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整合基因集分析:在铂类药物基因组学中的应用

Integrative gene set analysis: application to platinum pharmacogenomics.

作者信息

Fridley Brooke L, Abo Ryan, Tan Xiang-Lin, Jenkins Gregory D, Batzler Anthony, Moyer Ann M, Biernacka Joanna M, Wang Liewei

机构信息

1 Department of Biostatistics, University of Kansas Medical Center , Kansas City, Kansas.

出版信息

OMICS. 2014 Jan;18(1):34-41. doi: 10.1089/omi.2013.0099. Epub 2013 Nov 7.

Abstract

Integrative genomics has the potential to uncover relevant loci, as clinical outcome and response to chemotherapies are most likely not due to a single gene (or data type) but rather a complex relationship involving genetic variation, mRNA, DNA methylation, and copy number variation. In addition to this complexity, many complex phenotypes are thought to be controlled by the interplay of multiple genes within the same molecular pathway or gene set (GS). To address these two challenges, we propose an integrative gene set analysis approach and apply this strategy to a cisplatin (CDDP) pharmacogenomics study involving lymphoblastoid cell lines for which genome-wide SNP and mRNA expression data was collected. Application of the integrative GS analysis implicated the role of the RNA binding and cytoskeletal part GSs. The genes LMNB1 and CENPF, within the cytoskeletal part GS, were functionally validated with siRNA knockdown experiments, where the knockdown of LMNB1 and CENPF resulted in CDDP resistance in multiple cancer cell lines. This study demonstrates the utility of an integrative GS analysis strategy for detecting novel genes associated with response to cancer therapies, moving closer to tailored therapy decisions for cancer patients.

摘要

整合基因组学有潜力揭示相关基因座,因为临床结果和对化疗的反应很可能并非由单个基因(或数据类型)导致,而是涉及遗传变异、mRNA、DNA甲基化和拷贝数变异的复杂关系。除了这种复杂性外,许多复杂表型被认为是由同一分子途径或基因集(GS)内多个基因的相互作用所控制。为应对这两个挑战,我们提出一种整合基因集分析方法,并将此策略应用于一项顺铂(CDDP)药物基因组学研究,该研究涉及收集了全基因组SNP和mRNA表达数据的淋巴母细胞系。整合基因集分析的应用表明了RNA结合和细胞骨架部分基因集的作用。细胞骨架部分基因集内的LMNB1和CENPF基因通过小干扰RNA(siRNA)敲低实验进行了功能验证,其中LMNB1和CENPF的敲低导致多种癌细胞系对CDDP产生抗性。这项研究证明了整合基因集分析策略在检测与癌症治疗反应相关的新基因方面的效用,朝着为癌症患者做出个性化治疗决策又迈进了一步。

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