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血管生成 mRNA 和 microRNA 基因表达谱预测浆液性卵巢癌的一种新亚型。

Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer.

机构信息

Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.

出版信息

PLoS One. 2012;7(2):e30269. doi: 10.1371/journal.pone.0030269. Epub 2012 Feb 13.

Abstract

Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding "angiogenesis signature" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials.

摘要

卵巢癌是美国女性癌症死亡的第五大主要原因,也是全球第七大致命癌症。尽管卵巢癌最初对铂类治疗敏感,但绝大多数患者最终会复发癌症,并死于对铂类越来越耐药的疾病。现代靶向癌症药物干预细胞信号转导,确定关键疾病机制和途径将极大地提高我们的治疗能力。为了揭示卵巢癌的分子多样性,我们对 129 例晚期高级别浆液性卵巢癌进行了全面的转录谱分析。我们实施了一种基于重新采样的 ISIS 类发现算法(rISIS:稳健 ISIS),并将其应用于整个卵巢癌转录谱集。rISIS 确定了以前未描述的患者分层,进一步得到 micro-RNA 表达谱的支持,基因集富集分析为分层提供了强有力的生物学支持,涉及细胞外基质、细胞黏附和血管生成基因。相应的“血管生成特征”在十个已发表的独立卵巢癌基因表达数据集中得到验证,与总生存期显著相关。我们定义的亚型具有潜在的转化意义,因为它们可能与识别可能受益于正在临床试验中测试的抗血管生成治疗的患者有关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65ba/3278409/6288327872d6/pone.0030269.g001.jpg

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