Prognostic value of long non-coding RNA signatures in bladder cancer

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Abstract

Bladder cancer (BLCA) is a devastating cancer whose early diagnosis can ensure better prognosis. Aim of this study was to evaluate the potential utility of lncRNAs in constructing lncRNA-based classifiers of BLCA prognosis and recurrence. Based on the data concerning BLCA retrieved from TCGA, lncRNA-based classifiers for OS and RFS were built using the least absolute shrinkage and selection operation (LASSO) Cox regression model in the training cohorts. More specifically, a 14-lncRNA-based classifier for OS and a 12-lncRNA-based classifier for RFS were constructed using the LASSO Cox regression. According to the prediction value, patients were divided into high/low-risk groups based on the cut-off of the median risk-score. The log-rank test showed significant differences in OS and RFS between low- and high-risk groups in the training, validation and whole cohorts. In the time-dependent ROC curve analysis, the AUCs for OS in the first, third, and fifth year were 0.734, 0.78, and 0.78 respectively, whereas the prediction capability of the 14-lncRNA classifier was superior to a previously published lncRNA classifier. As for the RFS, the AUCs in the first, third, and fifth year were 0.755, 0.715, and 0.740 respectively. In summary, the two-lncRNA-based classifiers could serve as novel and independent prognostic factors for OS and RFS individually.

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