Comprehensive Analysis of Cuproptosis Genes and Identification of Cuproptosis Subtypes in Breast Cancer

Author:

Li Jialin12,Li Lei2,Dong Yi34,Zhong Bin34,Yin Wei12

Affiliation:

1. Clinical College, Wuhan University of Science and Technology, Wuhan 430000, China

2. Tianyou Hospital of Wuhan University of Science and Technology, Wuhan 430000, China

3. General Hospital of Central Theater Command, Wuhan 430000, China

4. Southern Medical College University, University, Guangzhou 510000, China

Abstract

Background: Copper-induced death (cuproptosis) is copper-dependent regulated cell death, which is different from known death mechanisms and is dependent on mitochondrial respiration. However, its effect on breast cancer (BRCA) is unclear. Objective: The objective of this study is to explore the important clinical significance of cuproptosis genes and to provide a new idea for guiding the personalized immunotherapy strategy of BRCA patients. Materials and Method: We collected cuproptosis genes from published work. The gene alteration, differential expression, and prognostic value of cuproptosis genes were explored in BRCA based on TCGA database. We identified two subtypes (clusters A and B) by performing unsupervised clustering. The difference between two clusters was deeply explored, including clinical features, differential expressed genes (DEGs), pathways, and immune cell infiltration. Based on the DEGs between two clusters, a cuproptosis score was constructed and its predictive capability for overall survival of BRCA patients was validated. Results and Discussion: Patients with high cuproptosis score have worse survival status, with an increased infiltration level of most immune cells. Further analysis suggested that BRCA patients with high cuproptosis score may be sensitive to immune checkpoint inhibitor (ICI) treatment. Conclusion: Our findings may improve our understanding of cuproptosis in BRCA and may distinguish patients suitable for ICI treatment.

Publisher

Bentham Science Publishers Ltd.

Subject

Organic Chemistry,Computer Science Applications,Drug Discovery,General Medicine

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