Establishment and validation of a multigene model to predict the risk of relapse in hormone receptor-positive early-stage Chinese breast cancer patients. Issue 2 (7th November 2022)

• Record Type: Journal Article
• Authors: Liu, Jiaxiang; Zhao, Shuangtao; Yang, Chenxuan; Ma, Li; Wu, Qixi; Meng, Xiangzhi; Zheng, Bo; Guo, Changyuan; Feng, Kexin; Shang, Qingyao; Liu, Jiaqi; Wang, Jie; Zhang, Jingbo; Shan, Guangyu; Xu, Bing; Liu, Yueping; Ying, Jianming; Wang, Xin; Wang, Xiang
• Editors: Hao, Xiuyuan; Jia, Rongman
• Is Part Of: Chinese medical journal. Volume 136:Issue 2(2023)
• Page Start: 184
• Page End: 193
• Publication Date: 2022-11-07
• Subjects: Breast neoplasms -- CKMT1B -- OR11M1P -- Predictive model -- Prognosis -- Risk score -- SMR3B; Medicine -- Periodicals; Medicine, Oriental -- Periodicals; Medicine; Medicine, Oriental; Medicine; Medicine, East Asian Traditional; Periodicals; Electronic journals; 610.5
• DOI: 10.1097/CM9.0000000000002411
• Languages: English
• ISSNs: 0366-6999

Abstract

Abstract: 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 chemotherapy after surgery for breast cancer patients.