Identification of a 4-lncRNA signature predicting prognosis of patients with non-small cell lung cancer: a multicenter study in China
Identification of a 4-lncRNA signature predicting prognosis of patients with non-small cell lung cancer: a multicenter study in China
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鉴定预测非小细胞肺癌患者预后的 4-lncRNA 特征：中国的一项多中心研究
DOI：
10.1186/s12967-020-02485-8
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发表时间：
2020-08-20
影响因子：
7.4
通讯作者：
Wang, Hui-Yun
中科院分区：
文献类型：
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作者：
Wang, Rui-Qi;Long, Xiao-Ran;Wang, Hui-Yun
背景先前的研究结果表明，肿瘤、淋巴结和转移（TNM）分期系统不足以准确预测非小肺癌（NSCLC）患者的生存结果。因此，本研究旨在鉴定预测NSCLC患者生存的长链非编码RNA （lncRNA）特征，并为TNM分期系统提供额外的预后信息。方法从某医院招募非小细胞肺癌患者，分为发现组（n = 194）和验证组（n = 172），使用定制的lncRNA芯片进行检测。另外73例来自不同医院的NSCLC病例（独立验证队列）采用qRT-PCR检查。通过微阵列程序的显著性分析确定差异表达的lncrna，并在发现队列中使用Cox回归确定与生存相关的lncrna。这些预后lncrna被用来用风险评分法构建预后特征。然后，使用验证队列和独立队列确认预后特征的效用。结果在发现队列中，我们发现305个lncrna在非小细胞肺癌组织与匹配的邻近正常肺组织中差异表达，其中15个与生存相关；从15个存活lncrna中鉴定出4-lncRNA预后特征，这与NSCLC患者的存活显著相关。在验证队列和独立验证队列中进一步验证了这一特征。此外，多变量Cox分析表明，4-lncRNA特征是一个独立的生存预测因子。然后，我们将4-lncRNA签名与TNM分期相结合，建立了新的风险评分模型。受试者工作特征（ROC）曲线显示，在所有队列中，联合模型的预后价值均显著高于单纯TNM期。在这项研究中，我们发现了一个4-lncRNA特征，它可能是一个强大的预后生物标志物，可以为TNM分期系统提供额外的生存信息。
BackgroundPrevious findings have indicated that the tumor, nodes, and metastases (TNM) staging system is not sufficient to accurately predict survival outcomes in patients with non-small lung carcinoma (NSCLC). Thus, this study aims to identify a long non-coding RNA (lncRNA) signature for predicting survival in patients with NSCLC and to provide additional prognostic information to TNM staging system.MethodsPatients with NSCLC were recruited from a hospital and divided into a discovery cohort (n = 194) and validation cohort (n = 172), and detected using a custom lncRNA microarray. Another 73 NSCLC cases obtained from a different hospital (an independent validation cohort) were examined with qRT-PCR. Differentially expressed lncRNAs were determined with the Significance Analysis of Microarrays program, from which lncRNAs associated with survival were identified using Cox regression in the discovery cohort. These prognostic lncRNAs were employed to construct a prognostic signature with a risk-score method. Then, the utility of the prognostic signature was confirmed using the validation cohort and the independent cohort.ResultsIn the discovery cohort, we identified 305 lncRNAs that were differentially expressed between the NSCLC tissues and matched, adjacent normal lung tissues, of which 15 are associated with survival; a 4-lncRNA prognostic signature was identified from the 15 survival lncRNAs, which was significantly correlated with survivals of NSCLC patients. This signature was further validated in the validation cohort and independent validation cohort. Moreover, multivariate Cox analysis demonstrates that the 4-lncRNA signature is an independent survival predictor. Then we established a new risk-score model by combining 4-lncRNA signature and TNM staging stage. The receiver operating characteristics (ROC) curve indicates that the prognostic value of the combined model is significantly higher than that of the TNM stage alone, in all the cohorts.ConclusionsIn this study, we identified a 4-lncRNA signature that may be a powerful prognosis biomarker and can provide additional survival information to the TNM staging system.