• Le Qu, Ze-Lin Wang, Qi Chen, Yao-Ming Li, Hao-Wei He, James J Hsieh, Song Xue, Zhen-Jie Wu, Bing Liu, Hao Tang, Xiao-Feng Xu, Feng Xu, Jie Wang, Yi Bao, An-Bang Wang, Dong Wang, Xiao-Ming Yi, Zhong-Kui Zhou, Chang-Jie Shi, Ke Zhong, Zheng-Cheng Sheng, Yu-Lin Zhou, Jun Jiang, Xiao-Yuan Chu, Jia He, Jing-Ping Ge, Zheng-Yu Zhang, Wen-Quan Zhou, Cheng Chen, Jian-Hua Yang, Ying-Hao Sun, and Lin-Hui Wang.
• Department of Urology, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, China; Department of Urology, Changzheng Hospital, Second Military Medical University, Shanghai, China. Electronic address: septsoul@hotmail.com.
• Eur. Urol. 2018 Dec 1; 74 (6): 756-763.

BackgroundLong non-coding RNAs (lncRNAs) can be used as prognostic biomarkers in many types of cancer.ObjectiveWe sought to establish an lncRNA signature to improve postoperative risk stratification for patients with localized clear cell renal cell carcinoma (ccRCC).Design, Setting, And ParticipantsBased on the RNA-seq data of 444 stage I-III ccRCC tumours from The Cancer Genome Atlas project, we built a four-lncRNA-based classifier using the least absolute shrinkage and selection operation (LASSO) Cox regression model in 222 randomly selected samples (training set) and validated the classifier in the remaining 222 samples (internal validation set). We confirmed this classifier in an external validation set of 88 patients with stage I-III ccRCC from a Japan cohort and using quantitative reverse transcription polymerase chain reaction (RT-PCR) in another three independent sets that included 1869 patients from China with stage I-III ccRCC.Outcome Measurements And Statistical AnalysisUnivariable and multivariable Cox regression, Harrell's concordance index (c-index), and time-dependent receiver operating characteristic curves were used to evaluate the association of the classifier with overall survival, disease-specific survival, and disease-free survival.Results And LimitationsUsing the LASSO Cox regression model, we built a classifier named RCClnc4 based on four lncRNAs: ENSG00000255774, ENSG00000248323, ENSG00000260911, and ENSG00000231666. In the RNA-seq and RT-PCR data sets, the RCClnc4 signature significantly stratified patients into high-risk versus low-risk groups in terms of clinical outcome across and within subpopulations and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 1.34 [95% confidence interval {CI}: 1.03-1.75; p=0.028] to 1.89 [95% CI, 1.55-2.31; p<0.001]) after adjusting for clinical and pathologic factors. The RCClnc4 signature achieved a higher accuracy (mean c-index, 0.72) than clinical staging systems such as TNM (mean c-index, 0.62) and the stage, size, grade, and necrosis (SSIGN) score (mean c-index, 0.64), currently reported prognostic signatures and biomarkers for the estimation of survival. When integrated with clinical characteristics, the composite clinical and lncRNA signature showed improved prognostic accuracy in all data sets (TNM + RCClnc4 mean c-index, 0.75; SSIGN + RCClnc4 score mean c-index, 0.75). The RCClnc4 classifier was able to identify a clinically significant number of both high-risk stage I and low-risk stage II-III patients.ConclusionsThe RCClnc4 classifier is a promising and potential prognostic tool in predicting the survival of patients with stage I-III ccRCC. Combining the lncRNA classifier with clinical and pathological parameters allows for accurate risk assessment in guiding clinical management.Patient SummaryThe RCClnc4 classifier could facilitate patient management and treatment decisions.Copyright © 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.

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