Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis
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...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-01-01
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| Series: | Annals of Medicine |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2021.1914343 |
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| _version_ | 1849341099658706944 |
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| author | Ying Li Xin Zhao Qiang Liu Yujie Liu |
| author_facet | Ying Li Xin Zhao Qiang Liu Yujie Liu |
| author_sort | Ying Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eba2895a300b4e3e92bb9741ee84d117 |
| institution | Kabale University |
| issn | 0785-3890 1365-2060 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of Medicine |
| spelling | doaj-art-eba2895a300b4e3e92bb9741ee84d1172025-08-20T03:43:43ZengTaylor & Francis GroupAnnals of Medicine0785-38901365-20602021-01-015311020103210.1080/07853890.2021.1914343Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosisYing Li0Xin Zhao1Qiang Liu2Yujie Liu3Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, SunYat-sen University, Guangzhou, ChinaDepartment of Breast Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, ChinaGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, SunYat-sen University, Guangzhou, ChinaGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, SunYat-sen University, Guangzhou, ChinaBackground 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.https://www.tandfonline.com/doi/10.1080/07853890.2021.1914343Breast cancermacrophages marker genesprognostic signaturebioinformatics |
| spellingShingle | Ying Li Xin Zhao Qiang Liu Yujie Liu Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis Annals of Medicine Breast cancer macrophages marker genes prognostic signature bioinformatics |
| title | Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis |
| title_full | Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis |
| title_fullStr | Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis |
| title_full_unstemmed | Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis |
| title_short | Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis |
| title_sort | bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis |
| topic | Breast cancer macrophages marker genes prognostic signature bioinformatics |
| url | https://www.tandfonline.com/doi/10.1080/07853890.2021.1914343 |
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