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卵巢癌的多种可变剪接标志物。

Multiple alternative splicing markers for ovarian cancer.

作者信息

Klinck Roscoe, Bramard Anne, Inkel Lyna, Dufresne-Martin Geneviève, Gervais-Bird Julien, Madden Richard, Paquet Eric R, Koh ChuShin, Venables Julian P, Prinos Panagiotis, Jilaveanu-Pelmus Manuela, Wellinger Raymund, Rancourt Claudine, Chabot Benoit, Abou Elela Sherif

机构信息

Laboratoire de génomique fonctionnelle de l'Université de Sherbrooke, Université de Sherbrooke, Sherbrooke, Québec, Canada.

出版信息

Cancer Res. 2008 Feb 1;68(3):657-63. doi: 10.1158/0008-5472.CAN-07-2580.

Abstract

Intense efforts are currently being directed toward profiling gene expression in the hope of developing better cancer markers and identifying potential drug targets. Here, we present a sensitive new approach for the identification of cancer signatures based on direct high-throughput reverse transcription-PCR validation of alternative splicing events. This layered and integrated system for splicing annotation (LISA) fills a gap between high-throughput microarray studies and high-sensitivity individual gene investigations, and was created to monitor the splicing of 600 cancer-associated genes in 25 normal and 21 serous ovarian cancer tissues. Out of >4,700 alternative splicing events screened, the LISA identified 48 events that were significantly associated with serous ovarian tumor tissues. In a further screen directed at 39 ovarian tissues containing cancer pathologies of various origins, our ovarian cancer splicing signature successfully distinguished all normal tissues from cancer. High-volume identification of cancer-associated splice forms by the LISA paves the way for the use of alternative splicing profiling to diagnose subtypes of cancer.

摘要

目前,人们正在全力以赴地对基因表达进行分析,以期开发出更好的癌症标志物并确定潜在的药物靶点。在此,我们提出了一种基于对可变剪接事件进行直接高通量逆转录-聚合酶链反应验证来识别癌症特征的灵敏新方法。这种分层整合的剪接注释系统(LISA)填补了高通量微阵列研究与高灵敏度单个基因研究之间的空白,旨在监测25个正常组织和21个浆液性卵巢癌组织中600个癌症相关基因的剪接情况。在筛选的4700多个可变剪接事件中,LISA识别出48个与浆液性卵巢肿瘤组织显著相关的事件。在对39个含有各种来源癌症病理的卵巢组织进行的进一步筛选中,我们的卵巢癌剪接特征成功地将所有正常组织与癌症区分开来。LISA对癌症相关剪接形式的大量识别为利用可变剪接分析来诊断癌症亚型铺平了道路。

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