6 results on '"Cui Mingke"'
Search Results
2. An Immune Model to Predict Prognosis of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Based on Support Vector Machine
- Author
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Yusong Wang, Haoran Dong, Zhiyuan Pang, Mengshen Wang, Shuang Li, Cui Mingke, Qiang Zhang, Ying-Ying Xu, Yanfu Zheng, Litong Yao, Xiangyu Sun, and Mozhi Wang
- Subjects
Oncology ,Cancer Research ,medicine.medical_specialty ,T cell ,medicine.medical_treatment ,Breast cancer ,Immune system ,breast cancer ,Internal medicine ,Genetic model ,medicine ,Cytotoxic T cell ,support vector machine ,RC254-282 ,Original Research ,Tumor microenvironment ,Chemotherapy ,business.industry ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,prediction ,medicine.disease ,immunity ,medicine.anatomical_structure ,business ,CD8 ,neoadjuvant chemotherapy - Abstract
Tumor microenvironment has been increasingly proved to be crucial during the development of breast cancer. The theory about the conversion of cold and hot tumor attracted the attention to the influences of traditional therapeutic strategies on immune system. Various genetic models have been constructed, although the relation between immune system and local microenvironment still remains unclear. In this study, we tested and collected the immune index of 262 breast cancer patients before and after neoadjuvant chemotherapy. Five indexes were selected and analyzed to form the prediction model, including the ratio values between after and before neoadjuvant chemotherapy of CD4+/CD8+ T cell ratio; lymphosum of T, B, and natural killer (NK) cells; CD3+CD8+ cytotoxic T cell percent; CD16+CD56+ NK cell absolute value; and CD3+CD4+ helper T cell percent. Interestingly, these characters are both the ratio value of immune status after neoadjuvant chemotherapy to the baseline. Then the prediction model was constructed by support vector machine (accuracy rate = 75.71%, area under curve = 0.793). Beyond the prognostic effect and prediction significance, the study instead emphasized the importance of immune status in traditional systemic therapies. The result provided new evidence that the dynamic change of immune status during neoadjuvant chemotherapy should be paid more attention.
- Published
- 2021
3. An Immune Model to Predict Prognosis of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Based on Support Vector Machine.
- Author
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Wang, Mozhi, Pang, Zhiyuan, Wang, Yusong, Cui, Mingke, Yao, Litong, Li, Shuang, Wang, Mengshen, Zheng, Yanfu, Sun, Xiangyu, Dong, Haoran, Zhang, Qiang, and Xu, Yingying
- Subjects
NEOADJUVANT chemotherapy ,SUPPORT vector machines ,BREAST cancer prognosis ,T helper cells ,CYTOTOXIC T cells - Abstract
Tumor microenvironment has been increasingly proved to be crucial during the development of breast cancer. The theory about the conversion of cold and hot tumor attracted the attention to the influences of traditional therapeutic strategies on immune system. Various genetic models have been constructed, although the relation between immune system and local microenvironment still remains unclear. In this study, we tested and collected the immune index of 262 breast cancer patients before and after neoadjuvant chemotherapy. Five indexes were selected and analyzed to form the prediction model, including the ratio values between after and before neoadjuvant chemotherapy of CD4
+ /CD8+ T cell ratio; lymphosum of T, B, and natural killer (NK) cells; CD3+ CD8+ cytotoxic T cell percent; CD16+ CD56+ NK cell absolute value; and CD3+ CD4+ helper T cell percent. Interestingly, these characters are both the ratio value of immune status after neoadjuvant chemotherapy to the baseline. Then the prediction model was constructed by support vector machine (accuracy rate = 75.71%, area under curve = 0.793). Beyond the prognostic effect and prediction significance, the study instead emphasized the importance of immune status in traditional systemic therapies. The result provided new evidence that the dynamic change of immune status during neoadjuvant chemotherapy should be paid more attention. [ABSTRACT FROM AUTHOR]- Published
- 2021
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4. A machine learning model to predict efficacy of neoadjuvant therapy in breast cancer based on dynamic changes in systemic immunity.
- Author
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Wang Y, Wang M, Yu K, Xu S, Qiu P, Lyu Z, Cui M, Zhang Q, and Xu Y
- Subjects
- Humans, CD8-Positive T-Lymphocytes, Machine Learning, Killer Cells, Natural, Neoadjuvant Therapy methods, Triple Negative Breast Neoplasms drug therapy, Triple Negative Breast Neoplasms pathology
- Abstract
Objective: Neoadjuvant therapy (NAT) has been widely implemented as an essential treatment to improve therapeutic efficacy in patients with locally-advanced cancer to reduce tumor burden and prolong survival, particularly for human epidermal growth receptor 2-positive and triple-negative breast cancer. The role of peripheral immune components in predicting therapeutic responses has received limited attention. Herein we determined the relationship between dynamic changes in peripheral immune indices and therapeutic responses during NAT administration., Methods: Peripheral immune index data were collected from 134 patients before and after NAT. Logistic regression and machine learning algorithms were applied to the feature selection and model construction processes, respectively., Results: Peripheral immune status with a greater number of CD3
+ T cells before and after NAT, and a greater number of CD8+ T cells, fewer CD4+ T cells, and fewer NK cells after NAT was significantly related to a pathological complete response ( P < 0.05). The post-NAT NK cell-to-pre-NAT NK cell ratio was negatively correlated with the response to NAT (HR = 0.13, P = 0.008). Based on the results of logistic regression, 14 reliable features ( P < 0.05) were selected to construct the machine learning model. The random forest model exhibited the best power to predict efficacy of NAT among 10 machine learning model approaches (AUC = 0.733)., Conclusions: Statistically significant relationships between several specific immune indices and the efficacy of NAT were revealed. A random forest model based on dynamic changes in peripheral immune indices showed robust performance in predicting NAT efficacy., Competing Interests: No potential conflicts of interest are disclosed., (Copyright © 2023 Cancer Biology & Medicine.)- Published
- 2023
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- View/download PDF
5. Research Progress on Influencing Factors and Intervention Measures of Post-traumatic Growth in Breast Cancer Patients.
- Author
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Fu X, Sun J, Wang X, Cui M, and Zhang Q
- Subjects
- Adaptation, Psychological, Female, Health Status, Humans, Quality of Life, Breast Neoplasms therapy, Posttraumatic Growth, Psychological
- Abstract
Breast cancer is the highest incidence of female malignant tumor in the world, and it shows an increasing trend year by year. It poses a great threat to women's life and health and has become a public health issue of global concern. Paying attention to the psychological response of cancer patients is of definite value in helping patients cope with the disease, return to society, reshape an active and healthy life, and improve their quality of life with cancer. In recent years, researchers have increasingly focused on the positive changes experienced by cancer patients from the perspective of positive psychology, namely post-traumatic growth. It is of great significance to explore individual and social resources to help patients grow and improve their survival ability and quality of life by paying attention to the potential resources and positive forces in the process of patients' fighting against diseases. This paper summarizes the influencing factors and intervention measures of post-traumatic growth of breast cancer patients, providing ideas and reference for clinical medical staff to carry out relevant intervention., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Fu, Sun, Wang, Cui and Zhang.)
- Published
- 2022
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- View/download PDF
6. The choice of a neoadjuvant chemotherapy cycle for breast cancer has significance in clinical practice: results from a population-based, real world study.
- Author
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Yao L, Pang Z, Wang M, Wang M, Sun X, Cui M, Zheng Y, Li X, Dong H, Zhang Q, and Xu Y
- Abstract
Objective: Neoadjuvant chemotherapy (NAC) is currently used in both early stage and locally advanced breast cancers. The survival benefits of standard vs. non-standard NAC cycles are still unclear. This study aimed to investigate the relationship between NAC cycles and survival based on real world data., Methods: We identified patients diagnosed with invasive primary breast cancers who underwent NAC followed by surgery. Patients who received at least 4 NAC cycles were defined as having received standard cycles, while patients who received less than 4 NAC cycles were defined as having received non-standard cycles. Kaplan-Meier curves and Cox proportional hazard models were used to estimate the disease-free survival (DFS) and overall survival (OS)., Results: Of the 1,024 included patients, 700 patients received standard NAC cycles and 324 patients received non-standard NAC cycles. The DFS estimates were 87.1% and 81.0% ( P = 0.007) and the OS estimates were 90.0% and 82.6% ( P = 0.001) in the standard and non-standard groups, respectively. Using multivariate analyses, patients treated with standard NAC cycles showed significant survival benefits in both DFS [hazard ratio (HR): 0.62, 95% confidence interval (CI): 0.44-0.88] and OS (HR: 0.54, 95% CI: 0.37-0.79). Using stratified analyses, standard NAC cycles were associated with improved DFS (HR: 0.59, 95% CI: 0.36-0.96) and OS (HR: 0.49, 95% CI: 0.28-0.86) in the HER2 positive group. Similar DFS (HR: 0.50, 95% CI: 0.25-0.98) and OS (HR: 0.45, 95% CI: 0.22-0.91) benefits were shown for the triple negative group., Conclusions: Standard NAC cycles were associated with a significant survival benefit, especially in patients with HER2 positive or triple negative breast cancer., Competing Interests: No potential conflicts of interest are disclosed., (Copyright © 2021 Cancer Biology & Medicine.)
- Published
- 2021
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