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Published in: Discover Oncology 1/2023

Open Access 01-12-2023 | Breast Cancer | Research

A novel PD-1/PD-L1 pathway molecular typing-related signature for predicting prognosis and the tumor microenvironment in breast cancer

Authors: Yuxin Man, Chao Dai, Qian Guo, Lingxi Jiang, Yi Shi

Published in: Discover Oncology | Issue 1/2023

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Abstract

Background

Currently, the development of breast cancer immunotherapy based on the PD-1/PD-L1 pathway is relatively slow, and the specific mechanism affecting the immunotherapy efficacy in breast cancer is still unclear.

Methods

Weighted correlation network analysis (WGCNA) and the negative matrix factorization (NMF) were used to distinguish subtypes related to the PD-1/PD-L1 pathway in breast cancer. Then univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were used to construct the prognostic signature. A nomogram was established based on the signature. The relationship between the signature gene IFNG and breast cancer tumor microenvironment was analyzed.

Results

Four PD-1/PD-L1 pathway-related subtypes were distinguished. A prognostic signature related to PD-1/PD-L1 pathway typing was constructed to evaluate breast cancer’s clinical characteristics and tumor microenvironment. The nomogram based on the RiskScore could be used to accurately predict breast cancer patients’ 1-year, 3-year, and 5-year survival probability. The expression of IFNG was positively correlated with CD8+ T cell infiltration in the breast cancer tumor microenvironment.

Conclusion

A prognostic signature is constructed based on the PD-1/PD-L1 pathway typing in breast cancer, which can guide the precise treatment of breast cancer. The signature gene IFNG is positively related to CD8+ T cell infiltration in breast cancer.
Appendix
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Metadata
Title
A novel PD-1/PD-L1 pathway molecular typing-related signature for predicting prognosis and the tumor microenvironment in breast cancer
Authors
Yuxin Man
Chao Dai
Qian Guo
Lingxi Jiang
Yi Shi
Publication date
01-12-2023
Publisher
Springer US
Published in
Discover Oncology / Issue 1/2023
Print ISSN: 1868-8497
Electronic ISSN: 2730-6011
DOI
https://doi.org/10.1007/s12672-023-00669-4

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