Li Song-Chao, Yan Li-Jie, Wei Xu-Liang, Jia Zhan-Kui, Yang Jin-Jian, Ning Xiang-Hui
Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Institute of Pharmaceutical Science, Zhengzhou University, Zhengzhou, China.
Front Pharmacol. 2023 May 2;14:1038457. doi: 10.3389/fphar.2023.1038457. eCollection 2023.
Kidney cancer is one of the most common and lethal urological malignancies. Discovering a biomarker that can predict prognosis and potential drug treatment sensitivity is necessary for managing patients with kidney cancer. SUMOylation is a type of posttranslational modification that could impact many tumor-related pathways through the mediation of SUMOylation substrates. In addition, enzymes that participate in the process of SUMOylation can also influence tumorigenesis and development. We analyzed the clinical and molecular data which were obtanied from three databases, The Cancer Genome Atlas (TCGA), the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC), and ArrayExpress. Through analysis of differentially expressed RNA based on the total TCGA-KIRC cohort, it was found that 29 SUMOylation genes were abnormally expressed, of which 17 genes were upregulated and 12 genes were downregulated in kidney cancer tissues. A SUMOylation risk model was built based on the discovery TCGA cohort and then validated successfully in the validation TCGA cohort, total TCGA cohort, CPTAC cohort, and E-TMAB-1980 cohort. Furthermore, the SUMOylation risk score was analyzed as an independent risk factor in all five cohorts, and a nomogram was constructed. Tumor tissues in different SUMOylation risk groups showed different immune statuses and varying sensitivity to the targeted drug treatment. In conclusion, we examined the RNA expression status of SUMOylation genes in kidney cancer tissues and developed and validated a prognostic model for predicting kidney cancer outcomes using three databases and five cohorts. Furthermore, the SUMOylation model can serve as a biomarker for selecting appropriate therapeutic drugs for kidney cancer patients based on their RNA expression.
肾癌是最常见且致命的泌尿系统恶性肿瘤之一。发现一种能够预测预后和潜在药物治疗敏感性的生物标志物对于肾癌患者的管理至关重要。SUMO化是一种翻译后修饰类型，它可通过SUMO化底物的介导影响许多肿瘤相关通路。此外，参与SUMO化过程的酶也会影响肿瘤的发生和发展。我们分析了从三个数据库（癌症基因组图谱（TCGA）、美国国立癌症研究所的临床蛋白质组肿瘤分析联盟（CPTAC）和ArrayExpress）获取的临床和分子数据。通过基于整个TCGA - KIRC队列对差异表达RNA进行分析，发现29个SUMO化基因在肾癌组织中异常表达，其中17个基因上调，12个基因下调。基于发现的TCGA队列构建了一个SUMO化风险模型，随后在验证TCGA队列、整个TCGA队列、CPTAC队列和E - TMAB - 1980队列中成功验证。此外，在所有五个队列中分析了SUMO化风险评分作为独立风险因素，并构建了列线图。不同SUMO化风险组的肿瘤组织表现出不同的免疫状态以及对靶向药物治疗的不同敏感性。总之，我们研究了肾癌组织中SUMO化基因的RNA表达状态，并使用三个数据库和五个队列开发并验证了一个用于预测肾癌预后的模型。此外，SUMO化模型可作为一种生物标志物，根据肾癌患者的RNA表达为其选择合适的治疗药物。
