BackgroundFerroptosis is a newly defined cell death process triggered by increased iron load and tremendous lipid reactive oxygen species (ROS). Oxidative stress-related ferroptosis is of great important to the occurrence and progression of clear cell renal cell carcinoma (ccRCC), which is particularly susceptibility to ferroptosis agonist. Therefore, exploring the molecular features of ferroptosis and oxidative stress might guide the clinical treatment and prognosis prediction for ccRCC patients.MethodsThe differentially expressed ferroptosis and oxidative stress-associated genes (FPTOSs) between normal renal and ccRCC tissues were identified based on The Cancer Genome Atlas (TCGA) database, and those with prognostic significances were applied to develop a prognostic model and a risk scoring system (FPTOS_score). The clinical parameter, miRNA regulation, tumor mutation burden (TMB), immune cell infiltration, immunotherapy response, and drug susceptibility between two FPTOS-based risk stratifications were determined.ResultsWe have identified 5 prognosis-associated FPTOSs (ACADSB, CDCA3, CHAC1, MYCN, and TFAP2A), and developed a reliable FPTOS_socre system to distinguish patients into low- and high-risk groups. The findings implied that patients from the high-risk group performed poor prognoses, even after stratified analysis of various clinical parameters. A total of 30 miRNA-FPTOS regulatory pairs were recognized to identify the possible molecular mechanisms. Meanwhile, patients from the high-risk group exhibited higher TMB levels than those from the low-risk groups, and the predominant mutated driver genes were VHL, PBRM1 and TTN in both groups. The main infiltrating immune cells of high- and low-risk groups were CD8+ T cells and resting mast cells, respectively, and patients from the high-risk groups showed preferable drug responsiveness to anti-PD-1 immunotherapy. Eventually, potential sensitive drugs (cisplatin, BI-D1870, and docetaxel) and their enrichment pathways were identified to guide the treatment of ccRCC patients with high-risk.ConclusionOur study comprehensively analyzed the expression profiles of FPTOSs and constructed a scoring system with considerable prognostic value, which would supply novel insights into the personalized treatment strategies and prognostic evaluation of ccRCC patient.

Immune-cell infiltration and tumor-related immune molecules play a key role in tumorigenesis and progression. It remains to be systematically studied how immune interactions influence clear cell renal cell carcinoma (ccRCC) molecular characteristics and prognosis. A machine learning algorithm was applied to transcriptome data from the Cancer Genome Atlas (TCGA) database in order to determine the immunophenotypic and immunological characteristics of ccRCC patients. These algorithms included single-sample gene set enrichment analyses and cell type identification. By using bioinformatics techniques, we examined the prognostic potential and regulatory networks of immune-related genes (IRGs) involved in ccRCC immune interactions. Fifteen IRGs (CCL7, CHGA, CMA1, CRABP2, IFNE, ISG15, NPR3, PDIA2, PGLYRP2, PLA2G2A, SAA1, TEK, TGFA, TNFSF14, and UCN2) were identified as prognostic IRGs associated with overall survival and were applied to construct a prognostic model. According to further analysis, the area under the receiver operating characteristic curve at one year was 0.927, but at three years was 0.822, and at five years, it was 0.717, indicating good predictive accuracy. It was also discovered that ccRCC immune interactions are governed by molecular regulatory networks. Additionally, we developed a nomogram containing the model and clinical characteristics with high prognostic potential. By systematically examining the sophisticated regulatory mechanisms, molecular characteristics, and prognostic potential of ccRCC immune interactions, we have provided an important framework for understanding ccRCC's molecular mechanisms and identifying new prognostic markers and therapeutic targets for future research.