State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, Guangdong, China.
Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, Guangdong, China.
Genes (Basel). 2020 Aug 8;11(8):908. doi: 10.3390/genes11080908.
Currently, an increasing number of studies suggest that long non-coding RNAs (lncRNAs) and methylation-regulated lncRNAs play a critical role in the pathogenesis of various cancers including hepatocellular carcinoma (HCC). Therefore, methylated differentially expressed lncRNAs (MDELs) may be critical biomarkers of HCC. In this study, 63 MDELs were identified by screening The Cancer Genome Atlas (TCGA) HCC lncRNAs expression data set and lncRNAs methylation data set. Based on univariate and multivariate survival analysis, four MDELs (AC025016.1, LINC01164, LINC01183 and LINC01269) were selected to construct the survival prognosis prediction model. Through the PI formula, the study indicates that our new prediction model performed well and is superior to the traditional staging method. At the same time, compared with the previous prediction models reported in the literature, the results of time-dependent receiver operating characteristic (ROC) curve analysis show that our 4-MDELs model predicted overall survival (OS) stability and provided better prognosis. In addition, we also applied the prognostic model to Cancer Cell Line Encyclopedia (CCLE) cell lines and classified different hepatoma cell lines through the model to evaluate the sensitivity of different hepatoma cell lines to different drugs. In conclusion, we have established a new risk scoring system to predict the prognosis, which may have a very important guiding significance for the individualized treatment of HCC patients.
目前，越来越多的研究表明，长非编码 RNA（lncRNA）和甲基化调节的 lncRNA 在包括肝细胞癌（HCC）在内的各种癌症的发病机制中起着关键作用。因此，甲基化差异表达的 lncRNA（MDEL）可能是 HCC 的关键生物标志物。在这项研究中，通过筛选癌症基因组图谱（TCGA）HCC lncRNA 表达数据集和 lncRNA 甲基化数据集，鉴定了 63 个 MDEL。基于单变量和多变量生存分析，选择了四个 MDEL（AC025016.1、LINC01164、LINC01183 和 LINC01269）构建生存预后预测模型。通过 PI 公式，研究表明我们的新预测模型表现良好，优于传统的分期方法。同时，与文献中报道的以前的预测模型相比，时间依赖性接收器操作特征（ROC）曲线分析的结果表明，我们的 4-MDEL 模型预测总体生存率（OS）的稳定性更好，提供了更好的预后。此外，我们还将预后模型应用于癌症细胞系百科全书（CCLE）细胞系，并通过模型对不同肝癌细胞系进行分类，以评估不同肝癌细胞系对不同药物的敏感性。总之，我们建立了一个新的风险评分系统来预测预后，这可能对 HCC 患者的个体化治疗具有非常重要的指导意义。
