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通过 lncRNA 相关 ceRNA 分析筛选与肝癌预后相关的显著生物标志物。

Screening of significant biomarkers related with prognosis of liver cancer by lncRNA-associated ceRNAs analysis.

机构信息

Department of General Surgery, Shanxi Dayi Hospital, Shanxi Medical University, Taiyuan, China.

Department of Hepatobilliary Pancreatic Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China.

出版信息

J Cell Physiol. 2020 Mar;235(3):2464-2477. doi: 10.1002/jcp.29151. Epub 2019 Sep 10.

Abstract

This study aimed to identify significant biomarkers related to the prognosis of liver cancer using long noncoding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) analysis. Differentially expressed mRNA and lncRNAs between liver cancer and paracancerous tissues were screened, and the functions of these mRNAs were predicted by gene ontology and pathway enrichment analyses. A ceRNA network consisting of differentially expressed mRNAs and lncRNAs was constructed. LncRNA FENDRR and lncRNA HAND2-AS1 were hub nodes in the ceRNA network. A risk score assessment model consisting of eight genes (PDE2A, ESR1, FBLN5, ALDH8A1, AKR1D1, EHHADH, ADRA1A, and GNE) associated with prognosis were developed. Multivariate Cox regression suggested that both pathologic_T and risk group could be regarded as independent prognostic factors. Furthermore, a nomogram model consisting of pathologic_T and risk group showed a good prediction ability for predicting the survival rate of liver cancer patients. The nomogram model consisting of pathologic_T and a risk score assessment model could be regarded as an independent factor for predicting prognosis of liver cancer.

摘要

本研究旨在通过长非编码 RNA(lncRNA)相关竞争性内源性 RNA(ceRNA)分析,鉴定与肝癌预后相关的显著生物标志物。筛选肝癌与癌旁组织之间差异表达的 mRNA 和 lncRNA,并通过基因本体和通路富集分析预测这些 mRNA 的功能。构建了包含差异表达的 mRNA 和 lncRNA 的 ceRNA 网络。lncRNA FENDRR 和 lncRNA HAND2-AS1 是 ceRNA 网络中的枢纽节点。构建了一个由 8 个基因(PDE2A、ESR1、FBLN5、ALDH8A1、AKR1D1、EHHADH、ADRA1A 和 GNE)组成的与预后相关的风险评分评估模型。多变量 Cox 回归表明,病理_T 和风险组都可以作为独立的预后因素。此外,由病理_T 和风险组组成的列线图模型显示出良好的预测肝癌患者生存率的能力。由病理_T 和风险评分评估模型组成的列线图模型可作为预测肝癌预后的独立因素。

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