Identification and validation of telomerase related lncRNAs signature to predict prognosis and tumor immunotherapy response in bladder cancer

Telomerase allows eukaryotic cells to proliferate indefinitely, an important characteristic of tumor cells. Telomerase-related long no coding RNAs (TERLs) are involved in prognosis and drug sensitivity prediction; however, their association with bladder cancer (BLCA) is still unreported. The objective of this research is to determine a predictive prognostic TERL signature for OS and to provide an efficient treatment option for BLCA. The RNA sequence, clinical information, and mutational data of BLCA patients were acquired from The Cancer Genome Atlas (TCGA) database. With the help of the data from least absolute shrinkage and selection operator (LASSO) regression and Cox regression, a prognostic signature was established including 14 TERLs, which could divide BLCA patients into low-risk (L-R) and high-risk (H-R) cohorts. The time-dependent receiver operating characteristic (ROC) curve demonstrated the greater predictive power of the model. By combing the TERLs-based signature and clinical risk factors (age, sex, grade, and stage), a prognostic nomogram was constructed to forecast the survival rates of patients with BLCA at 1-, 3-, and 5-years, which was well matched by calibration plots C-index and Decision curve analysis (DCA). Furthermore, the L-R cohort showed higher tumor mutation burden (TMB) and lower tumor immune dysfunction and exclusion (TIDE) than the H-R cohort, as well as substantial variability in immune cell infiltration and immune function between the two cohorts was elucidated. As for external validation, LINC01711 and RAP2C-AS1 were identified as poor prognostic factors by survival analysis from the Kaplan-Meier Plotter database, which were validated in BLCA cell lines (EJ, 253J, T24, and 5637) and SV-HUC-1 cells as the control group using qRT-PCR. In addition, interference with the expression of RAP2C-AS1 suppresses the proliferation and migration of BLCA cells, and RAP2C-AS1 could affect the expression of CD274 and CTLA4, which could serve as prognostic markers and characterize the tumor microenvironment in BLCA. Overall, the model based on the 14-TERLs signature can efficiently predict the prognosis and drug treatment response in individuals with bladder cancer.