Bladder urothelial carcinoma (BLCA) is recognized to be immunogenic and tumorigenic. This study identified a novel long noncoding RNA (lncRNA) signature for predicting survival for patients with BLCA. A univariate Cox regression model and the random survival forest-variable hunting (RSF-VH) algorithm were employed to achieve variable selection. Ten lncRNAs (LOC105375787, CYTOR, URB1-AS1, C21orf91-OT1, CASC15, LOC101928433, FLJ45139, LINC00960, HOTAIR and TTTY19) with the highest prognostic values were identified to establish the prognostic model. The nomogram integrating the signature and clinical factors showed high concordance index values of 0.94, 0.7 and 0.90 in the three datasets, and the calibration curves showed concordance between the predicted and observed 3- and 5-year survival rates. The risk score based on the 10-lncRNA signature accurately distinguished high- and low-risk BLCA patients with different disease-specific survival(DSS) or overall survival(OS) outcomes, which were stratified according to clinical factors, including T stage and tumour grade. Gene set enrichment analysis identified BLCA-specific biological pathways and enriched functional categories, such as the cell cycle, DNA repair and immune system. Furthermore, the increased infiltration of immune cells in the high-risk group indicated that lncRNA-related inflammation may reduce the survival of BLCA patients.

BackgroundThis study aimed to develop a prognostic signature based on immune related gene(IRG) predicting survival for patients with bladder urothelial carcinoma(BLCA).Methods1534 IRG’s expression data of 996 BLCA patients form Gene Expression Omnibus database and The Cancer Genome Atlas database were used for development and validation of the prognostic signature. Univariate Cox regression model and Random Survival Forest Variable Hunting algorithm were employed to achieve the variable selection. The independently prognostic ability of the signature was validated by Multivariate Cox model and Kaplan–Meier analysis in independent datasets. A nomogram was established to improve prognosis stratification. The relationship between tumor-infiltrating immune cells and the signature was analyzed using data retrieved from EPIC resource.FindingsA prognostic model consisting of 10 IRGs was developed as our immune signature. Based on this signature, patients were separated into low- and high- risk groups with different survival in both training and validation sets(HR : range from 1.1 [95% CI: 1–1.2; p = 0.038] to 1.3 [95% CI: 1.3–1.5; p <0.001]). Multivariable analyses demonstrated that this signature was an independent prognostic predictor and was strongly associated with important clinicopathological factors. The signature also showed a significantly positive correlation with immune checkpoint molecules, and had a superior prognostic value compared with some important targets. In addition, our signature was found to correlate the enhancement of tumor microenvironment positively.ConclusionsThis signature predicts prognosis for BLCA patients, which may promote individualized treatment and provide potential targets for immunotherapy.