Heliyon (May 2023)
A potential biological signature of 7-methylguanosine-related lncRNA to predict the immunotherapy effects in bladder cancer
Abstract
Background: Bladder urothelial carcinoma (BLCA) is the second prevalent genitourinary carcinoma globally. N7-methylguanosine (m7G) is important for tumorigenesis and progression. This study aimed to build a predictive model for m7G-related long non-coding RNAs (lncRNAs), elucidate their role in the tumor immune microenvironment (TIME), and predict immunotherapy response in BLCA. Methods: We first used univariate Cox regression and coexpression analyses to identify m7G-related lncRNAs. Next, the prognostic model was built by utilizing LASSO regression analysis. Then, the prognostic significance of the model was examined utilizing Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, nomogram, and univariate, multivariate Cox regression. We also analyzed Gene set enrichment analyses (GSEA), immune analysis and principal component analysis (PCA) in risk groups. To further predict immunotherapy effectiveness, we evaluated the predictive ability for immunotherapy in 2 risk groups and clusters using tumor immune dysfunction and exclusion (TIDE) score and Immunophenoscore (IPS). Results: Seven lncRNAs related to m7G were used to create a model. The calibration plots for the model suggested a strong fit with the prediction of overall survival (OS). The area under the curve (AUC) for first, second, and third years was respectively, 0.722, 0.711, and 0.686. In addition, the risk score had strong correlation with TIME features and genes linked to immune checkpoint blockade (ICB). TIDE scores were dramatically different between two risk groups (p < 0.05), and IPS scores were markedly different between two clusters (p < 0.05). Conclusion: Our research constructed a novel m7G-related lncRNAs that could be used to predict patient outcomes and the effectiveness of immunotherapy in BLCA. Immunotherapy may be more effective for the low-risk group and cluster 2.
