Wang Jiahao, Bao Dan, Chen Xiaochao, Yu Zijie, Kong Weiyu, Xu Chen, Li Songtao, Yue Yulin
Department of Clinical Laboratory, Children's Hospital of Nanjing Medical University, Nanjing, 210008, China.
First Clinical Medical College of Nanjing Medical University, Nanjing, 210029, China.
Discov Oncol. 2025 May 13;16(1):756. doi: 10.1007/s12672-025-02505-3.
Arginine methylation, a key post-translational modification, plays a pivotal role in regulating various cellular processes and has been implicated in cancer progression. However, the potential of arginine methylation-related genes as prognostic markers in clear cell renal cell carcinoma (ccRCC) remains underexplored.
We utilized public transcriptomic datasets from TCGA, E-MTAB-1980 and ICGC, for model construction and validation. Single-cell RNA sequencing datasets were employed to evaluate gene expression patterns at the cellular level. Consensus clustering, KM survival analysis, and GSVA were applied to identify molecular subtypes and related pathways. Univariate and multivariate Cox regression analyses were applied to develop an arginine methylation-related signature (AMS). Immune profiling, mutation landscape, and drug sensitivity prediction were also employed to explore the model's association with clinical features, immune infiltration, mutation burden, and therapeutic responses.
The AMS demonstrated robust prognostic performance, with consistent validation across external cohorts. High-risk patients exhibited significantly worse survival, elevated TMB, and an immunosuppressive tumor microenvironment characterized by increased infiltration of regulatory immune cells. Single-cell RNA sequencing revealed key prognostic genes expressed predominantly in cancer and immune cells, supporting their role in tumor progression and immune interactions.
The arginine methylation-based prognostic model provides a reliable framework for survival risk stratification in ccRCC and holds promise for guiding personalized therapeutic strategies. Future research should emphasize clinical validation of this model and explore its potential role in optimizing immunotherapy and targeted treatment strategies for ccRCC.
精氨酸甲基化是一种关键的翻译后修饰，在调节各种细胞过程中起关键作用，并与癌症进展有关。然而，精氨酸甲基化相关基因作为透明细胞肾细胞癌（ccRCC）预后标志物的潜力仍未得到充分探索。
我们利用来自TCGA、E-MTAB-1980和ICGC的公共转录组数据集进行模型构建和验证。采用单细胞RNA测序数据集在细胞水平评估基因表达模式。应用一致性聚类、KM生存分析和GSVA来识别分子亚型和相关途径。应用单变量和多变量Cox回归分析来开发精氨酸甲基化相关特征（AMS）。还采用免疫图谱分析、突变景观分析和药物敏感性预测来探索该模型与临床特征、免疫浸润、突变负担和治疗反应的关联。
AMS显示出强大的预后性能，在外部队列中得到一致验证。高危患者的生存明显更差，肿瘤突变负荷（TMB）升高，且具有以调节性免疫细胞浸润增加为特征的免疫抑制性肿瘤微环境。单细胞RNA测序揭示了主要在癌症和免疫细胞中表达的关键预后基因，支持了它们在肿瘤进展和免疫相互作用中的作用。
基于精氨酸甲基化的预后模型为ccRCC的生存风险分层提供了一个可靠的框架，并有望指导个性化治疗策略。未来的研究应强调该模型的临床验证，并探索其在优化ccRCC免疫治疗和靶向治疗策略中的潜在作用。
