Establishment and validation of a multigene model to predict the risk of relapse in hormone receptor-positive early-stage Chinese breast cancer patients / 中华医学杂志(英文版)

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.

Clinicopathologic significance of legumain overexpression in cancer: a systematic review and meta-analysis.

Since reports on the clinical significance of legumain in cancer have shown inconsistent results, we systematically evaluated clinical indicators of legumain in cancer. We searched the Cochrane Library, PubMed, Embase, and EBSCO databases and the Wangfang and CNKI databases in China by using "legumain" and ("neoplasms" OR "cancer") as search terms. We included case-controlled studies of legumain and cancer. The quality of the studies was evaluated by using Lichtenstein's guidelines, and valid data was extracted for analysis. In total, 10 articles were included in this study. Meta-analysis showed that legumain was overexpressed in cancer compared with in normal tissue and was higher in stage III-IV disease than in I-II disease. Moreover, legumain overexpression was correlated with poor prognosis and clinical stage. Furthermore, Cancer Genome Atlas data showed that among patients with rectal cancer, those with tumors overexpressing legumain had shorter overall survival than those in the low expression group (P < 0.05). Legumain appears to be involved in tumor development and deterioration; thus, it can potentially be developed into both a marker for monitoring and diagnosing tumors and a therapeutic target.