A Novel Ferroptosis-Related Gene Model for Overall Survival Predictions of Bladder Urothelial Carcinoma Patients

Abstract

Introduction: Bladder cancer is the most common urinary tract malignancy, and 90% of bladder tumors are urothelial cell carcinomas. Ferroptosis is a new form of cell death discovered in recent years, which is an iron-dependent form of cell death characterized by the lethal intracellular accumulation of lipid-based reactive oxygen species. Ferroptosis is considered to be a double-edged sword for cancer and cancer therapy.

Materials and methods: In the current study, expression profiles of bladder cancer (BLCA) specimens were obtained from The Cancer Genome Atlas (TCGA) RNA-Seq database. Ferroptosis-related genes were downloaded from the FerrDb website. The ferroptosis-related differentially expressed genes (DEGs) which were related to overall survival (OS) were first identified. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression methods were utilized to develop a ferroptosis-related prognostic model (FRPM). In addition, a nomogram model based on FRPM and clinicopathological features was successfully constructed and validated. In addition, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) methods were utilized in this study in order to compare the DEGs between the high-risk and low-risk groups. This study also adopted RT-qPCR, CCK-8 assay, and scratch assay methods to perform experimental verification processes.

Results and discussion: A 7-gene FRPM was constructed in this research investigation in order to stratify the patients into two groups according to their risk scores. The results of this study's survival analysis and time-dependent receiver operating characteristic (ROC) analysis demonstrated that the model had achieved a stable performance level. This multivariate Cox regression results revealed that the FRPM was an independent prognostic predictor for the OS of BLCA patients and the results were displayed using a nomogram. In addition, the ROC analysis, concordance index (C-index), calibration plots, and decision curve analysis (DCA) curves further indicated that this study's nomogram method enabled valuable prediction results. The functional enrichment analysis results suggested that the DEGs between the high- and low-risk groups played vital roles in the progression of the ferroptosis. Also, the ssGSEA indicated that the immune status was different between the two groups. This study found that the RT-qPCR results had confirmed the differential expressions of DEGs in the tissue samples, and the CCK-8 assay and scratch assay results confirmed the promoting effects of SCD on the proliferation and migration of tumor cells.

Conclusions: This study defined a novel prognostic model of seven ferroptosis-related genes, which proved to be independently associated with the OS of BLCA. A nomogram method was developed for the purpose of providing further insight into the accurate predictions of BLCA prognoses.

Keywords: TCGA; bladder neoplasms; ferroptosis; nomogram; prognostic model.

Figure 1

Flow chart of the data collection and analysis processes.

Figure 2

Identification of prognostic ferroptosis-related differentially expressed genes. (A) Venn diagram for identifying ferroptosis-related DEGs related to OS. (B) Expressions of thirteen overlapping genes, in which the upregulated and downregulated DEGs were indicated in red and blue, respectively, and N and T represented adjacent normal samples and tumor samples, respectively.

Figure 3

Construction of the FRPM. (A) LASSO Cox regression model applied to screen key genes, and partial likelihood deviance with 10-fold cross-validation was utilized to calculate the best lambda. (B) Distributions and median value of the risk scores in BLCA patients. (C) Distributions of the survival status values. (D) Kaplan-Meier curves for the seven genes relative to the overall survival outcomes. (E) AUC of the time-dependent ROC curve showing the predictive efficiency. (F, G) PCA plot and t-SNE analysis.

Figure 4

Independent prognostic values of the FRPM. (A) Correlations between the risk groups and the clinical traits. (B, C) Univariate and multivariate regression analysis results of the relationships between the FRPM and the clinical features. *P < 0.05, ***P < 0.001.

Figure 5

Development and validation of a prognostic nomogram model. (A) Nomogram for predicting the probability of 1-, 3-, and 5-year OS. (B) Time-dependent ROC curves of the nomogram. (C) Calibration curves of the nomogram for predicting the probability of OS in 1-, 3-, and 5-year timeframes. (D–F) Decision curve analysis for the OS in BLCA patients at 1, 3, and 5 years, respectively.

Figure 6

Enrichment analysis and ssGSEA scores. (A, B) GO and KEGG analyses. (C, D) Comparison of the ssGSEA scores between the different risk groups: Scores of the 16 immune cells and Scores of the 13 immune-related functions. *P < 0.05, **P < 0.01, ***P < 0.001, ns, no significance.

Figure 7

Experimental verification processes. (A) Marplot exhibiting the relative expressions of the seven genes evaluated by RT-qPCR in 16 bladder cancer samples and paired normal samples. (B) Relative expression levels of SCD in eight cell lines. (C, D) CCK-8 assay results show the relative proliferation of the J82 and RT4 cells after the addition of A939572. (E, F) Scratch assay results showed migration rates of the J82 and RT4 cells. In the figure, the data were shown as means ± S.D. *P < 0.05, **P < 0.01, ***P < 0.001.