Identification and Validation of a Prognostic lncRNA Signature for Hepatocellular Carcinoma
Identification and Validation of a Prognostic lncRNA Signature for Hepatocellular Carcinoma
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DOI：
10.3389/fonc.2020.00780
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发表时间：
2020-06-10
影响因子：
4.7
通讯作者：
Huang, Zi-Lin
中科院分区：
文献类型：
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作者：
Li, Wang;Chen, Qi-Feng;Huang, Zi-Lin
背景：越来越多的证据表明，长链非编码RNA（lncRNA）可以作为潜在的癌症预后因素。然而，lncRNA组合在估计肝细胞癌（HCC）的总生存期（OS）中的效用仍有待阐明。本研究的目的是构建一个强大的lncRNA签名相关的OS肝癌，以提高预后的准确性。方法：应用美国癌症基因组图谱（TCGA），分析371例HCC患者lncRNA的表达情况及相关临床资料。通过比较肿瘤与相邻正常样品获得差异表达的lncRNA（DElncRNA）。通过单变量考克斯回归分析和最小绝对收缩和选择算子（LASSO）算法筛选显示与OS显著相关的lncRNA。将所有病例以3：7的比例分为验证组或训练组，以验证构建的lncRNA签名。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）和Transwell侵袭，迁移，CCK-8和集落形成的测定，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）。结果：共鉴定出1292个DElncRNA，单因素考克斯回归分析有统计学意义的有172个。在训练组（n= 263）中，LASSO回归分析证实了11个DElncRNA，包括AC010547.1、AC010280.2、AC015712.7、GACAT 3 1、AC051618.1、AL121721.1、LINC 01747、LINC 01517和AC008750.3。计算预后风险评分，构建的风险模型与HCC OS显著相关（log-rankP值为8.489e-9，风险比为3.648，95%可信区间：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）、整个队列和外部GEO数据集（n= 203）中得到证实。GACAT 3在肝癌组织和细胞系中高表达。基于在线数据库，GACAT 3表达独立影响HCC患者的OS和无病生存期。GACAT 3基因沉默可显著抑制肝癌细胞的增殖、侵袭和迁移。此外，通过GSEA证实了与lncRNA模型风险评分相关的途径。结论：本研究建立的lncRNA标签可用于预测肝癌预后，为肝癌的靶向治疗提供新的临床依据。
Background: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. Methods: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. Results:We identified 1292 DElncRNAs, among which 172 were significant in univariate Cox regression analysis. In the training group (n= 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-rankP-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 (n= 108), the entire cohort, and the external GEO dataset (n= 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 GACAT3in vitrosignificantly suppressed HCC cell proliferation, invasion, and migration. Moreover, pathways related to the lncRNA model risk score were confirmed by GSEA. Conclusion: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.