乳腺癌
肿瘤科
内科学
癌症
亚型
免疫系统
激素受体
生物
医学
癌症研究
免疫学
计算机科学
程序设计语言
作者
Mengyan Zhang,Xingda Zhang,Te Ma,Cong Wang,Jiyun Zhao,Yue Gu,Yan Zhang
标识
DOI:10.1016/j.compbiomed.2023.107222
摘要
A significant proportion of breast cancer cases are characterized by hormone receptor positivity (HR+). Clinically, the heterogeneity of HR+ breast cancer leads to different therapeutic effects on endocrine. Therefore, definition of subgroups in HR+ breast cancer is important for effective treatment. Here, we have developed a CMBR method utilizing computational functional networks based on DNA methylation to identify conserved subgroups in HR+ breast cancer. Calculated by CMBR, HR+ breast cancer was divided into five subgroups, of which HR+/negative epidermal growth factor receptor-2 (Her2-) was divided into two subgroups, and HR+/positive epidermal growth factor receptor-2 (Her2+) was divided into three subgroups. These subgroups had heterogeneity in the immune microenvironment, tumor infiltrating lymphocyte patterns, somatic mutation patterns and drug sensitivity. Specifically, CMBR identified two subgroups with the “Hot” tumor phenotype. In addition, these conserved subgroups were broadly validated on external validation datasets. CMBR identified the molecular signature of HR+ breast cancer subgroups, providing valuable insights into personalized treatment strategies and management options.
科研通智能强力驱动
Strongly Powered by AbleSci AI