Supplementary Material for: Identification of Potential Prognostic Long Non-Coding RNA Biomarkers for Predicting Survival in Patients with Hepatocellular Carcinoma

Background/Aims: The aim of the current study was to identify potential prognostic long non-coding RNA (lncRNA) biomarkers for predicting survival in patients with hepatocellular carcinoma (HCC) using The Cancer Genome Atlas (TCGA) dataset and bioinformatics analysis. Methods: RNA sequencing and clinical data of HCC patients from TCGA were used for prognostic association assessment by univariate Cox analysis. A prognostic signature was built using stepwise multivariable Cox analysis, and a comprehensive analysis was performed to evaluate its prognostic value. The prognostic signature was further evaluated by functional assessment and bioinformatics analysis. Results: Thirteen differentially expressed lncRNAs (DELs) were identified and used to construct a single prognostic signature. Patients with high risk scores showed a significantly increased risk of death (adjusted P < 0.0001, adjusted hazard ratio = 3.522, 95% confidence interval = 2.307–5.376). In the time-dependent receiver operating characteristic analysis, the prognostic signature performed well for HCC survival prediction with an area under curve of 0.809, 0.782 and 0.79 for 1-, 3- and 5-year survival, respectively. Comprehensive survival analysis of the 13-DEL prognostic signature suggested that it serves as an independent factor in HCC, showing a better performance for prognosis prediction than traditional clinical indicators. Functional assessment and bioinformatics analysis suggested that the prognostic signature was associated with the cell cycle and peroxisome proliferator-activated receptor signaling pathway. Conclusions: The novel lncRNA expression signature identified in the present study may be a potential biomarker for predicting the prognosis of HCC patients.背景与目的：本研究旨在利用癌症基因组图谱（The Cancer Genome Atlas，TCGA）数据集与生物信息学分析方法，筛选可用于预测肝细胞癌（hepatocellular carcinoma，HCC）患者生存预后的潜在长链非编码RNA（long non-coding RNA，lncRNA）生物标志物。方法：提取TCGA数据库中肝细胞癌患者的RNA测序数据与临床资料，通过单变量Cox回归分析开展预后关联评估；采用逐步多变量Cox回归分析构建预后特征模型，并通过综合分析评估其预后价值；此外还通过功能富集分析与生物信息学方法对该预后特征模型进行进一步验证与评价。结果：本研究共筛选得到13个差异表达长链非编码RNA（differentially expressed lncRNAs，DELs），并以此构建了单一预后特征模型。风险评分较高的患者死亡风险显著升高（校正P值<0.0001，校正风险比=3.522，95%置信区间=2.307~5.376）。在时间依赖性受试者工作特征曲线分析中，该预后特征模型对肝细胞癌患者的生存预测表现优异，1年、3年和5年生存率的曲线下面积分别为0.809、0.782和0.79。针对该13个差异表达长链非编码RNA预后特征模型的综合生存分析显示，其可作为肝细胞癌预后的独立影响因素，且预后预测性能优于传统临床指标。功能富集分析与生物信息学分析结果表明，该预后特征模型与细胞周期及过氧化物酶体增殖物激活受体信号通路密切相关。结论：本研究筛选得到的新型长链非编码RNA表达特征模型，有望成为预测肝细胞癌患者预后的潜在生物标志物。