Prognostic Value of a Long Non-coding RNA Signature in Localized Clear Cell Renal Cell Carcinoma.

Abstract Background Long non-coding RNAs (lncRNAs) can be used as prognostic biomarkers in many types of cancer. Objective We sought to establish an lncRNA signature to improve postoperative risk stratification for patients with localized clear cell renal cell carcinoma (ccRCC). Design, setting, and participants Based 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 analysis Univariable 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 limitations Using 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 Conclusions The 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 summary The RCClnc4 classifier could facilitate patient management and treatment decisions.