机构地区:[1]Department of Breast Surgical 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 Thoracic Surgery,Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital,Capital Medical University,Beijing 101149,China [3]Breast Center,The Fourth Hospital of Hebei Medical University,Shijiazhuang,Hebei 050035,China [4]Research and Development Department,Beijing USCI Medical Laboratory,Beijing 100195,China [5]Department of Pathology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing 100021,China [6]Department of Ultrasound,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing 100021,China [7]Department of Pathology,The Fourth Hospital of Hebei Medical University,Shijiazhuang,Hebei 050011,China
出 处:《Chinese Medical Journal》2023年第2期184-193,共10页中华医学杂志(英文版)
基 金:supported by the National Key Research and Development Program of China(2019YFE0110000);National Natural Science Foundation of China(82072097);CAMS Innovation Fund for Medical Science(CIFMS)(2020-I2M-C&T-B-069,2021-I2M-1-014);and Beijing Hope Run Special Fund of Cancer Foundation of China(LC2020A18).
摘 要:Background: Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. Methods: In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC). Results: A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. Conclusions: A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemothe
关 键 词:Breast neoplasms CKMT1B OR11M1P Predictive model Prognosis Risk score SMR3B
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