Shao-Hao Chen1, Fei Lin1, Jun-Ming Zhu1, Zhi-Bin Ke1, Ting-Ting Lin1, Yun-Zhi Lin1, Xue-Yi Xue1, Yong Wei1, Qing-Shui Zheng1, Ye-Hui Chen2, Ning Xu3. 1. Departments of Urology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China. 2. Departments of Urology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China. Electronic address: chenyehui@fjmu.edu.cn. 3. Departments of Urology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China. Electronic address: drxun@fjmu.edu.cn.
Abstract
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.
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.