A Novel DNA Methylation Signature as an Independent Prognostic Factor in Muscle-Invasive Bladder Cancer

Front Oncol. 2021 Feb 15:11:614927. doi: 10.3389/fonc.2021.614927. eCollection 2021.

Abstract

Background: Muscle-invasive bladder cancer (MIBC) accounts for approximately 20% of all urothelial bladder carcinomas (UBC) at time of diagnosis, and up to 30% of patients with non-muscle invasive UBC will progress to MIBC over time. An increasing body of evidence has revealed a strong correlation between aberrant DNA methylation and tumorigenesis in MIBC.

Results: Using The Cancer Genome Atlas (TCGA) molecular data for 413 patients, we described a DNA methylation-based signature as a prognostic factor for overall survival (OS) in MIBC patients. By using a least absolute shrinkage and selection operator (LASSO) model, differentially methylated regions were first identified using multiple criteria followed by survival and LASSO analyses to identify DNA methylation probes related to OS and build a classifier to stratify patients with MIBC. The prognostic value of the classifier, referred to as risk score (RS), was validated in a held-out testing set from the TCGA MIBC cohort. Finally, receiver operating characteristic (ROC) analysis was used to compare the prognostic accuracy of the models built with RS alone, RS plus clinicopathologic features, and clinicopathologic features alone. We found that our seven-probe classifier-based RS stratifies patients into high- and low-risk groups for overall survival (OS) in the testing set (n = 137) (AUC at 3 years, 0.65; AUC at 5 years, 0.65). In addition, RS significantly improved the prognostic model when it was combined with clinical information including age, smoking status, Tumor (T) stage, and Lymph node metastasis (N) stage.

Conclusions: The DNA methylation-based RS can be a useful tool to predict the accuracy of preoperative and/or post-cystectomy models of OS in MIBC patients.

Keywords: DNA methylation marker; MIBC; ROC; bladder cancer (BC); methylation and prognosis; survival analysis.