乳头状肾细胞癌中一种免疫相关lncRNA预后模型：lncRNA表达分析
An immune-related lncRNA prognostic model in papillary renal cell carcinoma: A lncRNA expression analysis.
作者信息
Chen Shao-Hao, Lin Fei, Zhu Jun-Ming, Ke Zhi-Bin, Lin Ting-Ting, Lin Yun-Zhi, Xue Xue-Yi, Wei Yong, Zheng Qing-Shui, Chen Ye-Hui, Xu Ning
机构信息
Departments of Urology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China.
Departments of Urology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China.
出版信息
Genomics. 2021 Jan;113(1 Pt 2):531-540. doi: 10.1016/j.ygeno.2020.09.046. Epub 2020 Sep 24.
OBJECTIVE
To screen several immune-related long non-coding RNAs (lncRNAs) and construct a prognostic model for papillary renal cell carcinoma (pRCC).
METHODS
Transcriptome-sequencing data of pRCC was downloaded and a prognostic model was constructed. Time-dependent receiver operating characteristic (ROC) curve was plotted and the area under curve (AUC) was calculated. We conducted quantitative reverse transcription polymerase chain reaction (RT-PCR) to verify the model. The gene set enrichment analysis (GSEA) was used to show the connection of our model with immune pathways.
RESULT
We identified four lncRNAs to constructed the model. The model was significantly associated with the survival time and survival state. The expression-levels of the four lncRNAs were measured and the prognosis of high-risk patients was significantly worse. The two immune-gene sets had an active performance in the high-risk patients.
CONCLUSION
We constructed a prognostic model in pRCC which provided more reference for treatment.
目的
筛选几种免疫相关的长链非编码RNA（lncRNA），并构建乳头状肾细胞癌（pRCC）的预后模型。
方法
下载pRCC的转录组测序数据并构建预后模型。绘制时间依赖的受试者工作特征（ROC）曲线并计算曲线下面积（AUC）。我们进行定量逆转录聚合酶链反应（RT-PCR）以验证该模型。基因集富集分析（GSEA）用于显示我们的模型与免疫途径的联系。
结果
我们鉴定出四种lncRNA以构建模型。该模型与生存时间和生存状态显著相关。测量了四种lncRNA的表达水平，高危患者的预后明显更差。两个免疫基因集在高危患者中表现活跃。
结论
我们构建了pRCC的预后模型，为治疗提供了更多参考。
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