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TRIM58 蛋白表达对接受新辅助化疗的乳腺癌患者的预测和预后价值
Authors Zheng YZ, Li JY, Ning LW, Xie N
Received 23 August 2022
Accepted for publication 30 November 2022
Published 21 December 2022 Volume 2022:14 Pages 475—487
DOI https://doi.org/10.2147/BCTT.S387209
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Pranela Rameshwar
Introduction: Tripartite motif-containing protein (TRIM) family members play crucial roles in carcinogenesis and chemotherapy resistance. In this study, we aimed to determine whether TRIM58 protein expression is related to patient responses to neoadjuvant therapy (NAT) and their survival outcome.
Methods: Immunohistochemistry was performed on female breast cancer samples from biopsies before NAT in Shenzhen Second People’s Hospital. Univariate and multivariate logistic regression tests were used to analyze the association between TRIM58 protein expression and pathological complete response (pCR). The Cox proportional hazards model was used to calculate the adjusted hazard ratio (HR) with a 95% confidence interval (95% CI). The Kaplan–Meier plotter database was used to analyze the prognostic value of TRIM58.
Results: High TRIM58 expression was associated with small tumor size in all the patients (n = 58). Multivariate analysis suggested that low TRIM58 expression was an independent predictive factor for higher pCR (odds ratio = 0.06, 95% CI 0.005– 0.741, P = 0.028). The Kaplan–Meier Plotter dataset suggested that the TRIM58 high-expression group showed a worse 5-year overall survival than the low-expression group (HR = 1.34, 95% CI 1.07– 1.67, P = 0.01). Pathway analysis revealed the potential mechanisms of TRIM58 in chemoresistance.
Discussion: Our study suggests that TRIM58 is a promising biomarker for both neoadjuvant chemosensitivity and long-term clinical outcomes in breast cancer. It may also help to identify candidate responders and determine treatment strategies.
Keywords: chemosensitivity, pathological complete response, biomarker, patient stratification, predictive diagnostics