Zhang Hao, Liu Renzheng, Sun Lin, Hu Xiao
Department of Hepatobiliary Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Department of ICU, The Affiliated Hospital of Qingdao University, Qingdao, China.
Front Mol Biosci. 2021 Nov 12;8:749313. doi: 10.3389/fmolb.2021.749313. eCollection 2021.
Liver cancer is a highly malignant tumor. Notably, recent studies have found that long non-coding RNAs (lncRNAs) play a prominent role in the prognosis of patients with liver cancer. Herein, we attempted to construct an lncRNA model to accurately predict the survival rate in liver cancer. Based on The Cancer Genome Atlas (TCGA) database, we first identified 1066 lncRNAs with differential expression. The patient data obtained from TCGA were divided into the experimental group and the verification group. According to the difference in lncRNAs, we used single-factor and multi-factor Cox regression to select the genes needed to build the model in the experimental group, which were verified in the verification group. The results showed that the model could accurately predict the survival rate of patients in the high and low risk groups. The reliability of the model was also confirmed by the area under the receiver operating characteristic curve. Our model is significantly correlated with different clinicopathological features. Finally, we built a ceRNA network based on lncRNAs, which was used to display miRNAs and mRNAs related to lncRNAs. In summary, we constructed an lncRNA model to predict the survival rate of patients with hepatocellular carcinoma.
肝癌是一种高度恶性的肿瘤。值得注意的是，最近的研究发现长链非编码RNA（lncRNA）在肝癌患者的预后中起着重要作用。在此，我们试图构建一个lncRNA模型来准确预测肝癌患者的生存率。基于癌症基因组图谱（TCGA）数据库，我们首先鉴定出1066个差异表达的lncRNA。从TCGA获得的患者数据被分为实验组和验证组。根据lncRNA的差异，我们在实验组中使用单因素和多因素Cox回归来选择构建模型所需的基因，并在验证组中进行验证。结果表明，该模型能够准确预测高风险和低风险组患者的生存率。模型的可靠性也通过受试者工作特征曲线下面积得到证实。我们的模型与不同的临床病理特征显著相关。最后，我们基于lncRNA构建了一个ceRNA网络，用于展示与lncRNA相关的miRNA和mRNA。总之，我们构建了一个lncRNA模型来预测肝细胞癌患者的生存率。
