Chinese Medical Journal (Jan 2023)

Establishment and validation of a multigene model to predict the risk of relapse in hormone receptor-positive early-stage Chinese breast cancer patients

  • Jiaxiang Liu,
  • Shuangtao Zhao,
  • Chenxuan Yang,
  • Li Ma,
  • Qixi Wu,
  • Xiangzhi Meng,
  • Bo Zheng,
  • Changyuan Guo,
  • Kexin Feng,
  • Qingyao Shang,
  • Jiaqi Liu,
  • Jie Wang,
  • Jingbo Zhang,
  • Guangyu Shan,
  • Bing Xu,
  • Yueping Liu,
  • Jianming Ying,
  • Xin Wang,
  • Xiang Wang,
  • Xiuyuan Hao,
  • Rongman Jia

DOI
https://doi.org/10.1097/CM9.0000000000002411
Journal volume & issue
Vol. 136, no. 2
pp. 184 – 193

Abstract

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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.