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七种长链非编码RNA组成的panel作为卵巢癌的候选预后生物标志物

Panel of seven long noncoding RNA as a candidate prognostic biomarker for ovarian cancer.

作者信息

Zhan Xiaohui, Dong Chuanpeng, Liu Gang, Li Yixue, Liu Lei

机构信息

Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai.

University of Chinese Academy of Sciences, Beijing.

出版信息

Onco Targets Ther. 2017 Jun 1;10:2805-2813. doi: 10.2147/OTT.S128797. eCollection 2017.

Abstract

Ovarian cancer is one of the most common and lethal gynecological malignancies. The diagnosis of ovarian cancer is often at an advanced stage. Accumulated evidence suggests that long noncoding RNAs (lncRNAs) play important roles during ovarian tumorigenesis. In this study, using the lncRNA-mining approach, we analyzed lncRNA expression profiles of 493 ovarian cancer patients from Gene Expression Omnibus datasets, and identified a signature group of seven lncRNAs (BC037530, AK021924, AK094536, AK094536, BC062365, BC004123 and BC007937) associated with patient survival in the training dataset GSE9891. We also formulated a risk score model to divide patients into low-risk and high-risk groups based on the expression of these seven lncRNAs. We further validated the predictive power of our risk score model in two other datasets, GSE26193 and GSE63885. Our analysis showed that the seven-lncRNA signature can serve as an independent predictor apart from Federation of Gynecology and Obstetrics (FIGO) stage and patient age. Further investigation revealed the seven-lncRNA signature correlated with few critical signaling pathways involved in cancer. Combined, all these findings strongly support that the seven-lncRNA signature can serve as a strong prognosis biomarker.

摘要

卵巢癌是最常见且致命的妇科恶性肿瘤之一。卵巢癌的诊断通常处于晚期。越来越多的证据表明,长链非编码RNA(lncRNA)在卵巢肿瘤发生过程中发挥着重要作用。在本研究中,我们采用lncRNA挖掘方法,分析了来自基因表达综合数据库中493例卵巢癌患者的lncRNA表达谱,并在训练数据集GSE9891中鉴定出一组与患者生存相关的由7个lncRNA组成的特征集(BC037530、AK021924、AK094536、AK094536、BC062365、BC004123和BC007937)。我们还构建了一个风险评分模型,根据这7个lncRNA的表达将患者分为低风险组和高风险组。我们在另外两个数据集GSE26193和GSE63885中进一步验证了我们风险评分模型的预测能力。我们的分析表明,除了国际妇产科联盟(FIGO)分期和患者年龄外,这7个lncRNA特征集可作为一个独立的预测指标。进一步研究发现,这7个lncRNA特征集与癌症中少数关键信号通路相关。综合来看,所有这些发现有力地支持了这7个lncRNA特征集可作为一个强大的预后生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69b6/5466362/820b0df2a59f/ott-10-2805Fig1.jpg

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