Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes

Abstract

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs).Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases.Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model.Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p <0.049; HR = 1.84; 95% CI = 1.02–3.32) and the training set (p < 0.001; HR = 3.12, 95% CI = 2.23–4.37). The numbers of regulatory T cells (Tregs) were significantly positively correlated with the six immunity-related genes identified, with correlation coefficients were 0.385, 0.415, 0.399, 0.451, 0.485, and 0.333, respectively (p <0.001).Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.

A novel prognostic predictor of immune microenvironment and therapeutic response in clear cell renal cell carcinoma based on angiogenesis–immune-related gene signature

“…Li et al constructed an immune-related prognostic model using kidney renal papillary cell carcinoma (KIRP) as an independent validation set. In this validation cohort, the AUC was 0.601, 0.571, and 0.518 at 1, 3, and 5 years, respectively [ 55 ], demonstrating the superior diagnostic performance of our risk score model.…”

Identification of a Combined Immune- and Metabolism- Related Prognostic Signature for Clear Cell Renal Cell Carcinoma

“…To investigate the predictive performance of the model that associates with immune system and metabolism, we conducted a comparison with ten prognostic signatures that were previously published within the last three years. These included ve immune-related prognostic models, namely Immune-related_Cao, Immune-related_Lin, Immune-related_Liu, Immune-related_Wang, and Immune-related_Zhang, as well as ve metabolism-related prognostic models, involving Metabolism-related_Guo, Metabolism-related_Huang, Metabolism-related_Tan, Metabolism-related_Wang, and Metabolism-related_Wei [14][15][16][17][18][19][20][21][22][23]. In order to ensure comparability of the signatures, we utilized the same method for computing and converting the Risk Scores (RS) across TCGA-KIRC cohort.…”

“…Moreover, oncometabolites have been found to have extracellular roles in the TME that go beyond their established intracellular functions, facilitating intercellular communication [13]. As a result, several prognostic signatures have been developed based on metabolism or immunity to aid in predicting the prognosis of ccRCC patients and guiding immunotherapy, yielding promising outcomes [14][15][16][17][18][19][20][21][22][23]. However, ccRCC is not just a highly metabolic tumor, but also one with high immunogenicity, characterized by substantial immune cell in ltration.…”