Integrated risk scores from N6-methyladenosine-related lncRNAs are potential biomarkers for predicting the overall survival of bladder cancer patients

Background: N6-methyladenosine (m6A) is the most common form of mRNA- and long noncoding RNA (lncRNA)-specific internal modification encountered in eukaryotes, with important effects on mRNA stability, translation, and splicing. The role of m6A-modified lncRNAs (m6A-lncRNAs) in bladder cancer (BLCA) is rarely reported. This study aimed to evaluate an efficient prognostic model of BLCA in patients, based on m6A-lncRNAs, and to discover potential biological targets. Methods: Differentially expressed lncRNAs were investigated in 433 BLCA samples derived from The Cancer Genome Atlas (TCGA) database. Kaplan–Meier and univariate Cox regression analyses were performed to screen for m6A-lncRNAs with prognostic roles in BLCA. We implemented Pearson correlation analysis to analyze 18 potentially prognostic lncRNAs and 20 known m6A-associated genes. Next, the data were imputed using least absolute shrinkage and selection operator (LASSO) Cox regression to establish an m6A-lncRNA prognostic signature. Results: We established an integrated risk score (RS) containing five m6A-lncRNAs and constructed a nomogram that had the ability to forecast the overall survival (OS) of patients with BLCA. We showed that the predictive accuracy of the RS for BLCA prognosis was high, which was confirmed by the area under the receiver operating characteristic (ROC) curve. We analyzed the correlation between tumor immune infiltrating cells and RS in high- and low-risk patients with BLCA and used tumor immune dysfunction and exclusion to predict the effect of immunotherapy. We screened out the most relevant modules of RS through the weighted gene co-expression network analysis network and explored their potential biological functions using GO and KEGG analyses. Conclusion: Our findings demonstrate that, compared with nomograms constructed using a single prognostic factor, the integrated RS represents a superior model for predicting survival in patients with BLCA, which may improve the clinical management of BLCA.