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An immune-related risk gene signature predicts the prognosis of breast cancer

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Abstract

Background

Accurate prediction of the outcome of breast cancer remains as a challenge due to its heterogeneous nature. We aimed to construct an immune-related risk signature to predict the overall outcome of breast cancer using bioinformatic approaches.

Methods

In this study, transcriptome and survival data obtained from The Cancer Genome Atlas database and the Gene Expression Omnibus database were used to identify differentially expressed genes between breast cancer and normal samples. A regulatory network was constructed based on the immune-related prognostic genes and transcription factors screened from the differently expressed genes. The immune-related risk gene signature was obtained using the least absolute shrinkage and selection operator (LASSO) method and Cox regression model. The immune-related prognostic scores of breast cancer (IPSBC) calculated from the risk signature were used to group breast cancer patients by risk levels. The accuracy of IPSBC was evaluated by survival analysis and receiver operating characteristic curve analysis. The independency and the relationship of IPSBC with clinicopathological characteristics and abundance of tumor-infiltrated immune cells were also investigated.

Results

A total of 4296 differentially expressed genes between breast cancer and normal samples were identified, and a total of 13 prognostic immune-related genes were eventually selected as the risk gene signature, which was an independent prognostic factor of the overall survival of breast cancer. The IPSBC stratified breast cancer patients into low- and high-risk groups. Breast cancer patients in the high-risk group were associated with worse overall outcomes, more advanced stage and less abundance of tumor-infiltrated immune cells, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells compared to low-risk group.

Conclusion

In this study, an immune-related gene signature of breast cancer was identified, which could be used as potential prognostic and therapeutic targets of breast cancer.

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Abbreviations

AUC:

Area under the curve

BP:

Biological process

CC:

Cellular component

CI:

Confidence interval

DEG:

Differentially expressed gene

GO:

Gene ontology

GEO:

Gene Expression Omnibus

HR:

Hazard ratio

IPSBC:

Immune-related prognostic score for breast cancer

IRG:

Immune-related gene

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LASSO:

Least absolute shrinkage and selection operator

MF:

Molecular function

OS:

Overall survival

PD-L1:

Programmed death-ligand 1

ROC:

Receiver operating characteristic

RMA:

Robust Multi-Array Average

TCGA:

The Cancer Genome Atlas

TF:

Transcription factor

TNBC:

Triple-negative breast cancer

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Acknowledgements

This work was supported by the State Key Laboratory of Chemical Oncogenomics, Technology R & D Funds of Shenzhen, China (Grant No. GJHZ20170314164935502), and Shenzhen Bay Laboratory, Shenzhen, China. The authors thank Yang Yan, Qiang Li and Longyu Xia from Tsinghua University for their valuable suggestions.

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Authors and Affiliations

  1. Department of Chemistry, Tsinghua University, Beijing, 100084, People’s Republic of China

    Wenning Cao

  2. State Key Laboratory of Chemical Oncogenomics, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People’s Republic of China

    Wenning Cao, Xiang Ji & Lan Ma

  3. Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen, 518132, People’s Republic of China

    Yike Jiang & Lan Ma

  4. Precision Medicine and Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, 518055, People’s Republic of China

    Yike Jiang & Lan Ma

  5. School of Life Science, Tsinghua University, Beijing, 100084, People’s Republic of China

    Xiang Ji

  6. Division of Life Science and Health, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People’s Republic of China

    Lan Ma

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  1. Wenning Cao
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  2. Yike Jiang
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Correspondence to Lan Ma.

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Cao, W., Jiang, Y., Ji, X. et al. An immune-related risk gene signature predicts the prognosis of breast cancer. Breast Cancer 28, 653–663 (2021). https://doi.org/10.1007/s12282-020-01201-0

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  • DOI: https://doi.org/10.1007/s12282-020-01201-0

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