Zhao Guo, Ding Jiatong, Ma Jiaxiu, Jiang Yale, Wang Yuning, Wang Shuhang, Li Ning
Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300052, China.
Int J Mol Sci. 2025 May 21;26(10):4953. doi: 10.3390/ijms26104953.
Clear cell renal cell carcinoma (ccRCC) is distinguished by metabolic irregularities and unique immunological profiles. Nevertheless, the comprehensive examination of immune and metabolic attributes within the tumor microenvironment of ccRCC remains inadequately elucidated. In this study, we identified two distinct molecular subtypes (C1 and C2) of ccRCC using the non-negative matrix factorization (NMF) algorithm. Utilizing univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we developed a prognostic signature comprising eight immune- and metabolism-related genes (IMRGs) associated with the tumor microenvironment. The validation of this signature was performed using both testing and entire datasets. A nomogram was developed using IMRGs prognostic signature and various clinical parameters, including age and TNM stage. We also performed the in vitro experiments to validate the carcinogenic role of PYCR1 in ccRCC cells. Subtype C1 exhibited a more favorable prognosis and higher levels of immune cell infiltration compared to subtype C2. The AUCs of the nomogram at 1-, 3-, and 5-year intervals (AUC = 0.874, 0.820, and 0.794) were slightly higher than those of the IMRGs signature alone (AUC = 0.773, 0.755, and 0.764). The association between risk score and immune checkpoint expressions, immunophenoscore (IPS), and microsatellite instability (MSI) collectively predicted treatment efficacy accurately. Additionally, in vitro experiments confirmed the involvement of in promoting the aggressive behaviors of ccRCC cells, as evidenced by reduced proliferation, invasion, and enhanced apoptosis upon knockdown. In conclusion, the IMRGs signature shows promise in predicting prognostic risk, assessing the effectiveness of immunotherapy, and tailoring treatment for ccRCC patients.
透明细胞肾细胞癌（ccRCC）以代谢异常和独特的免疫特征为特点。然而，对ccRCC肿瘤微环境中免疫和代谢特性的全面研究仍未得到充分阐明。在本研究中，我们使用非负矩阵分解（NMF）算法确定了ccRCC的两种不同分子亚型（C1和C2）。利用单变量和最小绝对收缩与选择算子（LASSO）Cox回归分析，我们开发了一个由八个与肿瘤微环境相关的免疫和代谢相关基因（IMRGs）组成的预后特征。使用测试数据集和完整数据集对该特征进行了验证。利用IMRGs预后特征和包括年龄和TNM分期在内的各种临床参数构建了列线图。我们还进行了体外实验，以验证PYCR1在ccRCC细胞中的致癌作用。与C2亚型相比，C1亚型表现出更有利的预后和更高水平的免疫细胞浸润。列线图在1年、3年和5年时的曲线下面积（AUC = 0.874、0.820和0.794）略高于单独的IMRGs特征（AUC = 0.773、0.755和0.764）。风险评分与免疫检查点表达、免疫表型评分（IPS）和微卫星不稳定性（MSI）之间的关联共同准确预测了治疗效果。此外，体外实验证实了 在促进ccRCC细胞侵袭性行为中的作用，如在 敲低后增殖、侵袭减少以及凋亡增强所证明。总之，IMRGs特征在预测ccRCC患者的预后风险、评估免疫治疗效果和制定个性化治疗方案方面显示出前景。 （注：原文中部分内容缺失，已按原文翻译）
