a Department of Pathology , Union Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan , China.
b Intensive Care Unit , Zhongnan Hospital of Wuhan University , Wuhan , China.
Artif Cells Nanomed Biotechnol. 2018 Sep;46(6):1207-1214. doi: 10.1080/21691401.2017.1366334. Epub 2017 Aug 24.
The outcomes of Lung squamous cell carcinoma (LUSC) is still challenging to evaluate or predict. We aimed to screen prognostic lncRNAs and to mine their roles in LUSC. RNA-Seq data of primary lung cancer were extracted from the Cancer Genome Atlas. Generally, changed lncRNAs in cancer samples were screened and analyzed in univariate survival analysis for identification of prognostic lncRNAs. Robust likelihood-based survival model was generated and random sampling iterations were performed 1000 times to calculate the frequency of feature key lncRNAs. Clustering and multivariate survival analysis of these lncRNAs was used to evaluate their functions and impacts on prognosis. Finally, the stability and validity of the optimal clustering model were verified. In total, we obtained 5664 generally changed lncRNAs among primary lung cancer samples, including 289 identified relating to prognosis in univariate survival analysis. Robust likelihood-based survival modelling for 1000 iterations generated 11 feature lncRNAs with frequency larger than 300. Their interacting proteins were found participating in DNA repairing and cell proliferation. Among stable assembly of 11 lncRNAs, a 4-lncRNA model was selected finally with high stability and feasibility. The ideal 4-lncRNA model can cluster patient samples with significant difference, providing new avenues for the prognostic predication of LUSC.
肺鳞状细胞癌（LUSC）的预后评估仍然具有挑战性。本研究旨在筛选与预后相关的长链非编码 RNA（lncRNA），并挖掘其在 LUSC 中的作用。从癌症基因组图谱（TCGA）中提取了原发性肺癌的 RNA-Seq 数据。首先，通过单变量生存分析筛选和分析癌症样本中变化的 lncRNA，以确定与预后相关的 lncRNA。然后，生成基于似然的稳健生存模型，并进行 1000 次随机抽样迭代，以计算特征关键 lncRNA 的频率。对这些 lncRNA 进行聚类和多变量生存分析，以评估它们的功能和对预后的影响。最后，验证最优聚类模型的稳定性和有效性。在原发性肺癌样本中，我们共获得了 5664 个普遍变化的 lncRNA，其中 289 个 lncRNA 在单变量生存分析中与预后相关。通过 1000 次迭代的基于似然的稳健生存模型生成了 11 个特征 lncRNA，其频率大于 300。这些 lncRNA 的相互作用蛋白参与 DNA 修复和细胞增殖。在 11 个 lncRNA 的稳定组合中，最终选择了一个 4-lncRNA 模型，该模型具有较高的稳定性和可行性。理想的 4-lncRNA 模型可以对患者样本进行聚类，具有显著差异，为 LUSC 的预后预测提供了新的途径。
