Abstract
BACKGROUND: The prognosis of patients with kidney renal clear cell carcinoma (KIRC), a life-threatening condition, is poor. Immunogenic cell death (ICD) induces regulated cell death via immunogenic signal secretion and exposure. ICD induces regulated cell death through immunogenic signal secretion and exposure. ICD plays an essential role in tumorigenesis, however, the role of ICD in KIRC remains unclear. METHODS: This study examined the expression levels of 34 ICD-related genes in The Cancer Genome Atlas (TCGA) data set. Signature genes linked to KIRC survival were identified using Cox regression. Next, a prognostic risk model (RM) was built. Subsequently, the KIRC patients were divided into low- and high-risk groups. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were plotted. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were carried out to investigate the possible role of differential gene expression between the two groups. The immune microenvironment (IME) was assessed using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression, CIBERSORT, and single-sample gene-set enrichment analysis algorithms. An enrichment analysis was used to determine the biological significance of these regulatory networks we conducted. The relationship between immune checkpoint gene expression and risk score, and the relationship between treatment outcome and gene expression were assessed using correlation analyses. RESULTS: We developed a KIRC RM based on five ICD-related genes (i.e., FOXP3, IFNB1, IL6, LY96, and TLR4), which were identified as the prognostic signature genes. Using the TCGA data set, we conducted a survival analysis and found that the 3-year RM had an area under the curve (AUC) of 0.735, which validated the reliability of the signature. Similarly, using the International Cancer Genome Consortium (ICGC) data set, we found that the 3-year RM had an AUC of 0.732. CONCLUSIONS: A RM based on five ICD-related genes was built to predict the prognosis of KIRC patients. This RM predicted patient prognosis and reflected the tumor IME of KIRC patients. Thus, this RM could be used to promote individualized treatments and provide potential novel targets for immunotherapy.