A novel microRNA signature predicts survival in stomach adenocarcinoma.
A novel microRNA signature predicts survival in stomach adenocarcinoma.
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一种新的 microRNA 特征可预测胃腺癌的存活率
DOI：
10.18632/oncotarget.15961
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发表时间：
2017-04-25
期刊：
影响因子：
--
通讯作者：
Hao X
中科院分区：
文献类型：
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作者：
Ding B;Gao X;Li H;Liu L;Hao X
最近的microRNA （miRNA）表达谱研究表明，miRNA可作为各种恶性肿瘤的潜在预后生物标志物。在这项研究中，为了鉴定对胃腺癌（STAD）患者总生存（OS）具有预后价值的microrna，我们分析了来自癌症基因组图谱（TCGA）数据集的380例STAD样本的microrna表达谱和相关临床特征。通过生存分析和半监督主成分法，确定了预测STAD患者OS的8个mirna特征并进行了自我验证。我们开发了一个由这些mirna组成的线性预后模型，根据计算的预后评分将患者分为高风险组和低风险组。Kaplan-Meier分析显示，高危组患者的OS较低危组患者差。值得注意的是，该miRNA预后模型对早期STAD患者和化疗耐药患者的预后具有重要意义，这些患者可能受益于额外的医疗干预。最后，这个8 - mirna标记是一个独立的预后生物标志物，对5年生存率具有良好的预测性能。因此，这一特征可以作为预测STAD患者生存和化疗反应的一种新的生物标志物。
Recent microRNA (miRNA) expression profiling studies suggest the clinical use of miRNAs as potential prognostic biomarkers in various malignancies. In this study, aiming to identify microRNAs with prognostic value for overall survival (OS) in stomach adenocarcinoma (STAD) patients, we analyzed the miRNA expression profiles and the associated clinical characteristics in 380 STAD samples from The Cancer Genome Atlas (TCGA) dataset. An eight-miRNA signature for predicting OS in STAD patients was identified and self-validated by survival analysis and semi-supervised principal components method. We developed a linear prognostic model composed of these miRNAs to divide patients into high- and low-risk groups according to the calculated prognostic scores. Kaplan-Meier analysis demonstrated that patients in the high-risk group had worse OS compared with patients in the low-risk group. Notably, this miRNA prognostic model showed prognostic significance to the STAD patients in early stages and the chemo-resistant patients, who would potentially benefit from additional medical interventions. Finally, this eight-miRNA signature is an independent prognostic biomarker and demonstrates a good predictive performance for 5-year survival. Thus, this signature may serve as a novel biomarker for predicting survival as well as chemotherapy response in STAD patients.
基于有限混合模型的基因基因设置富集分析的方法。
影响因子：
1
作者：
Lee, Sang Mee;Wu, Baolin;Kersey, John H
通讯作者：
Kersey, John H