Deep learning model with L1 penalty for predicting breast cancer metastasis using gene expression data

Abstract

Abstract Breast cancer has the highest incidence and death rate among women; moreover, its metastasis to other organs increases the mortality rate. Since several studies have reported gene expression and cancer prognosis to be related, the study of breast cancer metastasis using gene expression is crucial. To this end, a novel deep neural network architecture, deep learning-based cancer metastasis estimator (DeepCME), is proposed in this paper for predicting breast cancer metastasis. However, the problem of overfitting occurs frequently while training deep learning models using gene expression data because they contain a large number of genes and the sample size is rather small. To address overfitting, several regularization methods are implemented, such as L1 penalty, batch normalization, and dropout. To demonstrate the superior performance of our model, area under curve (AUC) scores are evaluated and then compared with five baseline models: logistic regression, support vector classifier (SVC), random forest, decision tree, and k-nearest neighbor. Considering results, DeepCME demonstrates the highest average AUC scores in most cross-validation cases, and the average AUC score of DeepCME is 0.754, which is approximately 12.9% higher than SVC, the second-best model. In addition, the 30 most significant genes related to breast cancer metastasis are identified based on DeepCME results and some are discussed in further detail considering the reports from some previous medical studies. Considering the high expense involved in measuring the expression of a single gene, the ability to develop the cost-effective and time-efficient tests using only a few key genes is valuable. Based on this study, we expect DeepCME to be utilized clinically for predicting breast cancer metastasis and be applied to other types of cancer as well after further research.