ArticleBiochemistry and biophysics reports2025
Integrated machine learning identifies disulfidptosis-related and ferroptosis-related genes to evaluate survival prognosis and treatment efficacy in kidney renal clear cell carcinoma.
Article in Biochemistry and biophysics reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Disulfidptosis: Mechanisms, evidence boundaries, and translational opportunities.Redox biology · 2026Review
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8 authors.
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Abstract
Background: Ferroptosis and disulfidptosis, two programmed cell death pathways, critically drive tumor growth by affecting metastasis. Although the prognostic value of disulfidptosis and ferroptosis had been separately validated in kidney renal clear cell carcinoma (KIRC), prognostic effect of integrating two programmed death genes remains unclear in KIRC. Our objective is to establish an innovative prognostic model for KIRC. Methods: We sourced KIRC patients' information that contains clinical and genomic from The Cancer Genome Atlas (TCGA) database. We selected disulfidptosis-related and ferroptosis-related genes (DRFs) to construct a prognostic model. By combining clinical features and prognostic models, we developed the nomogram. Additionally, the mechanism of DRF was explored in KIRC, including tumor immune dysfunction and exclusion (TIDE), Kaplan-Meier (K-M) analysis, tumor microenvironment (TME) analysis, and more. Drug sensitivity analysis shows which drugs are sensitive to tumors. Experiment with RT-PCR to confirm DRFs gene expression in the cell line. Results: Constructing risk score with five DRFs, all tumor samples were categorized into high-risk group (HG) and low-risk group (LG). The HG samples demonstrated lower survival rates according to K-M survival curves. The nomogram with risk score demonstrated significant predictive value than nomogram without the risk score. TME analysis indicated that the proportion of T cells follicular helper and Tregs was higher in HG, while Macrophages M1 and Mast cells resting were higher in LG. GSEA analysis demonstrated Retinol metabolism pathway, drug metabolism other enzymes pathway, etc. were enriched in HG, while endocytosis-related pathway, neurotrophin signaling pathway, etc. were enriched in LG. TIDE analysis showed tumors in HG are more prone to immune evasion. The drug sensitivity analysis indicated that the HG is sensitive to antitumor drugs such as Cedrane and Osimertinib, while the LG is sensitive to antitumor drugs such as 5-Fluorouracil and Entinostat. RT-qPCR have confirmed expression of DRFs in KIRC cell lines. Conclusions: Our DRFs-based prognostic model and nomogram effectively predict survival and guide treatment decisions.
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