Abstract
Objective To construct the prognosis model of cuprotosis-related genes in clear-cell renal cell carcinoma patients. Methods The mRNA and relate clinical data of patients with ccRCC were retrieved from TCGA. LASSO Cox regression model utilized to construct a multigene signature. Results Most cuprotosis-related genes were differentially expressed between tumour tissues and paired para-neoplasm tissues. Four correlated with overall survival (OS) in the univariate Cox regression analysis (P < 0.05). Three genes were selected to construct a signature, and the patients divided into two risk groups. Compared with patients in the low-risk group, patients in the high-risk group had significantly lower OS (P < 0.05, statistically significant). The model’s risk score showed a significant difference in univariate and multivariate Cox regression analysis. Receiver operating characteristic (ROC) curve analysis confirmed the signature’s predictive ability. Immune cell correlation analysis showed that the immune status was different within risk groups, and there were significant differences in the scores of CD8+ T cells, CD4+ T cells, B cells, NK cells and macrophages. Conclusion, The newly constructed prognosis model of cuprotosis-related genes (FDX1, DLD and CDKN2A) can accurately predict the prognosis of patients. Moreover, targeting cuprotosis is also likely to become a new treatment option for ccRCC.
Z. Zhan and P. Han—Contributed to the work equally.
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Acknowledgement
This study was supported by Provincial Science and Technology Grant of Shanxi Province (20210302124588), Science and technology innovation project of Shanxi province universities (2019L0683).
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Zhan, Z., Han, P., Bi, X., Yang, J., Zhao, T. (2022). A Novel Cuprotosis-Related Gene Signature Predicts Survival Outcomes in Patients with Clear-Cell Renal Cell Carcinoma. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_21
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