1.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
Chinese Medical Journal 2023;136(2):184-193
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.
Humans
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Female
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Breast Neoplasms/genetics*
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East Asian People
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Neoplasm Recurrence, Local/genetics*
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Breast
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Algorithms
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Chronic Disease
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Prognosis
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Tumor Microenvironment