Predictive potential of five genes of cuproptosis for survival in LGG

Abstract

Low-grade glioma (LGG) is a common primary tumor in the central nervous system. The function of several types of cell death has been proved in tumorigenesis of low-grade glioma (LGG). Cuproptosis, a new form of cell death, has been defined in recent years. We put forward the function between cuproptosis and LGG for the first time. According to TCGA datasets, we performed a univariate Cox regression analysis and got 5 hub risk genes related to the survival of LGG. Then, we constructed a prognostic risk mode by using LASSO method and demonstrated that risk score is independent prognostic risk factor by using univariate and multivariate Cox regression analyses. The above results were verified in CGGA datasets. Next, we compared the immune characteristics by using CIBERSORT, ssGSEA and demonstrated that 5 hub genes play an important role in tumor immunotherapy. Finally, we performed GO term and KEGG pathway analyses and found 5 hub genes are likely to change the biological functions of LGG by regulating the metabolism related components. We believe that our results will provide a new strategy for tumor treatment.

Keywords

LGG / Cuproptosis / Prognosis / Immunotherapy / Tumor metabolism

Declarations

Not applicable.

Authors' contributions

Xiaopeng Li: Writing - original draft, Validation, Software, Formal analysis, Data curation. Yanpeng Xia: Formal analysis. Chunyan Zhu: Investigation. Qian Zhang: Methodology. Rongliang Guo: Writing - review & editing. Shuanli Xin: Visualization, Validation, Supervision, Funding acquisition. Zhou Tao: Formal analysis.

Ethics approval and consent to participate

As the study utilized publicly accessible databases, there was no requirement for ethics committee approval or informed consent from participants. All methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Availability of Material and Data

The datasets of TCGA and CGGA analyzed in our study can be found in the GLIOVIS platform (https://gliovis.bioinfo.cnio.es/). TCGA and GTEX datasets can be found in UCSCXENASHINY (https://shiny.hiplot.com.cn/ucsc-xena-shiny/).

Funding

This work was supported by the 2025 Hebei Province Yanzhao Huangjintai Talent Program — Dr. Xiaopeng Li Postdoctoral Key Talent Project.

Declarations of Competing Interests

The authors declare no competing interests.

Acknowledgements

Not applicable.

Authors' other information

Not applicable.

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