Construction of an interferon regulatory factors-related risk model for predicting prognosis, immune microenvironment and immunotherapy in clear cell renal cell carcinoma

Interferon regulatory factors (IRFs) played complex and essential roles in progression, prognosis, and immune microenvironment in clear cell renal cell carcinoma (ccRCC). The

In brief, a robust and effective risk model was developed to predict prognosis, TME characteristics and responses to immunotherapy and targeted drugs in ccRCC, which might provide new insights into personalized and precise therapeutic strategies.

Multi-omics analysis of IRFs in ccRCC was performed based on bulk RNA sequencing and single cell RNA sequencing data. According to the expression profiles of IRFs, the ccRCC samples were clustered by non-negative matrix factorization (NMF) algorithm. Then, least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were applied to construct a risk model to predict prognosis, immune cells infiltration, immunotherapy response and targeted drug sensitivity in ccRCC. Furthermore, a nomogram comprising the risk model and clinical characteristics was established.

Two molecular subtypes with different prognosis, clinical characteristics and infiltration levels of immune cells were identified in ccRCC. The IRFs-related risk model was developed as an independent prognostic indicator in the TCGA-KIRC cohort and validated in the E-MTAB-1980 cohort. The overall survival of patients in the low-risk group was better than that in the high-risk group. The risk model was superior to clinical characteristics and the ClearCode34 model in predicting the prognosis. In addition, a nomogram was developed to improve the clinical utility of the risk model. Moreover, the high-risk group had higher infiltration levels of CD8+ T cell, macrophages, T follicular helper cells and T helper (Th1) cells and activity score of type I IFN response but lower infiltration levels of mast cells and activity score of type II IFN response. Cancer immunity cycle showed that the immune activity score of most steps was remarkably higher in the high-risk group. TIDE scores indicated that patients in the low-risk group were more likely responsive to immunotherapy. Patients in different risk groups showed diverse drug sensitivity to axitinib, sorafenib, gefitinib, erlotinib, dasatinib and rapamycin. Conclusions: In brief, a robust and effective risk model was developed to predict prognosis, TME characteristics and responses to immunotherapy and targeted drugs in ccRCC, which might provide new insights into personalized and precise therapeutic strategies.