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一个六 miRNA -signature 预测胃癌患者的预后。

A six-microRNA signature to predict outcomes of patients with gastric cancer.

机构信息

Department of Oncology The First Affiliated Hospital of University of Science and Technology of China Hefei China.

出版信息

FEBS Open Bio. 2019 Jan 31;9(3):538-547. doi: 10.1002/2211-5463.12593. eCollection 2019 Mar.

DOI:10.1002/2211-5463.12593
PMID:30868062
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6396146/
Abstract

Gastric cancer (GC) is a common gastrointestinal tumor with poor prognosis. However, conventional prognostic factors cannot accurately predict the outcomes of GC patients. Therefore, there remains a need to identify novel predictive markers to improve prognosis. In this study, we obtained microRNA expression profiles of 385 GC patients from The Cancer Genome Atlas. We performed Cox regression analysis to identify overall survival-related microRNA and then constructed a microRNA signature-based prognostic model. The accuracy of the model was evaluated and validated through Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) curve analysis. The independent prognostic value of the model was assessed by multivariate Cox regression analysis. Enrichment analysis was performed to explore potential functions of the prognostic microRNA. Finally, a prognostic model based on a six-microRNA (miRNA-100, miRNA-374a, miRNA-509-3, miRNA-668, miRNA-549, and miRNA-653) signature was developed. Further analysis in the training, test, and complete The Cancer Genome Atlas set showed the model can distinguish between high-risk and low-risk patients and predict 3-year and 5-year survival. The six-microRNA signature was also an independent prognostic marker, and enrichment analysis suggested that the microRNA may be involved in cell cycle and mitosis. These results demonstrated that the model based on the six-microRNA signature can be used to accurately predict the prognosis of GC patients.

摘要

胃癌(GC)是一种常见的胃肠道肿瘤,预后较差。然而,传统的预后因素不能准确预测 GC 患者的结局。因此,仍然需要识别新的预测标志物来改善预后。在这项研究中,我们从癌症基因组图谱中获得了 385 例 GC 患者的 microRNA 表达谱。我们进行了 Cox 回归分析,以确定与总生存期相关的 microRNA,然后构建了基于 microRNA 特征的预后模型。通过 Kaplan-Meier 生存分析和时间依赖性 ROC 曲线分析评估和验证了模型的准确性。通过多变量 Cox 回归分析评估了模型的独立预后价值。进行了富集分析以探讨预后 microRNA 的潜在功能。最后,开发了基于六个 microRNA(miRNA-100、miRNA-374a、miRNA-509-3、miRNA-668、miRNA-549 和 miRNA-653)特征的预后模型。在训练、测试和完整的癌症基因组图谱集中的进一步分析表明,该模型可以区分高风险和低风险患者,并预测 3 年和 5 年的生存率。这六个 microRNA 特征也是一个独立的预后标志物,富集分析表明 microRNA 可能参与细胞周期和有丝分裂。这些结果表明,基于六个 microRNA 特征的模型可用于准确预测 GC 患者的预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ec0/6396146/cab8a8db2cb7/FEB4-9-538-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ec0/6396146/642aa1d56d4a/FEB4-9-538-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ec0/6396146/49bfbbb2f9b1/FEB4-9-538-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ec0/6396146/c03c88cca7f4/FEB4-9-538-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ec0/6396146/0e65dacc2a14/FEB4-9-538-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ec0/6396146/cab8a8db2cb7/FEB4-9-538-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ec0/6396146/642aa1d56d4a/FEB4-9-538-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ec0/6396146/49bfbbb2f9b1/FEB4-9-538-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ec0/6396146/c03c88cca7f4/FEB4-9-538-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ec0/6396146/0e65dacc2a14/FEB4-9-538-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ec0/6396146/cab8a8db2cb7/FEB4-9-538-g005.jpg

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