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[Construction of prognostic model and identification of prognostic biomarkers based on the expression of long non-coding RNA in bladder cancer via bioinformatics].

OBJECTIVE
To construct the prognostic model and identify the prognostic biomarkers based on long non-coding RNA (lncRNA) in bladder cancer.


METHODS
The lncRNA expression data and corresponding clinical data of bladder cancer were collected from The Cancer Genome Atlas (TCGA) database. The software Perl and R, and R packages were used for data integration, extraction, analysis and visualization. Detailly, R package "edgeR" was utilized to screen differentially expressed lncRNA in bladder cancer tissues compared with the normal bladder samples. The univariate Cox regression and the least absolute shrinkage and selection operator (Lasso) regression were performed to identify key lncRNA that were utilized to construct the prognostic model by the multivariate Cox regression. According to the median value of the risk score, all patients were divided into the high-risk group and low-risk group to perform the Kaplan-Meier (K-M) survival curves, receiver operating characteristic (ROC) curve and C-index, estimating the prognostic power of the prognostic model. In addition, the hazard ratio (HR) and 95% confidence interval (CI) of each key lncRNA were also calculated by the multivariate Cox regression. Moreover, we performed the K-M survival analysis for each significant key lncRNA from the result of the multivariate Cox regression.


RESULTS
A total of 691 lncRNA were identified as differentially expressed lncRNA, and 35 lncRNA signatures were initially considered associated with the prognosis of bladder cancer, where in 23 lncRNA were identified as key lncRNA associated with the prognosis. The overall survival time in years of the low-risk group was obviously longer than that of the high-risk group [(2.85±2.72) years vs. (1.58±1.51) years, P<0.001]. The area under the ROC curve (AUC) was 0.813 (3-year survival) and 0.778 (5-year survival) respectively, and the C-index was 0.73. In addition, HR and 95%CI of each key lncRNA were calculated by the multivariate Cox regression and 11 lncRNA were significant. Furthermore, K-M survival analysis revealed the independent prognostic value of 3 lncRNA, including AL589765.1 (P=0.004), AC023824.1 (P=0.022)and PKN2-AS1 (P=0.016).


CONCLUSION
The present study successfully constructed the prognostic model based on the expression level of 23 lncRNA and finally identified one protective prognostic biomarker AL589765.1, and two adverse prognostic biomarkers including AC023824.1 and PKN2-AS1 in bladder cancer.