Zheng Shanbo, Zheng Difan, Dong Chuanpeng, Jiang Jiahua, Xie Juntao, Sun Yihua, Chen Haiquan
Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, 270 Dong-An Road, Shanghai, 200032, People's Republic of China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
J Cancer Res Clin Oncol. 2017 Sep;143(9):1649-1657. doi: 10.1007/s00432-017-2411-9. Epub 2017 Apr 13.
Increasing evidence suggests that long non-coding RNAs (lncRNAs) may play a crucial role in many biological processes in a variety of cancers and serve as the basis for many clinical applications including prognostic biomarkers and potential therapeutic targets. The aim of this study is to develop a prognostic lncRNA signature with RNA-seq data in lung adenocarcinomas.
LncRNA expression profiles and clinical data of lung adenocarcinoma patients from The Cancer Genome Atlas (TCGA) were analyzed. Univariate Cox proportional regression model was used to identify prognostic lncRNAs, and then multivariate Cox proportional regression model was used to develop a prognostic signature. Survivals were compared using log-rank test, and the biological implications of prognostic lncRNAs were analyzed using the KEGG pathway functional enrichment analysis.
We identified eight lncRNAs which had prognostic association with p value <0.01 in a TCGA lung adenocarcinoma cohort of 478 patients. Then a novel prognostic signature with the eight lncRNAs was developed using Cox regression model. Signature high-risk cases had worse overall survival (OS, median 85.97 vs. 38.34 months, p < 0.001) and disease-free survival (DFS, median 44.02 vs. 26.58 months, p = 0.007) than low-risk cases. Multivariate Cox regression analysis suggested that the eight-lncRNA signature was independent of clinical and pathological factors. KEGG pathway functional enrichment analysis indicated potential functional roles of the eight prognostic lncRNAs in tumorigenesis.
Our findings suggest that the eight-lncRNA signature might provide an effective independent prognostic model for the prediction of lung adenocarcinoma patients.
越来越多的证据表明，长链非编码RNA（lncRNA）可能在多种癌症的许多生物学过程中发挥关键作用，并作为包括预后生物标志物和潜在治疗靶点在内的许多临床应用的基础。本研究的目的是利用肺腺癌的RNA测序数据开发一种预后lncRNA特征。
分析了来自癌症基因组图谱（TCGA）的肺腺癌患者的lncRNA表达谱和临床数据。使用单变量Cox比例回归模型识别预后lncRNA，然后使用多变量Cox比例回归模型开发预后特征。使用对数秩检验比较生存率，并使用KEGG通路功能富集分析分析预后lncRNA的生物学意义。
我们在一个478例患者的TCGA肺腺癌队列中鉴定出8个lncRNA，其与p值<0.01的预后相关。然后使用Cox回归模型开发了一种包含这8个lncRNA的新型预后特征。特征高风险病例的总生存期（OS，中位数85.97 vs. 38.34个月，p <0.001）和无病生存期（DFS，中位数44.02 vs. 26.58个月，p = 0.007）比低风险病例差。多变量Cox回归分析表明，这8个lncRNA特征独立于临床和病理因素。KEGG通路功能富集分析表明这8个预后lncRNA在肿瘤发生中的潜在功能作用。
我们的研究结果表明，这8个lncRNA特征可能为预测肺腺癌患者提供一种有效的独立预后模型。
