Fu Yang, Sun Shanshan, Bi Jianbin, Kong Chuize, Shi Du
Department of Urology, The First Hospital of China Medical University Shenyang 110001, Liaoning, China.
Department of Pharmacy, People's Hospital Affiliated of China Medical University Shenyang 110015, Liaoning, China.
Am J Transl Res. 2022 May 15;14(5):2825-2843. eCollection 2022.
The functions of RNA-binding proteins (RBPs) in the occurrence and development of tumors remain largely unexplored. We established a risk signature based on RBPs to predict the prognosis, tumor-related immunity, and treatment benefits of patients with testicular germ cell tumors (TGCTs).
A risk signature was built based on RBPs closely related to survival obtained from TGCT data in The Cancer Genome Atlas (TCGA) database. The ability of the signature to predict prognosis was analyzed by survival curves and Cox regression. The risk signature was validated using the Gene Expression Omnibus (GEO) database. The connection between tumor immunity and the risk score was evaluated. Risk score-related drug sensitivity and biofunctions were also explored.
A risk signature including four selected RBP genes (PARP12, USB1, POLR2E and EED) was established. The prognosis of high-risk TGCT patients was worse than that of low-risk TGCT patients. The risk score was considered a critical factor closely related to prognosis, as determined via Cox regression, and was also closely associated with multiple characteristics of tumor immunity, chemotherapy drugs and biofunctions.
The established risk signature including four selected RBPs in TGCTs could predict the prognosis, tumor-related immunity and treatment benefits of patients with TGCTs. Utilization of this signature could help clinicians make personalized treatment decisions.
RNA结合蛋白(RBPs)在肿瘤发生发展中的作用在很大程度上仍未得到充分探索。我们基于RBPs建立了一个风险特征模型,以预测睾丸生殖细胞肿瘤(TGCTs)患者的预后、肿瘤相关免疫及治疗获益情况。
基于从癌症基因组图谱(TCGA)数据库的TGCT数据中获得的与生存密切相关的RBPs构建风险特征模型。通过生存曲线和Cox回归分析该特征模型预测预后的能力。使用基因表达综合数据库(GEO)对风险特征模型进行验证。评估肿瘤免疫与风险评分之间的关联。还探索了与风险评分相关的药物敏感性和生物学功能。
建立了一个包含四个选定RBP基因(PARP12、USB1、POLR2E和EED)的风险特征模型。高危TGCT患者的预后比低危TGCT患者差。经Cox回归确定,风险评分被认为是与预后密切相关的关键因素,并且还与肿瘤免疫、化疗药物和生物学功能的多个特征密切相关。
在TGCTs中建立的包含四个选定RBPs的风险特征模型可以预测TGCT患者的预后、肿瘤相关免疫及治疗获益情况。利用该特征模型有助于临床医生做出个性化治疗决策。