Immunogenic cell death related genes predict prognosis and tumor microenvironment characteristics in patients with renal papillary carcinoma

Objective: To explore the predictive value of genes related to immunogenic cell death (ICD) for the prognosis and tumor microenvironment characteristics of patients with renal papillary carcinoma(RCC). Methods: Transcriptome data of RCC were downloaded from The Cancer Genome Atlas databases. We identified differentially expressed ICDs between RCC tissues and normal tissues with R software.We analyzed gene expression data from 291 patients with RCC, combined with clinical pathological data, and used statistical methods to evaluate the prognostic value of genes related to ICD. In addition, we also studied the relationship between these genes and the characteristics of the tumor microenvironment.Quantitative RT-PCR was employed to validate the expression levels of the key genes from the signature set. Results: Distinct expression patterns of ICD-associated genes in RCC were identified, and a Protein-Protein Interaction (PPI) network was mapped. Consistency clustering analysis classified patients into high and low ICD expression groups, with the high expression group exhibiting favorable clinical outcomes. Signaling pathways enriched in immune-related activities were associated with high ICD expression. Somatic mutation profiling revealed common mutations, and immune cell infiltration analysis demonstrated distinct immunological characteristics in the high ICD expression group. We identified 14 differentially expressed ICDs associated with the prognosis, 8 (CALR, EIF2AK3, IFNB1, IFNG, IL1R1, IL6, LY96, PDIA3) of which were selected to construct a ICDs signature.The relative expression levels of ICD genes were significantly higher in RCC tumor samples than paracancerous tissue. The prognostic risk model exhibited predictive performance and consistency in both training and validation datasets. Univariate and multivariate analyses identified the risk score as an independent prognostic factor. A nomogram incorporating clinical features and risk score accurately predicted patient survival rates. Conclusion: This study identifies ICD-related genes as potential prognostic markers in RCC, offering insights into the immunological characteristics associated with distinct ICD expression profiles. The constructed risk model and nomogram provide valuable tools for predicting survival outcomes and guiding personalized therapeutic interventions in RCC patients.