Identification of ferroptosis-related gene signatures as a novel prognostic model for clear cell renal cell carcinoma.

BACKGROUND

Clear cell renal cell carcinoma (ccRCC), a common type of renal cortical tumor, is the most prevalent subtype of renal malignancies within the urinary system and is associated with a low survival rate. Ferroptosis plays a crucial role in the process of renal carcinogenesis and holds potential for significant applications in patient prognosis. However, the clinical prognostic relevance of ferroptosis-related genes (FRGs) for ccRCC remains unclear. The identification of FRG signatures and the development of a novel prognostic model based on FRGs demonstrate important prognostic significance for ccRCC.

METHODS

Univariate cox screen was performed to screen for prognostic-related genes using ccRCC data from the The Cancer Genome Atlas (TCGA) database. And then an initial screen for prognostic genes was performed by taking intersections with the differential genes of the Gene Expression Omnibus (GEO) database datasets GSE213324 and GSE66271, as well as with the FRGs, and a multigene signature was constructed using least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. Subsequently, the model was evaluated using Kaplan-Meier (KM) survival curve analysis, receiver operating characteristic (ROC), nomogram, and decision curve analysis (DCA). Differences in tumor microenvironment and immune function were analyzed by single-sample gene set enrichment analysis (ssGSEA) and immune infiltration in patients in the high- and low-risk groups. The tumor immune dysfunction and exclusion (TIDE) assessed the immune checkpoint inhibitor (ICI) susceptibility in patients. The Gene Set Enrichment Analysis (GSEA) was performed for pathway enrichment analysis. Patient mutation data were downloaded and tumor mutation burden (TMB) were compared between patients in the high- and low-risk groups.

RESULTS

ADACSB, DPEP1, KIF20A, MT1G, PVT1 and TIMP1 were utilized to establish a novel prognostic signature. The KM curve analysis revealed that patients in the high-risk group exhibited a poorer prognosis. Additionally, the ROC results demonstrated that the model displayed favorable prognostic accuracy. Independent prognostic analyses indicated that the FRGs model could serve as an independent prognostic indicator. Furthermore, calibration curve of the nomogram illustrated enhanced precision in predicting survival rates for patients at 1, 3 and 5 years. Analysis of mutation data unveiled higher tumor mutation load among patients in the high-risk group, which correlated with an increase in risk score.

CONCLUSION

The FRGs model offers a novel approach for prognostic prediction of ccRCC patients and has the potential to provide personalized prognostic prediction and treatment for ccRCC patients.