Discover Oncology (Apr 2025)
Disulfidptosis, a novel regulated cell death to predict survival and therapeutic response in kidney renal clear cell carcinoma
Abstract
Abstract Background Metabolic regulation of cell death has become a potential therapeutic target for kidney renal clear cell carcinoma (KIRC), which is distinguished by notable heterogeneity and significant immune infiltration. Disulfidptosis, a recently identified form of cell death, has gained prominence in antitumor immunity. This research aims to investigate the correlation between disulfidptosis and prognosis of KIRC, while also exploring the possibility of predicting therapeutic response by disulfidptosis-associated genes (DAGs). Methods We sourced clinical data and RNA sequence of KIRC from the Cancer Genome Atlas Database. Employing unsupervised clustering based on 23 DAGs, we further identified key differentially expressed genes (DEGs) between clusters to construct a DAG prognostic signature. A nomogram was developed and validated to predict clinical outcome of KIRC. Finally, we examined immune cell infiltration, tumor mutational burden, immunotherapy response, and sensitivity to drugs in high and low-risk groups. Results Two distinct KIRC patient clusters were successfully stratified using the 23-DAG-related prognostic signature, comprising 11 key genes. This resulted in a robust risk model with strong predictive accuracy for overall survival. The nomogram, incorporating DAG-based risk scores, age, and pM stage, exhibited excellent predictive performance. The high-risk group displayed increased immune cell infiltration and tumor mutational burden, while the low-risk group showed heightened sensitivity to immunotherapies and targeted treatments. Conclusion This study established a robust DAG-based risk model for KIRC, highlighting its significant correlation with the immune landscape and therapeutic responses. Novel disulfidptosis-related biomarkers revealed distinct immune profiles, drug sensitivities, and immunotherapy potentials among KIRC patients.
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