COMBINE: A Comprehensive Multi-Omics Approach for Improving Breast Cancer Prognosis Classification in African American Women

Abstract

Abstract Breast cancer disproportionately affects African American women under the age of 50, leading to higher incidence rates, more aggressive cancer subtypes, and increased mortality compared to other racial and ethnic groups. To enhance the prediction of onset risk and enable timely intervention and treatment, it is crucial to investigate the genetic and molecular factors associated with these disparities. This study introduces COMBINE, an innovative ensemble learning model that combines three types of omics data to improve the accuracy of breast cancer prognosis classification and reduce the model's time complexity. A comparative analysis of the fusion effects for African American and White women reveals a significant improvement in the fusion effect for African American women. Additionally, gene enrichment analysis highlights the importance of considering race when selecting relevant biomarkers. To address the challenges of cancer prognosis classification, a combination of qualitative and quantitative methods, along with ensemble learning, is employed. This comprehensive approach facilitates the exploration of new concepts for the application of multi-omics data, potentially leading to more personalized and effective treatment strategies. The study highlights the potential of ensemble learning as a fusion technique for multi-omics data in cancer prognosis classification. It emphasizes the importance of refining our understanding of the genetic and molecular factors contributing to disparities in breast cancer incidence and outcomes. Ultimately, this research has the potential to improve healthcare outcomes for African American women and alleviate the burden of this formidable disease.