Zhu Xu, Shen Cheng, Zhang Wei, Ji Yuanfei, Xu Siyang, Zheng Bing, Chen Zhan
Department of Urology, Affiliated Hospital 2 of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu 226001, P.R. China.
Department of Medical Research Center, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu 226001, P.R. China.
Oncol Lett. 2025 May 22;30(1):362. doi: 10.3892/ol.2025.15108. eCollection 2025 Jul.
Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of kidney cancer. Defects in transfer RNA (tRNA) modification can lead to significantly impaired protein synthesis and misfolding, contributing to various pathologies, including malignancies. The present study aimed to develop a method predict survival outcomes and guide both immunotherapy and chemotherapy in patients with ccRCC. Patient data was collected from The Cancer Genome Atlas and tRNA modification-related genes from the Molecular Signature Database were identified. External validation of the prognostic model was conducted using the GSE29609 dataset from the Gene Expression Omnibus database. Molecular subtypes were determined through univariate Cox analysis of tRNA modification-related genes and the 'ConsensusClusterPlus' package. Multivariate Cox regression and the least absolute shrinkage and selection operator analyses were employed to establish a prognostic profile consisting of six independent prognostic genes: FTSJ1, LCMT2, METTL6, PUS1, TRMO and TRMT5. Higher risk scores and Cluster 2 classification were associated with poorer overall survival and increased expression of human leukocyte antigens and immune checkpoints. The assessment of immune cell infiltration and the tumor microenvironment was conducted using the ESTIMATE, CIBERSORT and single sample Gene Set Enrichment Analysis algorithms, were compared with the molecular subtypes and risk profiles of tRNA modification regulators. Additional analyses included somatic mutation analysis, nomogram construction, chemotherapy response prediction and small molecule drug prediction. Finally, the expression levels of the six identified genes in ccRCC cell lines were validated using reverse transcription-quantitative PCR, which confirmed consistency with the predictions made. The present study introduced a six-gene prognostic signature that may improve prognosis and facilitate personalized treatment strategies for patients with ccRCC in the future, thereby potentially enhancing individualized patient management.
透明细胞肾细胞癌（ccRCC）是最常见的肾癌类型。转运RNA（tRNA）修饰缺陷可导致蛋白质合成显著受损和错误折叠，从而引发包括恶性肿瘤在内的各种病理状况。本研究旨在开发一种方法来预测ccRCC患者的生存结局，并指导免疫治疗和化疗。从癌症基因组图谱收集患者数据，并从分子特征数据库中识别与tRNA修饰相关的基因。使用来自基因表达综合数据库的GSE29609数据集对预后模型进行外部验证。通过对与tRNA修饰相关基因进行单变量Cox分析以及使用“ConsensusClusterPlus”软件包来确定分子亚型。采用多变量Cox回归和最小绝对收缩和选择算子分析，建立了一个由六个独立预后基因组成的预后特征：FTSJ1、LCMT2、METTL6、PUS1、TRMO和TRMT5。较高的风险评分和聚类2分类与较差的总生存期以及人类白细胞抗原和免疫检查点的表达增加相关。使用ESTIMATE、CIBERSORT和单样本基因集富集分析算法对免疫细胞浸润和肿瘤微环境进行评估，并与tRNA修饰调节因子的分子亚型和风险特征进行比较。其他分析包括体细胞突变分析、列线图构建、化疗反应预测和小分子药物预测。最后，使用逆转录定量PCR验证了ccRCC细胞系中六个已鉴定基因的表达水平，证实与预测结果一致。本研究引入了一种六基因预后特征，未来可能改善ccRCC患者的预后并促进个性化治疗策略，从而潜在地加强个体化患者管理。
