为DIRAC识别出的通路水平表达差异提供了有力验证。

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