A united risk model of 11 immune‑related gene pairs and clinical stage for prediction of overall survival in clear cell renal cell carcinoma patients

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer. Currently, we lack effective risk models for the prognosis of ccRCC patients. Given the significant role of cancer immunity in ccRCC, we aimed to establish a novel united risk model including clinical stage and immune-related gene pairs (IRGPs) to assess the prognosis. The gene expression profile and clinical data of ccRCC patients from The Cancer Genome Atlas and Arrayexpress were divided into training cohort (n = 381), validation cohort 1 (n = 156), and validation cohort 2 (n = 101). Through univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator analysis, 11 IRGPs were obtained. After further analysis, it was found that clinical stage could be an independent prognostic factor; hence, we used it to construct a united prognostic model with 11 IRGPs. Based on this model, patients were divided into high-risk and low-risk groups. In Kaplan-Meier analysis, a significant difference was observed in overall survival (OS) among all three cohorts (p < 0.001). The calibration curve revealed that the signature model is in high accordance with the observed values of each data cohort. The 1-year, 3-year, and 5-year receiver operating characteristic curves of each data cohort showed better performance than only IRGP signatures. The results of immune infiltration analysis revealed significantly (p < 0.05) higher abundance of macrophages M0, T follicular helper cells, and other tumor infiltrating cells. In summary, we successfully established a united prognostic risk model, which can effectively assess the OS of ccRCC patients.

Metabolism‐related gene vaccines and immune infiltration in ovarian cancer: A novel risk score model of machine learning

“…Wang et al, 29,30 utilizing computational biology methods such as WGCNA, have elucidated the biomarkers in different tumors, thereby providing a methodological foundation for investigating the mechanisms underlying tumor development. Tao et al, 31 employing bioinformatics methods such as LASSO, have elucidated the roles of immune‐related genes in tumors, providing a methodological foundation for investigating the mechanisms underlying tumor development.…”

Integrative analysis of cuproptosis‐associated genes for predicting immunotherapy response in single‐cell and multi‐cohort studies

“…Wang et al, using WGCNA and other computational biology methods, have elucidated the biomarkers in different tumors, thereby providing a methodological foundation for investigating the mechanisms underlying tumor development 39,40 . Tao et al, employing bioinformatics methods such as LASSO, have elucidated the roles of immune‐related genes in tumors, providing a methodological foundation for investigating the mechanisms underlying tumor development 41 . Their research methods have inspired us, and we have referred to and adopted their analytical methods in conducting relevant analyses for our research.…”

“…39,40 Tao et al, employing bioinformatics methods such as LASSO, have elucidated the roles of immune-related genes in tumors, providing a methodological foundation for investigating the mechanisms underlying tumor development. 41 Their research methods have inspired us, and we have referred to and adopted their analytical methods in conducting relevant analyses for our research.…”

Identification and validation of an immune-related gene pairs signature for three urologic cancers

“…It is possible that the dysregulated immune contexture may result in the survival differences observed between risk groups as defined by the IRGPI. Noteworthily, the infiltration level of M1 macrophages in the high-risk and low-risk group are inconsistent in researches [ 49 , 50 , 58 – 61 ]. Of these studies, two of them are consistent with our result [ 50 , 58 ], three of them report that there is no significant difference [ 59 – 61 ], and only one research is contrary to ours [ 49 ].…”

“…Noteworthily, the infiltration level of M1 macrophages in the high-risk and low-risk group are inconsistent in researches [ 49 , 50 , 58 – 61 ]. Of these studies, two of them are consistent with our result [ 50 , 58 ], three of them report that there is no significant difference [ 59 – 61 ], and only one research is contrary to ours [ 49 ]. The inconsistencies in the infiltration level of M1 macrophages may be related to study population differences, that is, the samples that generate risk scores are different.…”