Pan Hao, Lu Wei, Zhang Mengyuan, Liu Chengxiao
Department of Anesthesiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Department of Oral and Maxillofacial Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Front Oncol. 2023 Apr 27;13:1131191. doi: 10.3389/fonc.2023.1131191. eCollection 2023.
Interferon regulatory factors (IRFs) played complex and essential roles in progression, prognosis, and immune microenvironment in clear cell renal cell carcinoma (ccRCC). The purpose of this study was to construct a novel IRFs-related risk model to predict prognosis, tumor microenvironment (TME) and immunotherapy response in ccRCC.
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.
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.
干扰素调节因子（IRFs）在透明细胞肾细胞癌（ccRCC）的进展、预后及免疫微环境中发挥着复杂而重要的作用。本研究旨在构建一种新型的IRFs相关风险模型，以预测ccRCC的预后、肿瘤微环境（TME）及免疫治疗反应。
基于批量RNA测序和单细胞RNA测序数据对ccRCC中的IRFs进行多组学分析。根据IRFs的表达谱，采用非负矩阵分解（NMF）算法对ccRCC样本进行聚类。然后，应用最小绝对收缩和选择算子（LASSO）及Cox回归分析构建风险模型，以预测ccRCC的预后、免疫细胞浸润、免疫治疗反应及靶向药物敏感性。此外，建立了包含风险模型和临床特征的列线图。
在ccRCC中鉴定出两种具有不同预后、临床特征及免疫细胞浸润水平的分子亚型。IRFs相关风险模型在TCGA-KIRC队列中作为独立预后指标得以建立，并在E-MTAB-1980队列中得到验证。低风险组患者的总生存期优于高风险组。该风险模型在预测预后方面优于临床特征和ClearCode34模型。此外，还开发了列线图以提高风险模型的临床实用性。而且，高风险组的CD8 T细胞、巨噬细胞、滤泡辅助性T细胞和辅助性T细胞（Th1）浸润水平及I型干扰素反应活性评分较高，但肥大细胞浸润水平及II型干扰素反应活性评分较低。癌症免疫循环显示，高风险组中大多数步骤的免疫活性评分显著更高。TIDE评分表明，低风险组患者更可能对免疫治疗有反应。不同风险组的患者对阿昔替尼、索拉非尼、吉非替尼、厄洛替尼、达沙替尼和雷帕霉素表现出不同的药物敏感性。
简而言之，我们开发了一种强大而有效的风险模型，用于预测ccRCC的预后、TME特征以及对免疫治疗和靶向药物的反应，这可能为个性化和精准治疗策略提供新的见解。
