Prognostic value of iron metabolism-related genes in bladder urothelial carcinoma

Abstract

Abstract Objectives Iron metabolism-related genes (IMRGs) play important roles in the prognostic assessment of many tumours. However, IMRGs have not been reported as prognostic biomarkers in bladder urothelial carcinoma (BLCA). Methods Gene expression profiles and clinical data from BLCA patients were obtained from The Cancer Genome Atlas (TCGA) database. We used the DESeq2 package to screen for differentially expressed genes (DEGs). The predictive values of the differentially expressed IMRGs in BLCA patients were further evaluated using univariate Cox regression analysis. The risk-scoring model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm. The performance of this model for predicting the prognosis of BLCA patients in TCGA-BLCA cohort was assessed using Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curves. This risk-scoring model was combined with the clinicopathological characteristics of BLCA patients in a multiple regression analysis, and a nomogram was constructed using the independent predictors identified. ROC analysis and calibration curves were adapted to test the predictive ability of the nomogram. Gene set enrichment analysis (GSEA) was used to identify potential molecular pathways and processes enriched by differential expression genes between risk groups. Finally, we explored the ability of the risk-scoring model to assess immune cell infiltration levels through a correlation analysis. Results Fourteen identified IMRGs with prognostic value were incorporated into the risk-scoring model. The ROC and K–M survival curves indicated that the model could effectively predict the overall survival (OS) outcomes of BLCA patients. The multiple regression analysis revealed that the risk-scoring model could be used as an independent prognostic factor for BLCA patients, and the associated nomogram could effectively predict the OS outcomes of BLCA patients. GSEA revealed that the DEGs between the risk groups were mainly involved in biological processes such as developmental process, cell cycle, mitosis, RHO GTPase reaction, DNA repair, and extracellular matrix regulation. The immune infiltration analysis showed that the infiltration levels of immune cells such as natural killer cells, memory T cells, effector T cells, Th2 cells, and macrophages differed significantly between the risk groups. Conclusions IMRGs screening revealed prognosis-associated genes. The prognostic model constructed could effectively predict the prognosis of BLCA patients, and the identified genes represent potential targets for BLCA treatment.