A Novel Prognostic Model Based on Ferroptosis-Related Gene Signature for Bladder Cancer

• Main Authors: Libo Yang, Chunyan Li, Yang Qin, Guoying Zhang, Bin Zhao, Ziyuan Wang, Youguang Huang, Yong Yang
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-08-01
• Series: Frontiers in Oncology
• Subjects: ferroptosis; prognosis; biomarkers; bladder cancer; bladder urothelial carcinoma
• Online Access: https://www.frontiersin.org/articles/10.3389/fonc.2021.686044/full

BackgroundBladder cancer (BC) is a molecular heterogeneous malignant tumor; the treatment strategies for advanced-stage patients were limited. Therefore, it is vital for improving the clinical outcome of BC patients to identify key biomarkers affecting prognosis. Ferroptosis is a newly discovered programmed cell death and plays a crucial role in the occurrence and progression of tumors. Ferroptosis-related genes (FRGs) can be promising candidate biomarkers in BC. The objective of our study was to construct a prognostic model to improve the prognosis prediction of BC.MethodsThe mRNA expression profiles and corresponding clinical data of bladder urothelial carcinoma (BLCA) patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. FRGs were identified by downloading data from FerrDb. Differential analysis was performed to identify differentially expressed genes (DEGs) related to ferroptosis. Univariate and multivariate Cox regression analyses were conducted to establish a prognostic model in the TCGA cohort. BLCA patients from the GEO cohort were used for validation. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were used to explore underlying mechanisms.ResultsNine genes (ALB, BID, FADS2, FANCD2, IFNG, MIOX, PLIN4, SCD, and SLC2A3) were identified to construct a prognostic model. Patients were classified into high-risk and low-risk groups according to the signature-based risk score. Receiver operating characteristic (ROC) and Kaplan–Meier (K–M) survival analysis confirmed the superior predictive performance of the novel survival model based on the nine-FRG signature. Multivariate Cox regression analyses showed that risk score was an independent risk factor associated with overall survival (OS). GO and KEGG enrichment analysis indicated that apart from ferroptosis-related pathways, immune-related pathways were significantly enriched. ssGSEA analysis indicated that the immune status was different between the two risk groups.ConclusionThe results of our study indicated that a novel prognostic model based on the nine-FRG signature can be used for prognostic prediction in BC patients. FRGs are potential prognostic biomarkers and therapeutic targets.