Zhong Yue, Yang Yong, He Lei, Zhou Yang, Cheng Niangmei, Chen Geng, Zhao Bixing, Wang Yingchao, Wang Gaoxiong, Liu Xiaolong
College of Life Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian, 350002, People's Republic of China.
The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, 350025, People's Republic of China.
J Hepatocell Carcinoma. 2021 Apr 29;8:301-312. doi: 10.2147/JHC.S303330. eCollection 2021.
The aberrant expressions of lncRNAs have been frequently demonstrated to be closely associated with the prognosis of patients in many cancer types including hepatocellular carcinoma (HCC). Integration of these lncRNAs might provide accurate evaluation of HCC. Therefore, this study aims to develop a novel prognostic evaluation model based on the expression of lncRNAs to predict the survival of HCC patients, postoperatively.
RNA sequencing (RNA-seq) analysis was performed for 61 HCC patients (training cohort) to screen prognosis-associated lncRNAs with univariate Cox regression and Log rank test analyses. Multivariate Cox regression analysis was then applied to establish the final model, which was further verified in a validation cohort (n=191). Moreover, performance of the mode was assessed with time-dependent receiver operating characteristic curve (tdROC), Harrell's c-index, and Gönen & Heller's K.
After a serial statistical computation, a novel risk scoring model consisting of four lncRNAs and TNM staging was established, which could successfully divide the HCC patients into low-risk and high-risk groups with significantly different OS and RFS in both training and validation cohorts. tdROC analysis showed that this model achieved a high performance in predicting OS and 2-year RFS in both cohorts. Gene Set Enrichment Analysis revealed that HCC tumor tissues with high-risk score have stronger capacities in immune escape and resistance to treatment.
We successfully established a novel prognostic evaluation model, which exhibited reliable capacity in predicting the OS and early recurrence of HCC patients with relatively higher accuracy.
lncRNAs的异常表达已被频繁证明与包括肝细胞癌（HCC）在内的多种癌症类型患者的预后密切相关。整合这些lncRNAs可能会为HCC提供准确的评估。因此，本研究旨在基于lncRNAs的表达开发一种新的预后评估模型，以预测HCC患者术后的生存情况。
对61例HCC患者（训练队列）进行RNA测序（RNA-seq）分析，通过单变量Cox回归和Log秩检验分析筛选与预后相关的lncRNAs。然后应用多变量Cox回归分析建立最终模型，并在验证队列（n = 191）中进一步验证。此外，使用时间依赖性受试者工作特征曲线（tdROC）、Harrell's c指数和Gönen & Heller's K评估模型的性能。
经过一系列统计计算，建立了一个由四个lncRNAs和TNM分期组成的新的风险评分模型，该模型能够成功地将HCC患者分为低风险和高风险组，在训练队列和验证队列中，两组的总生存期（OS）和无复发生存期（RFS）均有显著差异。tdROC分析表明，该模型在预测两个队列的OS和2年RFS方面均具有较高的性能。基因集富集分析显示，高风险评分的HCC肿瘤组织具有更强的免疫逃逸和抗治疗能力。
我们成功建立了一种新的预后评估模型，该模型在预测HCC患者的OS和早期复发方面表现出可靠的能力，且准确性相对较高。
