Chaoting Zhou1, Alex Heng Li1, Shan Liu1, Hong Sun1. 1. Department of Environmental Medicine, New York University School of Medicine, 341 East 25 Street, New York, NY 10010, USA.
Abstract
BACKGROUND: Survival rates for highly invasive bladder cancer (BC) patients have been very low, with a 5-year survival rate of 6%. Accurate prediction of tumor progression and survival is important for diagnosis and therapeutic decisions for BC patients. Our study aims to develop an autophagy-related-gene (ARG) signature that helps to predict the survival of BC patients. METHODS: RNA-seq data of 403 BC patients were retrieved from The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) database. Univariate Cox regression analysis was performed to identify overall survival (OS)-related ARGs. The Lasso Cox regression model was applied to establish an ARG signature in the TCGA training cohort (N = 203). The performance of the 11-gene ARG signature was further evaluated in a training cohort and an independent validation cohort (N = 200) using Kaplan-Meier OS curve analysis, receiver operating characteristic (ROC) analysis, as well as univariate and multivariate Cox regression analysis. RESULTS: Our study identified an 11-gene ARG signature that is significantly associated with OS, including APOL1, ATG4B, BAG1, CASP3, DRAM1, ITGA3, KLHL24, P4HB, PRKCD, ULK2, and WDR45. The ARGs-derived high-risk bladder cancer patients exhibited significantly poor OS in both training and validation cohorts. The prognostic model showed good predictive efficacy, with the area under the ROC curve (AUCs) for 1-year, 3-year, and 5-year overall survival of 0.702 (0.695), 0.744 (0.640), and 0.794 (0.658) in the training and validation cohorts, respectively. A prognostic nomogram, which included the ARGs-derived risk factor, age and stage for eventual clinical translation, was established. CONCLUSION: We identified a novel ARG signature for risk-stratification and robust prediction of overall survival for BC patients.
BACKGROUND: Survival rates for highly invasive bladder cancer (BC) patients have been very low, with a 5-year survival rate of 6%. Accurate prediction of tumor progression and survival is important for diagnosis and therapeutic decisions for BC patients. Our study aims to develop an autophagy-related-gene (ARG) signature that helps to predict the survival of BC patients. METHODS: RNA-seq data of 403 BC patients were retrieved from The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) database. Univariate Cox regression analysis was performed to identify overall survival (OS)-related ARGs. The Lasso Cox regression model was applied to establish an ARG signature in the TCGA training cohort (N = 203). The performance of the 11-gene ARG signature was further evaluated in a training cohort and an independent validation cohort (N = 200) using Kaplan-Meier OS curve analysis, receiver operating characteristic (ROC) analysis, as well as univariate and multivariate Cox regression analysis. RESULTS: Our study identified an 11-gene ARG signature that is significantly associated with OS, including APOL1, ATG4B, BAG1, CASP3, DRAM1, ITGA3, KLHL24, P4HB, PRKCD, ULK2, and WDR45. The ARGs-derived high-risk bladder cancerpatients exhibited significantly poor OS in both training and validation cohorts. The prognostic model showed good predictive efficacy, with the area under the ROC curve (AUCs) for 1-year, 3-year, and 5-year overall survival of 0.702 (0.695), 0.744 (0.640), and 0.794 (0.658) in the training and validation cohorts, respectively. A prognostic nomogram, which included the ARGs-derived risk factor, age and stage for eventual clinical translation, was established. CONCLUSION: We identified a novel ARG signature for risk-stratification and robust prediction of overall survival for BC patients.
Entities:
Keywords:
The Cancer Genome Atlas; autophagy; bladder cancer; gene signature; overall survival
Authors: J Alfred Witjes; Harman Max Bruins; Richard Cathomas; Eva M Compérat; Nigel C Cowan; Georgios Gakis; Virginia Hernández; Estefania Linares Espinós; Anja Lorch; Yann Neuzillet; Mathieu Rouanne; George N Thalmann; Erik Veskimäe; Maria J Ribal; Antoine G van der Heijden Journal: Eur Urol Date: 2020-04-29 Impact factor: 20.096