Abstract

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 [32]GSE213324 and
   [33]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.

Supplementary Information

   The online version contains supplementary material available at
   10.1007/s12672-025-02202-1.

   Keywords: Ferroptosis, Prognostic model, CcRcc, Immune

Introduction

   Cancer-related human mortality remains a challenging issue on a global
   scale, with important implications for overall survival rates [[34]1].
   As the urological tumor, ccRCC stands out as the most common malignant
   tumor in renal cancer, and its incidence and mortality rates continue
   to rise [[35]2]. According to the Global Cancer Statistics 2020, the
   number of new cases will be about 430,000, with 180,000 deaths [[36]3].
   Furthermore, ccRcc is characterized by high invasiveness and metastatic
   potential, while patients exhibit low resistance to radiotherapy and
   poor prognosis [[37]4]. Therefore, more accurate prognostic indicators
   are needed for clinicians to diagnose and treat the disease, which will
   help patients to improve their survival rates and living conditions.

   Programmed cell death (PCD) is crucial for physiological development,
   maintenance of homeostasis, and regulation of disease development
   [[38]5]. Ferroptosis is an iron-dependent mode of PCD, resulting from
   the excessive accumulation of lipid peroxides due to disturbances in
   intracellular metabolic pathways, which is intimately related to
   intracellular iron metabolism and lipid homeostasis [[39]6, [40]7] and
   distinguishes it from other forms of death such as apoptosis, necrosis,
   autophagy. In recent years, a growing body of evidence has demonstrated
   the association between ferroptosis and the development and advancement
   of malignant tumors including ccRCC, hepatocellular carcinoma (HCC),
   and colorectal cancer [[41]8], underscoring its significance in tumor
   therapy. Consequently, searching for more ferroptosis-related
   biomarkers has a profound importance for the prognosis and treatment of
   patients with ccRcc. For example, cysteine protease inhibitor SN (CST1)
   could regulate the protein stability of glutathione peroxidase 4 (GPX4)
   through OTUB1 and inhibit the ferroptosis of gastric cancer cells,
   which in turn promoted the metastasis of gastric cancer to the liver,
   lungs and peritoneum [[42]9]. High expression of CST1 was associated
   with a poor prognosis for patients [[43]9]. Otherwise, many other
   studies have reported the outcome of ferroptosis and ccRCC. Augmented
   circular RNA zinc finger with KRAB and SCAN domains 1 (circZKSCAN1)
   expression facilitated ccRCC development and accelerated tumor
   progression by targeting the miR-1294/ Pim-1 proto-oncogene,
   serine/threonine kinase (PIM1) axis [[44]10]. It has also been found
   that Krüppel-like factor 11(KLF11) suppressed the progression of ccRCC
   by increasing the transcription of nuclear receptor coactivator 4
   (NCOA4), which may be a therapeutic target for ccRCC [[45]11].
   Moreover, a significant correlation between ferroptosis and ccRcc
   prognosis was found recently [[46]12–[47]14]. These findings suggested
   that ferroptosis plays important roles in the development and
   progression of ccRCC and is closely linked to the prognosis of ccRcc
   patients. However, with the limited available research in this area,
   the relationship between ccRCC prognosis and FRGs remains unclear.
   Therefore, identifying for ferroptosis-related markers in ccRCC may
   further enhance our understanding of the role of ferroptosis in ccRCC,
   as well as show very important roles in prognosis and treatment of
   patient.

   Although the research has reported the association of FRGs with the
   prognosis of certain cancers, there is limited information on the
   clinical prognostic relevance of FRGs for ccRCC. In this study, a
   multi-gene prognostic model related to ferroptosis was screened and
   established based on data from TCGA and the ferroptosis database
   (FerrDb), which could independently predict the prognosis of ccRCC
   patients. These findings provide theoretical references for the