Baowen Yuan1, Wei Liu2, Miaomiao Huo1, Jingyao Zhang1, Yunkai Yang1, Tianyang Gao2, Xin Yin3, Tianshu Yang3, Xu Teng4, Wei Huang5, Hefen Yu6. 1. Key Laboratory of Cancer and Microbiome, State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China. 2. Department of Biochemistry and Molecular Biology, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China. 3. Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China. 4. Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China. tengxu@ccmu.edu.cn. 5. Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China. weihuang@ccmu.edu.cn. 6. Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China. yhf23@ccmu.edu.cn.
Abstract
BACKGROUND: RNA N6-methyladenosine (m6A) modification is primarily regulated by m6A regulators, which play significant epigenetic regulatory roles in tumorigenesis, tumor development, and tumor immune microenvironment. However, the correlation between m6A regulators and immune cell infiltration in breast cancer remains unclear. METHODS: In this study, m6A modification patterns were evaluated based on 31 m6A modification regulators. m6A clusters were determined by consensus clustering. Immune landscape and immune cell infiltration subgroups were characterized by m6A clusters. Key module and hub genes related to m6A regulators and immune infiltration cells were identified by WGCNA. LASSO algorithm was applied to select prognostic signatures. Multivariate Cox regression analysis was applied to assess the prognostic value of gene signatures. RESULTS: Two distinct m6A clusters were determined based on the expression of 31 m6A modification regulators and characterized by two tumor immune microenvironment (TIME) immune cell infiltration subgroups. Further, a total of 1971 differentially expressed genes between breast cancer patients and healthy controls were screened, nine modules associated with clinical characteristics of breast cancer patients were identified. Later, one key module and 13 hub genes correlated with m6A regulators and immune infiltration cells were identified. LASSO Cox regression analysis selected and constructed a ten-gene prognostic model to build a risk score system for individual breast cancer patient prognosis. The performance of the ten-gene-based risk score system was further validated in an independent dataset with an AUC of 0.659. CONCLUSIONS: This study revealed that m6A modification regulators played a significant role in the TIME regulation of breast cancer. The hub ten gene-based risk score system is valuable in predicting the prognosis of breast cancer patients, which may provide potential significance for breast cancer diagnosis, prognosis, and immunotherapy in the future.
BACKGROUND: RNA N6-methyladenosine (m6A) modification is primarily regulated by m6A regulators, which play significant epigenetic regulatory roles in tumorigenesis, tumor development, and tumor immune microenvironment. However, the correlation between m6A regulators and immune cell infiltration in breast cancer remains unclear. METHODS: In this study, m6A modification patterns were evaluated based on 31 m6A modification regulators. m6A clusters were determined by consensus clustering. Immune landscape and immune cell infiltration subgroups were characterized by m6A clusters. Key module and hub genes related to m6A regulators and immune infiltration cells were identified by WGCNA. LASSO algorithm was applied to select prognostic signatures. Multivariate Cox regression analysis was applied to assess the prognostic value of gene signatures. RESULTS: Two distinct m6A clusters were determined based on the expression of 31 m6A modification regulators and characterized by two tumor immune microenvironment (TIME) immune cell infiltration subgroups. Further, a total of 1971 differentially expressed genes between breast cancer patients and healthy controls were screened, nine modules associated with clinical characteristics of breast cancer patients were identified. Later, one key module and 13 hub genes correlated with m6A regulators and immune infiltration cells were identified. LASSO Cox regression analysis selected and constructed a ten-gene prognostic model to build a risk score system for individual breast cancer patient prognosis. The performance of the ten-gene-based risk score system was further validated in an independent dataset with an AUC of 0.659. CONCLUSIONS: This study revealed that m6A modification regulators played a significant role in the TIME regulation of breast cancer. The hub ten gene-based risk score system is valuable in predicting the prognosis of breast cancer patients, which may provide potential significance for breast cancer diagnosis, prognosis, and immunotherapy in the future.