Li Wang, Chen Qi-Feng, Huang Tao, Wu Peihong, Shen Lujun, Huang Zi-Lin
Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China.
State Key Laboratory of Oncology in South China, Guangzhou, China.
Front Oncol. 2020 Jun 10;10:780. doi: 10.3389/fonc.2020.00780. eCollection 2020.
An accumulating body of evidence suggests that long non-coding RNAs (lncRNAs) can serve as potential cancer prognostic factors. However, the utility of lncRNA combinations in estimating overall survival (OS) for hepatocellular carcinoma (HCC) remains to be elucidated. This study aimed to construct a powerful lncRNA signature related to the OS for HCC to enhance prognostic accuracy. The expression patterns of lncRNAs and related clinical data of 371 HCC patients were obtained based on The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) were acquired by comparing tumors with adjacent normal samples. lncRNAs displaying significant association with OS were screened through univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. All cases were classified into the validation or training group at the ratio of 3:7 to validate the constructed lncRNA signature. Data from the Gene Expression Omnibus (GEO) were used for external validation. We conducted real-time polymerase chain reaction (PCR) and assays for Transwell invasion, migration, CCK-8, and colony formation to determine the biological roles of lncRNA. Gene set enrichment analysis (GSEA) of the lncRNA model risk score was also conducted. We identified 1292 DElncRNAs, among which 172 were significant in univariate Cox regression analysis. In the training group ( = 263), LASSO regression analysis confirmed 11 DElncRNAs including AC010547.1, AC010280.2, AC015712.7, GACAT3 (gastric cancer associated transcript 3), AC079466.1, AC089983.1, AC051618.1, AL121721.1, LINC01747, LINC01517, and AC008750.3. The prognostic risk score was calculated, and the constructed risk model showed significant correlation with HCC OS (log-rank -value of 8.489e-9, hazard ratio of 3.648, 95% confidence interval: 2.238-5.945). The area under the curve (AUC) for this lncRNA model was up to 0.846. This risk model was confirmed in the validation group ( = 108), the entire cohort, and the external GEO dataset ( = 203). GACAT3 was highly expressed in HCC tissues and cell lines. Based on online databases, GACAT3 expression independently affects both OS and disease-free survival in HCC patients. Silencing GACAT3 significantly suppressed HCC cell proliferation, invasion, and migration. Moreover, pathways related to the lncRNA model risk score were confirmed by GSEA. The lncRNA signature established in this study can be used to predict HCC prognosis, which could provide novel clinical evidence to guide targeted HCC treatment.
越来越多的证据表明，长链非编码RNA（lncRNA）可作为潜在的癌症预后因素。然而，lncRNA组合在评估肝细胞癌（HCC）总生存期（OS）方面的效用仍有待阐明。本研究旨在构建一个与HCC的OS相关的强大lncRNA特征，以提高预后准确性。基于癌症基因组图谱（TCGA）获得了371例HCC患者的lncRNA表达模式及相关临床数据。通过将肿瘤与相邻正常样本进行比较，获得差异表达的lncRNA（DElncRNA）。通过单变量Cox回归分析和最小绝对收缩和选择算子（LASSO）算法筛选出与OS显著相关的lncRNA。所有病例按3:7的比例分为验证组或训练组，以验证构建的lncRNA特征。来自基因表达综合数据库（GEO）的数据用于外部验证。我们进行了实时聚合酶链反应（PCR）以及Transwell侵袭、迁移、CCK-8和集落形成试验，以确定lncRNA的生物学作用。还对lncRNA模型风险评分进行了基因集富集分析（GSEA）。我们鉴定出1292个DElncRNA，其中172个在单变量Cox回归分析中具有显著性。在训练组（n = 263）中，LASSO回归分析确认了11个DElncRNA，包括AC010547.1、AC010280.2、AC015712.7、GACAT3（胃癌相关转录本3）、AC079466.1、AC089983.1、AC051618.1、AL121721.1、LINC01747、LINC01517和AC008750.3。计算了预后风险评分，构建的风险模型与HCC的OS显著相关（对数秩P值为8.489e - 9，风险比为3.648，95%置信区间：2.238 - 5.945）。该lncRNA模型的曲线下面积（AUC）高达0.846。该风险模型在验证组（n = 108）、整个队列以及外部GEO数据集（n = 203）中得到了证实。GACAT3在HCC组织和细胞系中高表达。基于在线数据库，GACAT3表达独立影响HCC患者的OS和无病生存期。沉默GACAT3可显著抑制HCC细胞的增殖、侵袭和迁移。此外，GSEA证实了与lncRNA模型风险评分相关的通路。本研究建立的lncRNA特征可用于预测HCC预后，可为指导HCC靶向治疗提供新的临床证据。
