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International Conference on Intelligent Computing

ICIC 2022: Intelligent Computing Theories and Application pp 260–269Cite as

A Novel Cuprotosis-Related Gene Signature Predicts Survival Outcomes in Patients with Clear-Cell Renal Cell Carcinoma

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13394))

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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Authors and Affiliations

  1. Changzhi Medical College, Changzhi, 046000, Shanxi, China

    Zhenrun Zhan, Pengyong Han, Xiaodan Bi, Jinpeng Yang & Tingting Zhao

  2. Heping Hospital Affiliated to Changzhi Medical College, Changzhi, 046000, Shanxi, China

    Zhenrun Zhan, Xiaodan Bi, Jinpeng Yang & Tingting Zhao

Authors
  1. Zhenrun Zhan
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  2. Pengyong Han
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  3. Xiaodan Bi
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  4. Jinpeng Yang
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  5. Tingting Zhao
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Corresponding author

Correspondence to Tingting Zhao .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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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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  • DOI: https://doi.org/10.1007/978-3-031-13829-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13828-7

  • Online ISBN: 978-3-031-13829-4

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