Abstract
Background The aim of this study was to confirm the role of Brachyury in breast cells and to establish and verify whether four types of machine learning models can use Brachyury expression to predict the survival of patients.Methods We conducted a retrospective review of the medical records to obtain patient information, and made the patient's paraffin tissue into tissue chips for staining analysis. We selected a total of 303 patients for research and implemented four machine learning prediction algorithms, including multivariate logistic regression model, decision tree, artificial neural network and random forest, and compared the results of these models with each other. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results.Results The chi-square test results of relevant data suggested that the expression of Brachyury protein in cancer tissues was significantly higher than that in paracancerous tissues (p=0.0335); breast cancer patients with high Brachyury expression had a worse overall survival (OS) compared with patients with low Brachyury expression. We also found that Brachyury expression was associated with ER expression (p=0.0489). Subsequently, we used four machine learning models to verify the relationship between Brachyury expression and the survival of breast cancer patients. The results showed that the decision tree model had the best performance (AUC=0.781).Conclusions Brachyury is highly expressed in breast cancer and indicates that the patient had a poor chance of survival. Compared with conventional statistical methods, decision tree model shows superior performance in predicting the survival status of breast cancer patients. This indicates that machine learning can thus be applied in a wide range of clinical studies.
Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model
