BCSNP-ML: A Novel Breast Cancer Prediction Model Base on LightGBM and Estrogen Metabolic Enzyme Genes

Estrogen-related metabolic enzyme gene polymorphisms have been demonstrated to be linked to breast cancer, and in this paper, a novel noninvasive breast cancer prediction model was developed utilizing machine learning algorithms incorporating estrogen metabolic enzyme gene single nucleotide polymorphisms (SNPs). To precisely forecast the susceptibility to breast cancer,, the coded data of 14 SNPs from enrolled breast patients and normal women were randomly shuffled, with 80% of the data designated as training data, the remaining 20% reserved as the test group to be validated. Single factor analysis was performed to screen independent risk factors, and subsequent application of Breast Cancer with Single Nucleotide Polymorphisms - Machine Learning model (BCSNP-ML) prediction model was completed using Light Gradient Boosting Machine (LightGBM) algorithm. A total of 14 SNPs variables from 280 subjects were utilized in this study. Single factor analysis indicated that a meaningful association between SULT1A1 rs1042028, CYP1A1 rs1048943, CYP1B1 rs1056827, CYP1A1 rs1056836 and the incidence of breast cancer, with 14 variables demonstrates a notable area under the receiver operating characteristic curve (AUROC) of 0.809. The AUROC of the BCSNP-ML model constructed by four variables was 0.831. Additionally, BCSNP-ML is visualized and interpretated in the paper using SHapley Additive exPlanations analysis to further validate that the model exhibits great potential as a robust tool for clinical forecasting of breast cancer.