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. 2021 Dec;53(1):1019-1031.
doi: 10.1080/07853890.2021.1914343.

Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis

Ying Li  1   2 , Xin Zhao  3 , Qiang Liu  1   2 , Yujie Liu  1   2
Affiliations

Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis

Ying Li et al. Ann Med. 2021 Dec.

Abstract

Background: Breast cancer is a pivotal cause of global women cancer death. Immunotherapy has become a promising means to cure breast cancer. As constitutes of immune microenvironment of breast cancer, macrophages exert complicated functions in the tumour development and treatment. This study aims to develop a prognostic macrophage marker genes signature (MMGS).Methods: Single cell RNA sequence data analysis was performed to identify macrophage marker genes in breast cancer. TCGA database was used to construct MMGS model as a training cohort, and GSE96058 dataset was used to validate the MMGS as a validation cohort.Results: Genes included in the MMGS model were: SERPINA1, CD74, STX11, ADAM9, CD24, NFKBIA, PGK1. MMGS risk score stratified by overall survival of patients divided them into high- and low-risk groups. And MMGS risk score remained independent prognostic factor in multivariate analysis after adjusting for classical clinical factors in both training and validation cohorts. Besides, hormone receptors negative and human epidermal growth factor receptor 2 (HER2) positive patients had higher risk score. MMGS showed better distinguishing capability between high-risk and low-risk groups in hormone receptor positive and HER2 negative subgroup.Conclusion: MMGS provides a new understanding of immune cell marker genes in breast cancer prognosis and may offer reference for immunotherapy decision for breast cancer patients.

Keywords: Breast cancer; bioinformatics; macrophages marker genes; prognostic signature.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1.
Figure 1.
Identification macrophages marker genes by single cell sequence analysis. (a) PCA plot coloured by various samples. (b) t-SNE plot coloured by various cell types. (c) identification marker genes of different cell types.
Figure 2.
Figure 2.
Screen of macrophage marker genes signature. GO analysis (a) and KEGG pathway enrichment (b) of macrophages marker genes were performed to explore the biological function of these marker genes. Macrophages marker genes related risk score model was developed by univariate analysis (c) and multivariate analysis (d) of macrophages marker genes that were associated with overall survival of breast cancer patients in the TCGA database. (e) Dot plot showing the expression of macrophages marker genes included in the risk score model in various cell types. Dot intensity of colour indicates the average expression in a particular cluster and dot size represents the percent of cells expressing the gene in that cluster. Cluster 7 represents macrophages.
Figure 3.
Figure 3.
Breast cancer patients’ survival status, risk score distribution and Kaplan–Meier curves of OS in the TCGA database and the GSE96058 validation dataset. (a) Breast cancer patients were separated into high-risk and low-risk groups with the cut-off of risk score generated by X-tile software. (b) Breast cancer patients survival status and risk score distribution in the TCGA database. (c) Kaplan–Meier curves of OS between high-risk and low-risk groups in the TCGA database. (d) Breast cancer patients in the GSE96058 validation set were separated into high-risk and low-risk groups with the same cut-off in the TCGA database. (e) Breast cancer patients survival status and risk score distribution in the GSE96058 validation set. (f) Kaplan–Meier curves of OS between high-risk and low-risk groups in the GSE96058 validation set.
Figure 4.
Figure 4.
Receiver operating characteristic curves of the MMGS model to predict the 3- and 5-yearOS in the training set (a), validation set (b).
Figure 5.
Figure 5.
The relation between risk score and age (a), tumour size (b), lymph node status (c), ER status (d), PR status (e), HER2 status (f) of breast cancer patients in the TCGA database.
Figure 6.
Figure 6.
The relation between risk score and age (a), tumour size (b), lymph node status (c), ER status (d), PR status (e), HER2 status (f) of breast cancer patients in the GSE96058 validation dataset.
Figure 7.
Figure 7.
The correlation of MMGS risk score with classical clinical variables and the correlation between gene methylation and expression in MMGS. (a) The prognostic value of MMGS model in different subgroups in the TCGA training dataset. (b) The prognostic value of MMGS model in different subgroups in the GSE96058 validation dataset. (c) The correlation between DNA methylation and mRNA expression of CD74 and SERPINA1 in the risk score model.
Figure 7.
Figure 7.
The correlation of MMGS risk score with classical clinical variables and the correlation between gene methylation and expression in MMGS. (a) The prognostic value of MMGS model in different subgroups in the TCGA training dataset. (b) The prognostic value of MMGS model in different subgroups in the GSE96058 validation dataset. (c) The correlation between DNA methylation and mRNA expression of CD74 and SERPINA1 in the risk score model.
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References

    1. Bray F, Ferlay J, Soerjomataram I, et al. . Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424. - PubMed
    1. Bahcecioglu G, Basara G, Ellis BW, et al. . Breast cancer models: engineering the tumor microenvironment. Acta Biomater. 2020; 106:1–21. - PMC - PubMed
    1. Schmid P, Adams S, Rugo HS, et al. . Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer. N Engl J Med. 2018;379:2108–2121. - PubMed
    1. Billan S, Kaidar-Person O, Gil Z.. Treatment after progression in the era of immunotherapy. Lancet Oncol. 2020;21:e463–e476. - PubMed
    1. Jiang YZ, Liu Y, Xiao Y, et al. . Molecular subtyping and genomic profiling expand precision medicine in refractory metastatic triple-negative breast cancer: the FUTURE trial. Cell Res. 2021;31:178–1038. - PMC - PubMed

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This work was supported by grants from the National Natural Science Foundation of China [81672622, 81630074, 81872141], Guangdong Science and Technology Department [2019A1515010146], Guangzhou Science and Technology plan key projects [201804020076].