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  2. Identification and validation of a copper homeostasis-related gene signature for the predicting prognosis of breast cancer patients via integrated bioinformatics analysis

Identification and validation of a copper homeostasis-related gene signature for the predicting prognosis of breast cancer patients via integrated bioinformatics analysis

  • Sci Rep. 2024 Feb 7;14(1):3141. doi: 10.1038/s41598-024-53560-9.
Yi Li 1 2 Xiuxian Wei 1 2 Yuning Wang 1 2 Wenzhuo Wang 1 2 Cuntai Zhang 1 2 Deguang Kong 3 Yu Liu 4 5
Affiliations

Affiliations

  • 1 Department of Geriatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Building 6, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
  • 2 Key Laboratory of Vascular Aging, Ministry of Education, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China.
  • 3 Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, 238 Ziyang Road, Wuhan, 430060, People's Republic of China. kongdeguang08@126.com.
  • 4 Department of Geriatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Building 6, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China. liuyu2012@tjh.tjmu.edu.cn.
  • 5 Key Laboratory of Vascular Aging, Ministry of Education, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China. liuyu2012@tjh.tjmu.edu.cn.
Abstract

The prognostic value of copper homeostasis-related genes in breast Cancer (BC) remains largely unexplored. We analyzed copper homeostasis-related gene profiles within The Cancer Genome Atlas Program breast Cancer cohorts and performed correlation analysis to explore the relationship between copper homeostasis-related mRNAs (chrmRNA) and lncRNAs. Based on these results, we developed a gene signature-based risk assessment model to predict BC patient outcomes using COX regression analysis and a nomogram, which was further validated in a cohort of 72 BC patients. Using the gene set enrichment analysis, we identified 139 chrmRNAs and 16 core mRNAs via the Protein-Protein Interaction network. Additionally, our copper homeostasis-related lncRNAs (chrlncRNAs) (PINK1.AS, OIP5.AS1, HID.AS1, and MAPT.AS1) were evaluated as gene signatures of the predictive model. Kaplan-Meier survival analysis revealed that patients with a high-risk gene signature had significantly poorer clinical outcomes. Receiver operating characteristic curves showed that the prognostic value of the chrlncRNAs model reached 0.795 after ten years. Principal component analysis demonstrated the capability of the model to distinguish between low- and high-risk BC patients based on the gene signature. Using the pRRophetic package, we screened out 24 Anticancer drugs that exhibited a significant relationship with the predictive model. Notably, we observed higher expression levels of the four chrlncRNAs in tumor tissues than in the adjacent normal tissues. The correlation between our model and the clinical characteristics of patients with BC highlights the potential of chrlncRNAs for predicting tumor progression. This novel gene signature not only predicts the prognosis of patients with BC but also suggests that targeting copper homeostasis may be a viable treatment strategy.

Keywords

Breast cancer; Copper homeostasis; Immune infiltration; Tumor microenvironment; lncRNAs.

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