{"title":"基于THRSP和ACACA蛋白组织表达预测乳腺癌患者预后的Nomogram模型构建","authors":"Benkai Wei, Fan Li, Huanhuan Yan, Jun Shen","doi":"10.2147/PGPM.S516843","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>The expression of THRSP and ACACA proteins in tumor tissues of breast cancer patients was higher than that in non-tumor tissues (<i>p</i> < 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 (<i>p</i> < 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 (<i>p</i> < 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.</p><p><strong>Conclusion: </strong>The nomogram model constructed based on THRSP and ACACA proteins may provide a reference value for the prognostic evaluation of breast cancer patients.</p>","PeriodicalId":56015,"journal":{"name":"Pharmacogenomics & Personalized Medicine","volume":"18 ","pages":"179-188"},"PeriodicalIF":1.8000,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12326444/pdf/","citationCount":"0","resultStr":"{\"title\":\"Construction of a Nomogram Model for Predicting Prognosis in Breast Cancer Patients Based on the Expression of THRSP and ACACA Proteins Tissues.\",\"authors\":\"Benkai Wei, Fan Li, Huanhuan Yan, Jun Shen\",\"doi\":\"10.2147/PGPM.S516843\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>The expression of THRSP and ACACA proteins in tumor tissues of breast cancer patients was higher than that in non-tumor tissues (<i>p</i> < 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 (<i>p</i> < 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 (<i>p</i> < 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.</p><p><strong>Conclusion: </strong>The nomogram model constructed based on THRSP and ACACA proteins may provide a reference value for the prognostic evaluation of breast cancer patients.</p>\",\"PeriodicalId\":56015,\"journal\":{\"name\":\"Pharmacogenomics & Personalized Medicine\",\"volume\":\"18 \",\"pages\":\"179-188\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2025-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12326444/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pharmacogenomics & Personalized Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/PGPM.S516843\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmacogenomics & Personalized Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/PGPM.S516843","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
Construction of a Nomogram Model for Predicting Prognosis in Breast Cancer Patients Based on the Expression of THRSP and ACACA Proteins Tissues.
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 (p < 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 (p < 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 (p < 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.
期刊介绍：
Pharmacogenomics and Personalized Medicine is an international, peer-reviewed, open-access journal characterizing the influence of genotype on pharmacology leading to the development of personalized treatment programs and individualized drug selection for improved safety, efficacy and sustainability.
In particular, emphasis will be given to:
Genomic and proteomic profiling
Genetics and drug metabolism
Targeted drug identification and discovery
Optimizing drug selection & dosage based on patient''s genetic profile
Drug related morbidity & mortality intervention
Advanced disease screening and targeted therapeutic intervention
Genetic based vaccine development
Patient satisfaction and preference
Health economic evaluations
Practical and organizational issues in the development and implementation of personalized medicine programs.
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