Analysis of a Long Non-coding RNA associated Signature to Predict Survival in Patients with Bladder Cancer.

BACKGROUND: The study aimed to find a potential long non-coding RNA (lncRNA) model related to survival in bladder cancer by analyzing data in The Cancer Genome Atlas (TCGA). METHODS: We downloaded the gene expression data from the TCGA and analyzed the differentially expressed lncRNAs (DELs) between tumor and normal tissues. Patients were divided into training and testing groups, and a prognostic risk score model with lncRNAs was constructed by using data in the training group using multivariate Cox and lasso regression analysis. We divided patients into high-and low-risk groups according to the median value in the lncRNA signature model. Survival and receiver operating characteristic (ROC) curves were visualized in both groups. Further, we validated the model in the testing group. RESULTS: We screened 169 DELs for bladder cancer. The univariate Cox regression analysis showed that 13 lncRNAs were associated with prognosis with a p-value <0.01. We selected 12 of these lncRNAs to perform a multivariate Cox analysis to build the lncRNA signature. The formula with nine lncRNAs, namely, MIR497HG, LINC00968, NALCN-AS1, LINC02321, RNF144A-AS1, MNX1-AS1, FLJ22447, LINC01956, FLJ42969, was significantly related to prognosis. Patients in the high-risk group had a lower survival rate compared with the low-risk group in the training and testing sets (both p-values < 0.05) and the area of the ROC curve was 0.737 and 0.68, respectively. CONCLUSIONS: The study illustrated a significant lncRNA signature and indicated the risk score Cox model could be an important biomarker to predict the prognosis of bladder cancer.