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Article Abstract
Background: This study aimed to analyze the expression of thyroid hormone-responsive spot 14 (THRSP) and acetyl-CoA carboxylase alpha (ACACA) proteins in breast cancer tumor tissues and their relationship with clinicopathology and prognosis of breast cancer patients. In addition, a nomogram model to predict the prognosis of breast cancer patients was constructed in this study.
Methods: Retrospective analysis of 202 cases of breast cancer patients who underwent surgical treatment in our hospital from October 2019 to March 2021, and collection of patients' cancer tissues and non-Tumor tissue specimens. Immunohistochemistry was used to detect THRSP and ACACA protein expression. Multivariate COX regression was used to analyze the risk factors affecting the prognosis of breast cancer patients. The "rms" package in R software was used to build a survival nomogram model and evaluate the effectiveness of the model.
Results: The expression of THRSP and ACACA proteins in tumor tissues of breast cancer patients was higher than that in non-tumor tissues ( < 0.05). The expression of THRSP and ACACA proteins in breast cancer patients with lymph node metastasis was higher than that in patients without lymph node metastasis ( < 0.05). Cox regression analysis showed that TNM stage III, lymph node metastasis, high expression of Ki-67, high expression of THRSP, and high expression of ACACA were all risk factors for the prognosis of breast cancer patients ( < 0.05). The C-index of the nomogram model was 0.704 (95% CI: 0.596~0.892). The predicted 1-, 2- and 3-year survival AUCs of this nomogram model were 0.802, 0.769 and 0.770, respectively. The calibration curve showed that the model fit the ideal curve well. Decision curve analysis showed the high clinical utility of the model.
Conclusion: The nomogram model constructed based on THRSP and ACACA proteins may provide a reference value for the prognostic evaluation of breast cancer patients.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12326444 | PMC |
| http://dx.doi.org/10.2147/PGPM.S516843 | DOI Listing |
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