Machine Learning-Based Prognostic Gene Signature for Early Triple Negative Breast Cancer.

Abstract

Purpose: This study aimed to develop a machine learning-based approach to identify prognostic gene signatures for early-stage Triple Negative Breast Cancer (TNBC) using next-generation sequencing data from Asian populations.

Materials and methods: We utilized next-generation sequencing data to analyze gene expression profiles and identify potential biomarkers. Our methodology involved integrating various machine learning techniques, including feature selection and model optimization. We employed logistic regression, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curves to validate the identified gene signatures.

Results: We identified a gene signature significantly associated with relapse in TNBC patients. The predictive model demonstrated robustness and accuracy, with an area under the ROC curve (AUROC) of 0.9087, sensitivity of 0.8750, and specificity of 0.9231. The Kaplan-Meier survival analysis revealed a strong association between the gene signature and patient relapse, further validated by logistic regression analysis.

Conclusion: This study presents a novel machine learning-based prognostic tool for TNBC, offering significant implications for early detection and personalized treatment. The identified gene signature provides a promising approach for improving the management of TNBC, contributing to the advancement of precision oncology.