A novel risk model of three SUMOylation genes based on RNA expression for potential prognosis and treatment sensitivity prediction in kidney cancer.

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.